A UAV control simulation training method and electronic equipment

Abstract

The present application relates to a method and electronic equipment for simulating and training unmanned aerial vehicle control. The method comprises acquiring an image sequence set, wherein the image sequence set comprises a plurality of image sequence subsets, wherein the image sequence subsets comprise a plurality of frames of images, wherein the images comprise at least one preset target object, wherein the number of preset target objects contained in the plurality of frames of images of the same image sequence subset is equal, and the number of preset target objects contained in the images of different image sequence subsets is different; displaying the plurality of frames of images of the plurality of image sequence subsets in sequence according to a preset time interval and a preset order; acquiring the actual target object selected by a control player according to the image within each preset time interval; judging for each image whether the actual target object selected by the control player is consistent with the preset target object of the corresponding image, and obtaining the judgment result; and determining the training score of the control player according to the judgment result.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of drone control, and more specifically, to a drone control simulation training method and electronic equipment. Background Art

[0002] At present, the existing UAV control training equipment focuses on the training and improvement of the task performance of operators. The training enhancement method is often through repetitive training. This training method has low training efficiency, slow results, and is prone to bottlenecks in improvement. The bottleneck of the improvement of operators' related skills is actually directly related to the cognitive shortcomings of operators. For example, operators are often in a hurry in emergency response tasks because their brain alertness and reaction are weak. For example, operators always miss targets during reconnaissance, which is actually caused by cognitive shortcomings. Therefore, it is necessary to provide a UAV control simulation training method to solve the problems of low training efficiency, slow results, and easy bottlenecks in the existing UAV control training methods. Summary of the invention

[0003] One purpose of the embodiments of the present application is to provide a new technical solution for a drone control simulation training method and an electronic device.

[0004] According to a first aspect of the present application, a method for unmanned aerial vehicle control simulation training is provided, comprising obtaining an image sequence set, wherein the image sequence set comprises a plurality of image sequence subsets, wherein the image sequence subsets comprise a plurality of frames of images, wherein the images comprise at least one preset target object, wherein the number of the preset target objects comprised by the plurality of frames of images of the same image sequence subset is equal, and the number of the preset target objects comprised by the images of different image sequence subsets is different;

[0005] Displaying the multiple frames of the image of the plurality of image sequence subsets in sequence according to a preset time interval and a preset order; wherein the preset order includes displaying the plurality of image sequence subsets in sequence according to the preset target objects from least to most;

[0006] Obtaining the actual target object selected by the player according to the image within each preset time interval;

[0007] For each image, determining whether the actual target object selected by the controlling player is consistent with the preset target object of the corresponding image, and obtaining a determination result;

[0008] According to the judgment result, the training score of the manipulating player is determined.

[0009] Optionally, the image sequence set is a first image sequence set, the image sequence subset is a first image sequence subset, the image is a first image, the preset target object is a first preset target object, the preset time interval is a first preset time interval, the actual target object is a first actual target object, the judgment result is a first judgment result, and the training score is a selective attention training score;

[0010] The first preset target object is a first mark pre-set at a specified position of each frame of the first image, and the first mark has different positions on different first images of the same first image sequence subset;

[0011] The first actual target object is the actual position of the first mark selected by the manipulator for each frame of the first image.

[0012] Optionally, for each first image, determining whether the first actual target object selected by the manipulating player is consistent with the first preset target object corresponding to the first image includes:

[0013] For each first image, it is determined whether the actual position of the first marker selected by the manipulating player is the same as the set position of the first marker corresponding to the first image.

[0014] Optionally, the image sequence set is a second image sequence set, the image sequence subset is a second image sequence subset, the image is a second image, the preset target object is a second preset target object, the preset time interval is a second preset time interval, the actual target object is a second actual target object, the judgment result is a second judgment result, and the training score is an attention span training score;

[0015] The second preset target object is a second mark preset at a specified position of each frame of the second image, and the second mark has different positions on different second images of the same second image sequence subset;

[0016] The second actual target object is the actual number of the second identifiers selected by the manipulator for each frame of the second image.

[0017] Optionally, for each second image, determining whether the second actual target object selected by the manipulating player is consistent with the second preset target object corresponding to the second image includes:

[0018] For each second image, it is determined whether the actual number of the second identifiers selected by the controlling player is equal to the set number of the second identifiers corresponding to the second image.

[0019] Optionally, the image sequence set is a third image sequence set, the image sequence subset is a third image sequence subset, the image is a third image, the preset target object is a third preset target object, the preset time interval is a third preset time interval, the actual target object is a third actual target object, the judgment result is a third judgment result, and the training score is an attention allocation training score;

[0020] Each frame of the third image includes a main picture and multiple sub-pictures;

[0021] The third preset target object is a third mark preset at a designated position of a main picture of each frame of the third image, and a fourth mark randomly generated in a plurality of sub-pictures of each frame of the third image, wherein the positions of the third mark on different third images of the same third image sequence subset are different;

[0022] The third actual target object is the actual number of the third markers and the actual position of the fourth marker selected by the manipulator for each frame of the third image.

[0023] Optionally, for each third image, determining whether the third actual target object selected by the manipulating player is consistent with the third preset target object corresponding to the third image includes:

[0024] For each third image, determining whether the actual number of the third identifiers selected by the manipulating player is equal to the set number of the third identifiers corresponding to the third image;

[0025] Determining whether the actual position of the fourth marker selected by the controlling player is the same as the randomly generated position of the fourth marker corresponding to the third image;

[0026] When the actual number of the third identifiers selected by the controlling player is equal to the set number of the third identifiers corresponding to the third image, and the actual position of the fourth identifier selected by the controlling player is the same as the randomly generated position of the fourth identifier corresponding to the third image, it is determined that the third actual target object is consistent with the third preset target object; otherwise, it is determined that the third actual target object is inconsistent with the third preset target object.

[0027] Optionally, the image sequence set is a fourth image sequence set, the image sequence subset is a fourth image sequence subset, the image is a fourth image, the preset target object is a fourth preset target object, the preset time interval is a fourth preset time interval, the actual target object is a fourth actual target object, the judgment result is a fourth judgment result, and the training score is a brain alertness and reaction training score;

[0028] Each frame of the fourth image includes multiple pictures;

[0029] The fourth preset target object is a fifth identifier randomly generated in multiple frames of each frame of the fourth image;

[0030] The fourth actual target object is the actual position of the fifth mark selected by the manipulator for each frame of the fourth image;

[0031] Optionally, for each fourth image, determining whether the fourth actual target object selected by the manipulating player is consistent with the fourth preset target object corresponding to the fourth image includes:

[0032] For each fourth image, it is determined whether the actual position of the fifth marker selected by the controlling player is the same as the randomly generated position of the fifth marker corresponding to the fourth image.

[0033] According to the second aspect of the present application, an electronic device is also provided, including a processor and a memory; the memory stores computer instructions, and when the computer instructions are executed by the processor, the method described in the first aspect of the present application is implemented.

[0034] One beneficial effect of the embodiment of the present application is that the method of the embodiment of the present invention sequentially displays multiple frames of images of multiple image sequence subsets according to a preset time interval and a preset order, and the number of preset target objects contained in the multiple image sequence subsets increases in sequence according to the order of display time, that is, the training difficulty gradually increases, and the training score of the operator is determined by judging whether the actual target object selected by the operator in each preset time interval is consistent with the preset target object. According to the method of this embodiment, the operator's brain can be enhanced with high training efficiency and quick results, and the operator's cognitive shortcomings can be quickly supplemented to improve the operator's task performance.

[0035] Other features and advantages of the embodiments of the present application will become apparent from the following detailed description of exemplary embodiments of the present application with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present application and, together with the description, serve to explain the principles of the embodiments of the present application.

[0037] Figure 1 This is a schematic diagram of the hardware structure of the electronic device according to the first embodiment of the present application.

[0038] Figure 2 A flow chart of a drone control simulation training method provided in an embodiment of the present application Figure 1 ;

[0039] Figure 3A flow chart of a drone control simulation training method provided in an embodiment of the present application Figure 2 ;

[0040] Figure 4 A flow chart of a drone control simulation training method provided in an embodiment of the present application Figure 3 ;

[0041] Figure 5 A flow chart of a drone control simulation training method provided in an embodiment of the present application Figure 4 .

[0042] Figure 6 A flow chart of a drone control simulation training method provided in an embodiment of the present application Figure 5 ;

[0043] Figure 7 This is a schematic diagram of the hardware structure of an electronic device according to the second embodiment of the present application. DETAILED DESCRIPTION

[0044] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless otherwise specifically stated.

[0045] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0046] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0047] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0048] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0049] <Hardware Configuration>

[0050] Figure 1 is a block diagram of a hardware configuration of an electronic device 1000 that can be used to implement an embodiment of the present invention.

[0051] like Figure 1As shown, the electronic device 1000 may be a terminal device 1100 such as a PC, a laptop computer, etc. used by the player to control the player, which is not limited here.

[0052] In this example, refer to Figure 1 As shown, the terminal device 1100 may include a processor 1110, a memory 1120, an interface device 1130, a communication device 1140, a display device 1150, an input device 1160, a speaker 1170, a microphone 1180, and the like.

[0053] The processor 1110 may be a mobile version processor. The memory 1120 may include, for example, a ROM (read-only memory), a RAM (random access memory), a non-volatile memory such as a hard disk, etc. The interface device 1130 may include, for example, a USB interface, a headphone interface, etc. The communication device 1140 may, for example, be capable of wired or wireless communication. The communication device 1140 may include a short-range communication device, for example, any device for short-range wireless communication based on short-range wireless communication protocols such as Hilink protocol, WiFi (IEEE802.11 protocol), Mesh, Bluetooth, ZigBee, Thread, Z-Wave, NFC, UWB, LiFi, etc. The communication device 1140 may also include a remote communication device, for example, any device for WLAN, GPRS, 2G / 3G / 4G / 5G remote communication. The display device 1150 may, for example, be a liquid crystal display, a touch display, etc. The input device 1160 may include, for example, a touch screen, a keyboard, a mouse, etc. The user may input / output voice information through the speaker 1170 and the microphone 1180.

[0054] In this example, the memory 1120 of the terminal device 1100 is used to store instructions, and the instructions are used to control the processor 1110 to operate to at least perform the simulation training method according to any embodiment of the present invention. A technician can design instructions according to the scheme disclosed in the present invention. How the instructions control the processor 1110 to operate is well known in the art, so it will not be described in detail here.

[0055] Despite Figure 1 Multiple devices of the terminal device 1100 are shown in the figure, but the present invention may only involve some of the devices. For example, the terminal device 1100 only involves the processor 1110, the memory 1120, the display device 1150, the input device 1160 and the speaker 1170.

[0056] <Method Example>

[0057] Figure 2 is a flow chart of a method for training a drone control simulation according to an embodiment of the present invention. The method may be performed as follows: Figure 1The electronic device 1000 shown is implemented. In this embodiment, taking the analysis of a drone operator performing a training through an electronic device as an example, the drone control simulation training method of this embodiment is described. The method may include the following steps S210 to S250:

[0058] Step S210, obtaining an image sequence set.

[0059] The image sequence set includes multiple image sequence subsets, each image sequence subset includes multiple frames of images, and the images contain at least one preset target object. The number of preset target objects contained in the multiple frames of images of the same image sequence subset is equal, and the number of preset target objects contained in the images of different image sequence subsets is different.

[0060] In one example, a reconnaissance image taken by an existing drone may be processed, and a preset target object may be marked on the reconnaissance image to obtain an image.

[0061] In one example, an existing network image may be processed and a preset target object may be marked on the network image to obtain an image.

[0062] For example, on a network image, a mark is set at the location of any specified point, and the mark is the preset target object.

[0063] In one example, each image sequence subset is provided with multiple frames of images, and the multiple frames of images in the same image sequence subset contain an equal number of preset target objects, so as to facilitate repetitive training of the control player without changing the number of preset target objects, so as to improve the authenticity of the subsequent training scores of the control player.

[0064] In one example, images in different image sequence subsets contain different numbers of preset target objects. Images with different numbers of preset target objects represent different training difficulties, which facilitates enhanced training of different levels of difficulty for the control players, can quickly make up for the cognitive shortcomings of the control personnel, and improve the control personnel's task performance.

[0065] Step S220 , displaying multiple frames of images of multiple image sequence subsets in sequence according to a preset time interval and a preset order.

[0066] The preset order includes sequentially displaying a plurality of image sequence subsets in an order from least to most of the preset target objects.

[0067] In one example, the display device 1150 is controlled to sequentially display multiple frames of images of multiple image sequence subsets according to a preset time interval and a preset order until all images in the image sequence set are displayed.

[0068] In one example, multiple subsets of image sequences are displayed in order from the least to the most preset target objects, that is, the training difficulty is gradually increased to provide enhanced training for the manipulator.

[0069] In one example, the display device 1150 of the electronic device 1000 is controlled to display only one frame of image each time.

[0070] Step S230, obtaining the actual target object selected by the controlling player according to the image within each preset time interval.

[0071] In one example, when the display device 1150 displays images in an image sequence set, after the current frame image is displayed, the display device 1150 displays the next frame image at a preset time interval, and the controlling player selects the preset target object on the current frame image that he or she believes through the input device 1160 of the electronic device 1000 within the preset time interval to obtain the actual target object selected by the controlling player on the current frame image.

[0072] Step S240: for each image, determine whether the actual target object selected by the manipulating player is consistent with the preset target object of the corresponding image, and obtain the determination result.

[0073] In one example, after the controlling player selects the actual target object on the current frame image, the electronic device 1000 compares the actual target object on the current frame image selected by the controlling player with the preset target object on the current frame image, and determines whether the actual target object on the current frame image is consistent with the preset target object on the current frame image, thereby obtaining a judgment result of the current frame image.

[0074] In one example, the electronic device 1000 can obtain the judgment result of the current frame image after the current frame image is displayed and before the next frame image is displayed.

[0075] Step S250, determining the training score of the manipulating player according to the judgment result.

[0076] In one example, the training score of the manipulator is calculated based on the judgment results of all images in the image sequence set.

[0077] In this embodiment, before obtaining the image sequence set, in step S210, the process further includes: obtaining the training mode selected by the operator.

[0078] The training mode includes free mode and assessment mode.

[0079] In one example, when the operator selects the free mode, the electronic device 1000 only executes steps S210 to S240, and does not score the operator's training situation. In step S240, each time the electronic device 1000 obtains the judgment result of the current frame image, based on the judgment result, the display device 1150 directly displays the next frame image after reaching the preset time interval; based on the judgment result of no, the speaker 1170 is triggered to play the penalty sound, and the operator is subjected to sound penalty stimulation.

[0080] In one example, when the operator selects the training mode, the electronic device 1000 executes steps S210 to S250 to score the operator's training situation. Each time the electronic device 1000 obtains the judgment result of the current frame image, based on the judgment result, the display device 1150 directly displays the next frame image after reaching the preset time interval; based on the judgment result, the speaker 1170 is not triggered, and the display device 1150 directly displays the next frame image after reaching the preset time interval, so as to avoid the penalty sound played by the speaker 1170 from affecting the operator.

[0081] In this embodiment, before obtaining the training mode selected by the control player, the process also includes obtaining the training subject selected by the control player.

[0082] The training subjects include selective attention training, attention span training, attention allocation training, and brain alertness and responsiveness training.

[0083] The following describes in detail the training methods of the selective attention training subject, the attention span training subject, the attention allocation training subject, and the brain alertness and reaction training subject of the present application with reference to the accompanying drawings.

[0084] Figure 3 is a flow chart of a method for simulating and training a selective attention for drone control according to an embodiment of the present invention. The method may be performed as follows: Figure 1 The electronic device 1000 shown is implemented. The drone control selective attention simulation training method may include the following steps S310-S350:

[0085] Step S310: acquiring a first image sequence set.

[0086] The first image sequence set includes multiple first image sequence subsets, each first image sequence subset includes multiple frames of first images, the first images contain at least one first preset target object, the number of first preset target objects contained in multiple frames of first images of the same first image sequence subset is equal, and the number of first preset target objects contained in first images of different first image sequence subsets is different.

[0087] In one example, the first preset target object is a first mark preset at a specified position of each frame of the first image, and the positions of the first mark on different first images of the same first image sequence subset are different.

[0088] In one example, selective attention training is used to train operators to quickly locate targets and ignore distracting targets and background during unmanned system reconnaissance.

[0089] In one example, the first image includes a first mark and a plurality of first interference marks other than the first mark, and the number of the first interference marks is much greater than the number of the first marks. The first interference marks are used to interfere with the manipulating player.

[0090] In one example, when the operator selects the selective attention training subject, the electronic device 1000 obtains a first image sequence set, and multiple first image sequence subsets of the first image sequence set are used to enhance the selective attention training of the operator, which can quickly make up for the operator's selective attention cognitive shortcomings and improve the operator's task performance.

[0091] For example, the number of first image sequence subsets may be 10, the number of first images included in each first image sequence subset may be 2, the number of first identifiers included in the first images of the 10 first image sequence subsets may be an arithmetic progression, and the common of the arithmetic progression may be 1, for example, the number of first identifiers included in the first image of the 1st first image sequence subset may be 3, the number of first identifiers included in the first image of the 2nd first image sequence subset may be 4, ..., the number of first identifiers included in the first image of the 10th first image sequence subset may be 12.

[0092] Step S320: displaying a plurality of frames of first images of a plurality of first image sequence subsets in sequence according to a first preset time interval and a preset order.

[0093] The preset order includes sequentially displaying a plurality of first image sequence subsets in an order from least to most of the first preset target objects.

[0094] In one example, the display device 1150 of the electronic device 1000 is controlled to sequentially display multiple frames of first images of multiple first image sequence subsets according to a first preset time interval and a preset order until the display of all first images in the first image sequence set is completed.

[0095] In an example, the first preset time interval may be set at the factory or may be set by the user. The first preset time interval may be, for example, 8 seconds.

[0096] In one example, multiple first image sequence subsets are displayed in order from least to most according to the first identifiers, that is, the training difficulty is gradually increased, and enhanced training is performed on the operator, which can quickly make up for the operator's selective attention cognitive shortcomings and improve the operator's task performance.

[0097] In one example, the display device 1150 of the electronic device 1000 is controlled to display only one frame of the first image each time.

[0098] Step S330: obtaining the first actual target object selected by the controlling player according to the first image within each first preset time interval.

[0099] In one example, the first actual target object is the actual position of the first marker selected by the player through the mouse of the input device 1160 for each frame of the first image.

[0100] In one example, when the display device 1150 displays the first image in the first image sequence set, after the display of the first image of the current frame is completed, the display device 1150 displays the next first image frame at an interval of a first preset time interval, and the operator selects the position of the first marker on the first image of the current frame that he believes to be within the first preset time interval through the mouse of the input device 1160, and obtains the actual position of the first marker selected by the operator on the first image of the current frame.

[0101] Step S340: for each first image, determine whether the first actual target object selected by the manipulating player is consistent with the first preset target object corresponding to the first image, and obtain a first determination result.

[0102] In an example, in step S340, for each first image, determining whether the first actual target object selected by the manipulating player is consistent with the first preset target object corresponding to the first image may further include the following steps: for each first image, determining whether the actual position of the first identifier selected by the manipulating player is the same as the set position of the first identifier corresponding to the first image.

[0103] In one example, after the controlling player selects the actual position of the first marker on the first image of the current frame, the electronic device 1000 compares the actual position of the first marker on the first image of the current frame selected by the controlling player with the set position of the first marker on the first image of the current frame, and determines whether the actual position of the first marker on the first image of the current frame is the same as the set position of the first marker on the first image of the current frame, thereby obtaining a first judgment result of the first image of the current frame.

[0104] In one example, based on the first judgment result being yes, the first image is recorded as a valid first image; based on the first judgment result being no, the first image is recorded as an invalid first image.

[0105] In one example, the electronic device 1000 can obtain the first judgment result of the current frame first image after the current frame first image is displayed and before the next frame first image is displayed.

[0106] Step S350: Determine the selective attention training score of the control player according to the first judgment result.

[0107] In one example, the selective attention training score of the manipulator can be calculated based on the judgment results of all the first images in the first image sequence set.

[0108] In one example, the selective attention training score S 1 It can be obtained by the following formula (1):

[0109]

[0110] In formula (1), A represents the number of first image sequence subsets, B represents the number of first images in a first image sequence subset, and C represents the number of valid first images.

[0111] Figure 4 FIG. 1 is a flow chart of a method for simulating and training attention span of drone control according to an embodiment of the present invention. The method may be performed as follows: Figure 1 The electronic device 1000 shown is implemented. The drone control attention span simulation training method may include the following steps S410 to S450:

[0112] Step S410: acquiring a second image sequence set.

[0113] The second image sequence set includes multiple second image sequence subsets, each second image sequence subset includes multiple frames of second images, the second images contain at least one second preset target object, the number of second preset target objects contained in the multiple frames of second images of the same second image sequence subset is equal, and the number of second preset target objects contained in the second images of different second image sequence subsets is different.

[0114] In one example, the second preset target object is a second marker preset at a specified position of each second image frame, and the second marker has different positions on different second images of the same second image sequence subset.

[0115] In one example, attention span training is used to train operators to focus maximally on multiple targets during reconnaissance missions.

[0116] In one example, the second image includes the second marker and a plurality of second interference markers other than the second marker, and the number of the second interference markers is much greater than the number of the second markers.

[0117] In one example, when the operator selects the attention span training subject, the electronic device 1000 obtains a second image sequence set, and multiple second image sequence subsets of the second image sequence set are used to perform enhanced training on the operator's attention span, which can quickly make up for the operator's attention span cognitive shortcomings and improve the operator's task performance.

[0118] For example, the number of second image sequence subsets may be 9, the number of second images included in each second image sequence subset may be 2, the number of second identifiers included in the second images of the 9 second image sequence subsets may be an arithmetic progression, and the common difference of the arithmetic progression may be 1, for example, the number of second identifiers included in the second images of the first second image sequence subset may be 1, the number of second identifiers included in the second images of the second image sequence subset may be 2, ..., the number of second identifiers included in the second images of the ninth second image sequence subset may be 9, for example.

[0119] Step S420: displaying a plurality of frames of second images of a plurality of second image sequence subsets in sequence according to a second preset time interval and a preset order.

[0120] The preset order includes sequentially displaying a plurality of second image sequence subsets in an order from least to most of the second preset target objects.

[0121] In one example, the display device 1150 of the electronic device 1000 is controlled to sequentially display multiple frames of second images of multiple second image sequence subsets according to a second preset time interval and a preset order until all second images in the second image sequence set are displayed.

[0122] In an example, the second preset time interval may be set at the factory or may be set by the user. The second preset time interval may be, for example, 3 seconds.

[0123] In one example, multiple second image sequence subsets are displayed in order from the least to the most according to the second identifiers, that is, the training difficulty is gradually increased, and enhanced training is performed on the operator, which can quickly make up for the operator's attention span cognitive shortcomings and improve the operator's task performance.

[0124] In one example, the display device 1150 of the electronic device 1000 is controlled to display only one frame of the second image each time.

[0125] Step S430: obtaining a second actual target object selected by the controlling player according to the second image within each second preset time interval.

[0126] In one example, the second actual target object is the actual number of second markers selected by the player through the mouse of the input device 1160 for each frame of the second image.

[0127] In one example, when the display device 1150 displays the second image in the second image sequence set, after the display of the second image of the current frame is completed, the display device 1150 displays the next second image frame at a second preset time interval, and the controlling player selects the number of second identifiers that he or she believes to be on the second image of the current frame within the second preset time interval through the keyboard of the input device 1160 to obtain the actual number of second identifiers selected by the controlling player on the second image of the current frame.

[0128] Step S440: for each second image, determine whether the second actual target object selected by the manipulating player is consistent with the second preset target object corresponding to the second image, and obtain a second determination result.

[0129] In one example, in step S440, for each second image, determining whether the second actual target object selected by the manipulating player is consistent with the second preset target object corresponding to the second image may further include the following steps: for each second image, determining whether the actual number of second identifiers selected by the manipulating player is equal to the set number of second identifiers corresponding to the second image.

[0130] In one example, after the controlling player selects the actual number of second identifiers on the second image of the current frame, the electronic device 1000 compares the actual number of second identifiers on the second image of the current frame selected by the controlling player with the set number of second identifiers on the second image of the current frame, and determines whether the actual number of second identifiers on the second image of the current frame is equal to the set number of second identifiers on the second image of the current frame, thereby obtaining a second judgment result of the second image of the current frame.

[0131] In one example, based on the second judgment result being yes, the second image is recorded as a valid second image; based on the second judgment result being no, the second image is recorded as an invalid second image.

[0132] In one example, when the ratio of the number of valid second images in a second image sequence subset to the total number of second images is not less than a set value, the second image sequence subset is recorded as a valid second image sequence subset; when the ratio of the number of valid second images in a second image sequence subset to the total number of second images is less than a set value, the second image sequence subset is recorded as an invalid second image sequence subset.

[0133] In one example, the set value may be set at the factory or may be set by the user. The set value may be 50%, for example.

[0134] In one example, the electronic device 1000 can obtain the second determination result of the second image of the current frame after the second image of the current frame is displayed and before the second image of the next frame is displayed.

[0135] Step S450: Determine the attention span training score of the operator according to the second judgment result.

[0136] In one example, the attention span training score of the manipulator can be calculated based on the judgment results of all the second images in the second image sequence set.

[0137] In one example, the attention span training score S 2 It can be obtained by the following formula (2):

[0138] S 2 =D×10 formula (2);

[0139] In formula (2), D is the set number of the maximum second identifiers contained in the second image of the valid second image sequence subset.

[0140] For example, when the first second image sequence subset, the third second image sequence subset and the eighth second image sequence subset are all valid second image sequence subsets, and the other second image sequence subsets are all invalid second image sequence subsets, D is 8.

[0141] Figure 5 is a flow chart of a method for simulating and training attention allocation for controlling a drone according to an embodiment of the present invention. The method may be performed as follows: Figure 1 The electronic device 1000 shown is implemented. The drone control attention allocation simulation training method may include the following steps S510 to S550:

[0142] Step S510: acquiring a third image sequence set.

[0143] The third image sequence set includes multiple third image sequence subsets, each third image sequence subset includes multiple frames of third images, the third images contain at least one third preset target object, the number of third preset target objects contained in the multiple frames of third images of the same third image sequence subset is equal, and the number of third preset target objects contained in the third images of different third image sequence subsets is different.

[0144] In one example, attention allocation training is used to train operators to quickly switch attention and allocate resources between reconnaissance missions and emergency missions.

[0145] In one example, the third image includes a third preset target object and a plurality of third interfering target objects other than the third preset target object, and the number of the third interfering target objects is much greater than the number of the third preset target objects.

[0146] In one example, when the operator selects the attention allocation training subject, the electronic device 1000 obtains a third image sequence set, and multiple third image sequence subsets of the third image sequence set are used to perform enhanced training on the operator's attention allocation, which can quickly make up for the operator's attention allocation cognitive shortcomings and improve the operator's task performance.

[0147] In one example, the third image includes a main picture and a plurality of sub-pictures;

[0148] The third preset target object is a third mark preset at a designated position of the main screen of the third image, and a fourth mark randomly generated in multiple sub-screens of the third image, and the positions of the third mark on different third images of the same third image sequence subset are different;

[0149] The third actual target object is the actual number of third markers and the actual position of the fourth marker selected by the manipulator for each frame of the third image.

[0150] For example, the number of third image sequence subsets may be 9, the number of third images included in each third image sequence subset may be 2, the number of third identifiers included in the third images of the 9 third image sequence subsets is an arithmetic progression, and the common difference of the arithmetic progression may be 1, the number of third identifiers included in the third images of the first third image sequence subset may be 1, the number of third identifiers included in the third images of the second third image sequence subset may be 2, ..., the number of third identifiers included in the third images of the ninth third image sequence subset may be 9;

[0151] The number of fourth identifiers included in the third images of the nine third image sequence subsets is equal. For example, the number of fourth identifiers included in the third images of the third image sequence subset may be one.

[0152] Step S520: displaying multiple frames of third images of multiple third image sequence subsets in sequence according to a third preset time interval and a preset order.

[0153] The preset order includes sequentially displaying a plurality of third image sequence subsets in an order from least to most of the third preset target objects.

[0154] In one example, the display device 1150 of the electronic device 1000 is controlled to sequentially display multiple frames of third images of multiple third image sequence subsets according to a third preset time interval and a preset order until the display of all third images in the third image sequence set is completed.

[0155] In an example, the third preset time interval may be set at the factory or may be set by the user. The third preset time interval may be, for example, 3 seconds.

[0156] In one example, multiple third image sequence subsets are displayed in order from the least to the most according to the third preset target objects, that is, the training difficulty is gradually increased, and enhanced training is performed on the operator, which can quickly make up for the operator's attention allocation cognitive shortcomings and improve the operator's task performance.

[0157] In one example, the display device 1150 of the electronic device 1000 is controlled to display only one frame of the third image each time, and each frame of the third image includes a main picture and multiple sub-pictures.

[0158] Step S530: obtaining a third actual target object selected by the controlling player according to the third image within each third preset time interval.

[0159] In one example, when the display device 1150 displays the third image in the third image sequence set, after the display of the third image of the current frame is completed, the display device 1150 displays the next third image frame at a third preset time interval, and the controlling player selects the actual number of the third identifier on the third image of the current frame that he believes to be within the third preset time interval through the keyboard of the input device 1160, and selects the actual number of the sub-screen where the fourth identifier on the third image of the current frame is located through the keyboard of the input device 1160, to obtain the third actual target object selected by the controlling player on the third image of the current frame.

[0160] In one example, the sub-picture numbers are marked on the multiple sub-pictures of each frame of the third image.

[0161] Step S540: for each third image, determine whether the third actual target object selected by the manipulating player is consistent with the third preset target object corresponding to the third image, and obtain a third determination result.

[0162] In one example, for each third image, determining in step S540 whether the third actual target object selected by the manipulating player is consistent with the third preset target object corresponding to the third image may further include the following steps S541 to S543:

[0163] Step S541: for each third image, determining whether the actual number of third identifiers selected by the manipulating player is equal to the set number of third identifiers corresponding to the third image;

[0164] Step S542, determining whether the actual position of the fourth marker selected by the controlling player is the same as the randomly generated position of the fourth marker corresponding to the third image;

[0165] In one example, by judging whether the actual number of the fourth identifier selected by the controlling player is the same as the set number of the fourth identifier corresponding to the third image, it is judged whether the actual position of the fourth identifier selected by the controlling player is the same as the randomly generated position of the fourth identifier corresponding to the third image.

[0166] In one example, the set number of the fourth identifier refers to the number of the sub-screen where the randomly generated position of the fourth identifier is located.

[0167] Step S543: when the actual number of third identifiers selected by the controlling player is equal to the set number of third identifiers corresponding to the third image, and the actual position of the fourth identifier selected by the controlling player is the same as the randomly generated position of the fourth identifier corresponding to the third image, it is determined that the third actual target object is consistent with the third preset target object; otherwise, it is determined that the third actual target object is inconsistent with the third preset target object.

[0168] In one example, when the third actual target object is consistent with the third preset target object, the third image is recorded as a valid third image; when the third actual target object is inconsistent with the third preset target object, the third image is recorded as an invalid third image.

[0169] In one example, the electronic device 1000 can obtain the third determination result of the third image of the current frame after the third image of the current frame is displayed and before the third image of the next frame is displayed.

[0170] Step S550: Determine the attention allocation training score of the manipulator according to the third judgment result.

[0171] In one example, the attention allocation training score of the manipulator is calculated based on the judgment results of all the third images in the third image sequence set.

[0172] In one example, the attention allocation training score S 3 It can be obtained by the following formula (3):

[0173]

[0174] In formula (3), E represents the number of third image sequence subsets, F represents the number of third images in a third image sequence subset, and G represents the number of valid third images.

[0175] In this embodiment, when the number of fourth identifications remains unchanged and multiple third image sequence subsets are displayed, the number of fourth identifications contained in the third images of the multiple third image sequence subsets can be increased, and the third image sequence subsets can be displayed again to enhance the attention allocation training of the control player.

[0176] For example, when the number of fourth identifiers is 1 and all 9 third image sequence subsets are displayed, the number of fourth identifiers contained in the third images of the 9 third image sequence subsets can be increased to 2, and the 9 third image sequence subsets can be displayed again to enhance the attention allocation training of the control player.

[0177] Figure 6 FIG. 1 is a flow chart of a method for simulating and training the brain alertness and reaction force of drone control according to an embodiment of the present invention. The method may be performed as follows: Figure 1 The electronic device 1000 shown is implemented. The drone control brain alertness and reaction force simulation training method may include the following steps S610-S650:

[0178] Step S610: acquiring a fourth image sequence set.

[0179] The fourth image sequence set includes multiple fourth image sequence subsets, each fourth image sequence subset includes multiple frames of fourth images, the fourth images contain at least one fourth preset target object, the number of fourth preset target objects contained in the multiple frames of fourth images of the same fourth image sequence subset is equal, and the number of fourth preset target objects contained in the fourth images of different fourth image sequence subsets is different.

[0180] In one example, brain alertness and reaction training is used to train operators to perceive emergency situations and respond correctly when facing unmanned system environmental interference, anti-landing, and fault emergency handling.

[0181] In one example, the fourth image includes a fourth preset target object and a plurality of fourth interfering target objects other than the fourth preset target object, and the number of the fourth interfering target objects is much greater than the number of the fourth preset target objects.

[0182] In one example, when the operator selects the brain alertness and reaction training subject, the electronic device 1000 obtains a fourth image sequence set, and multiple fourth image sequence subsets of the fourth image sequence set are used to enhance the brain alertness and reaction of the operator, which can quickly make up for the operator's cognitive shortcomings in brain alertness and reaction, and improve the operator's task performance.

[0183] In one example, the fourth image includes multiple frames.

[0184] In one example, the number of the multiple frames included in the fourth image is no more than 9 frames.

[0185] The fourth preset target object is a fifth identifier randomly generated in multiple frames of each frame of the fourth image.

[0186] The fourth actual target object is the actual position of the fifth marker selected by the manipulator for each frame of the fourth image.

[0187] For example, the number of fourth image sequence subsets may be 5, the number of fourth images included in each fourth image sequence subset may be 3, the number of fifth identifiers included in the fourth images of the 5 fourth image sequence subsets may be an arithmetic progression, and the common difference of the arithmetic progression may be 1, for example, the number of fifth identifiers included in the fourth image of the first fourth image sequence subset may be 1, the number of fifth identifiers included in the fourth image of the second fourth image sequence subset may be 2, ..., the number of fifth identifiers included in the fourth image of the fifth fourth image sequence subset may be 5, for example.

[0188] Step S620: displaying a plurality of fourth image frames of a plurality of fourth image sequence subsets in sequence according to a fourth preset time interval and a preset order.

[0189] The preset order includes sequentially displaying a plurality of fourth image sequence subsets in an order from least to most of the fourth preset target objects.

[0190] In one example, the display device 1160 of the electronic device 1000 is controlled to sequentially display multiple frames of fourth images of multiple fourth image sequence subsets according to a fourth preset time interval and a preset order until the display of all fourth images in the fourth image sequence set is completed.

[0191] In an example, the fourth preset time interval may be set at the factory or may be set by the user. The fourth preset time interval may be, for example, 2 seconds.

[0192] In one example, multiple subsets of the fourth image sequence are displayed in order from the least to the most according to the fifth identifier, that is, the training difficulty is gradually increased, and enhanced training is performed on the operator, which can quickly make up for the operator's cognitive shortcomings in brain alertness and reaction ability and improve the operator's task performance.

[0193] In one example, the display device 1160 of the electronic device 1000 is controlled to display only one frame of the fourth image each time, and each frame of the fourth image includes multiple pictures.

[0194] Step S630: obtaining a fourth actual target object selected by the controlling player according to the fourth image within each fourth preset time interval.

[0195] In one example, when the display device 1160 displays the fourth image in the fourth image sequence set, after the display of the fourth image of the current frame is completed, the display device 1160 displays the next fourth image frame at a fourth preset time interval, and the controlling player selects the actual number of the sub-screen where the fifth marker on the fourth image of the current frame is located through the keyboard of the input device 1160, and obtains the actual position of the fifth marker selected by the controlling player on the fourth image of the current frame.

[0196] In one example, the sub-picture numbers are marked on the multiple sub-pictures of each frame of the fourth image.

[0197] In one example, when there are multiple fifth marks on the fourth image, the controlling player needs to press the actual numbers of the sub-screens where the multiple fifth marks are located through the keyboard of the input device 1160 at the same time.

[0198] Step S640: for each fourth image, determine whether the fourth actual target object selected by the manipulating player is consistent with the fourth preset target object corresponding to the fourth image, and obtain a fourth determination result.

[0199] In an example, in step S640, for each fourth image, determining whether the fourth actual target object selected by the manipulating player is consistent with the fourth preset target object corresponding to the fourth image may further include: for each fourth image, determining whether the actual position of the fifth identifier selected by the manipulating player is the same as the randomly generated position of the fifth identifier corresponding to the fourth image.

[0200] In one example, by judging whether the actual number of the fifth identifier selected by the controlling player is the same as the set number of the fifth identifier corresponding to the fourth image, it is judged whether the actual position of the fifth identifier selected by the controlling player is the same as the randomly generated position of the fifth identifier corresponding to the fourth image.

[0201] In one example, based on the fourth judgment result being yes, the fourth image is recorded as a valid fourth image; based on the fourth judgment result being no, the fourth image is recorded as an invalid fourth image.

[0202] In one example, the set number of the fifth marker refers to the number of the sub-screen where the randomly generated position of the fifth marker is located.

[0203] Step S650: Determine the brain alertness and reaction training score of the manipulating player according to the fourth judgment result.

[0204] In one example, according to the judgment results of all fourth images in the fourth image sequence set, the brain alertness and reaction training score of the manipulating player can be calculated.

[0205] In one example, the brain alertness and responsiveness training score S4 It can be obtained by the following formula (4):

[0206]

[0207] In formula (4), M represents the number of fourth image sequence subsets, N represents the number of fourth images in a fourth image sequence subset, and L represents the number of valid fourth images.

[0208] <Equipment Embodiment>

[0209] Figure 7 is a schematic diagram of the hardware structure of an electronic device according to another embodiment.

[0210] In this embodiment, an electronic device 1000 is also provided. The electronic device 1000 may be as follows: Figure 1 The terminal device 1100 is shown.

[0211] like Figure 7 As shown, the electronic device 1000 may also include a processor 1210 and a memory 1220, wherein the memory 1220 is used to store an executable computer program, and the processor 1210 is used to control the operation of the electronic device 1000 to execute the simulation training method according to any embodiment of the present invention under the control of the computer program.

[0212] Each module of the above electronic device 1000 may be implemented by the processor 1210 in this embodiment executing a computer program stored in the memory 1220, or may be implemented by other circuit structures, which is not limited here.

[0213] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0214] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: 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), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0215] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0216] The computer program instructions for performing the operation of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may 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 may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present invention.

[0217] Various aspects of the present invention are described herein with reference to the flow charts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each box of the flow chart and / or block diagram and the combination of each box in the flow chart and / or block diagram can be implemented by computer-readable program instructions.

[0218] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0219] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0220] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a part of a module, a program segment or an instruction, and a part of the module, a program segment or an instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

[0221] Embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the marketplace, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A UAV control simulation training method, characterized in that: include Acquire an image sequence set, wherein the image sequence set includes a plurality of image sequence subsets, the image sequence subsets include a plurality of frames of images, the images include at least one preset target object, the number of the preset target objects included in the plurality of frames of images of the same image sequence subset is equal, and the number of the preset target objects included in the images of different image sequence subsets is different; Displaying the multiple frames of the image of the plurality of image sequence subsets in sequence according to a preset time interval and a preset order; wherein the preset order includes displaying the plurality of image sequence subsets in sequence according to the preset target objects from least to most; Obtaining the actual target object selected by the player according to the image within each preset time interval; For each image, determining whether the actual target object selected by the controlling player is consistent with the preset target object of the corresponding image, and obtaining a determination result; According to the judgment result, the training score of the manipulating player is determined.

2. The simulation training method according to claim 1, characterized in that: The image sequence set is a first image sequence set, the image sequence subset is a first image sequence subset, the image is a first image, the preset target object is a first preset target object, the preset time interval is a first preset time interval, the actual target object is a first actual target object, the judgment result is a first judgment result, and the training score is a selective attention training score; The first preset target object is a first mark pre-set at a specified position of each frame of the first image, and the first mark has different positions on different first images of the same first image sequence subset; The first actual target object is the actual position of the first mark selected by the manipulator for each frame of the first image.

3. The simulation training method according to claim 2, for each first image, determining whether the first actual target object selected by the manipulator is consistent with the first preset target object corresponding to the first image, comprising: For each first image, it is determined whether the actual position of the first marker selected by the controlling player is the same as the set position of the first marker corresponding to the first image.

4. The simulation training method according to claim 1, characterized in that: The image sequence set is a second image sequence set, the image sequence subset is a second image sequence subset, the image is a second image, the preset target object is a second preset target object, the preset time interval is a second preset time interval, the actual target object is a second actual target object, the judgment result is a second judgment result, and the training score is an attention span training score; The second preset target object is a second mark preset at a specified position of each frame of the second image, and the second mark has different positions on different second images of the same second image sequence subset; The second actual target object is the actual number of the second identifiers selected by the manipulator for each frame of the second image.

5. The simulation training method according to claim 4, for each second image, determining whether the second actual target object selected by the manipulator is consistent with the second preset target object corresponding to the second image, comprising: For each second image, it is determined whether the actual number of the second identifiers selected by the controlling player is equal to the set number of the second identifiers corresponding to the second image.

6. The simulation training method according to claim 1, characterized in that: The image sequence set is a third image sequence set, the image sequence subset is a third image sequence subset, the image is a third image, the preset target object is a third preset target object, the preset time interval is a third preset time interval, the actual target object is a third actual target object, the judgment result is a third judgment result, and the training score is an attention allocation training score; Each frame of the third image includes a main picture and multiple sub-pictures; The third preset target object is a third mark preset at a designated position of a main picture of each frame of the third image, and a fourth mark randomly generated in a plurality of sub-pictures of each frame of the third image, wherein the positions of the third mark on different third images of the same third image sequence subset are different; The third actual target object is the actual number of the third markers and the actual position of the fourth marker selected by the manipulator for each frame of the third image.

7. The simulation training method according to claim 6, for each third image, determining whether the third actual target object selected by the manipulator is consistent with the third preset target object corresponding to the third image, comprising: For each third image, determining whether the actual number of the third identifiers selected by the manipulating player is equal to the set number of the third identifiers corresponding to the third image; Determining whether the actual position of the fourth marker selected by the controlling player is the same as the randomly generated position of the fourth marker corresponding to the third image; When the actual number of the third identifiers selected by the controlling player is equal to the set number of the third identifiers corresponding to the third image, and the actual position of the fourth identifier selected by the controlling player is the same as the randomly generated position of the fourth identifier corresponding to the third image, it is determined that the third actual target object is consistent with the third preset target object; Otherwise, it is determined that the third actual target object is inconsistent with the third preset target object.

8. The simulation training method according to claim 1, characterized in that: The image sequence set is a fourth image sequence set, the image sequence subset is a fourth image sequence subset, the image is a fourth image, the preset target object is a fourth preset target object, the preset time interval is a fourth preset time interval, the actual target object is a fourth actual target object, the judgment result is a fourth judgment result, and the training score is a brain alertness and reaction training score; Each frame of the fourth image includes multiple pictures; The fourth preset target object is a fifth identifier randomly generated in multiple frames of each frame of the fourth image; The fourth actual target object is the actual position of the fifth mark selected by the manipulator for each frame of the fourth image.

9. The simulation training method according to claim 8, characterized in that: For each fourth image, determining whether the fourth actual target object selected by the controlling player is consistent with the fourth preset target object corresponding to the fourth image, including: For each fourth image, it is determined whether the actual position of the fifth marker selected by the controlling player is the same as the randomly generated position of the fifth marker corresponding to the fourth image.

10. An electronic device, characterized in that: The method comprises a processor and a memory; the memory stores computer instructions, and when the computer instructions are executed by the processor, the method according to any one of claims 1 to 9 is implemented.

Images