Methods and related devices for processing virtual training objects in a hand function training system

Abstract

The present application provides a method and related device for processing a virtual training object in a hand function training system, the method comprising: determining the first image information of the target training object according to the user's historical training information and the initial image information of the target training object selected by the user, the initial image information refers to the image used to generate the preset initial state of the target training object, and the first image information refers to the image used to update the initial state of the target training object; controlling the display device to display the first image information; detecting the user's selection operation for the target part in the first image; in response to the selection operation, controlling the manipulator to collect the action information of the action performed by the user's hand; determining the image update information for updating the image of the target part according to the first image information and the action information; and controlling the display device to display the image update information. In the present application, the image of the target part of the target training object is updated based on the manipulator data, the interactivity is enhanced, and the training interest is thereby improved.

Description

Technical Field

[0001] The present application relates to the field of robotics technology, and in particular to a method and a related device for processing a virtual training object in a hand function training system. Background Art

[0002] To assist users in their training, engineers have designed various auxiliary mechanical devices to assist users in their limb training. For example, when training hands, users can wear a robot arm to train. However, existing robots perform training calculations according to fixed instructions, and the training process is single, which leads to low training efficiency when the training time is long. Summary of the invention

[0003] The present application provides a method and related device for processing a virtual training object in a hand function training system, which updates the image of a target part of a target training object through manipulator data, enhances interactivity, and thus improves the interest of training.

[0004] In a first aspect, an embodiment of the present application provides a method for processing a virtual training object in a hand function training system, which is applied to a computing device of the hand function training system, wherein the hand function training system includes the computing device, a display device, and a manipulator, wherein the computing device is connected to the display device and the manipulator, respectively, and the method includes:

[0005] Determining first image information of the target training object according to the historical training information of the user and the initial image information of the target training object selected by the user, wherein the initial image information refers to an image used to generate a preset initial state of the target training object, and the first image information refers to an image used to update the initial state of the target training object;

[0006] Controlling the display device to display the first image information;

[0007] detecting a selection operation by the user on a target part in the first image;

[0008] In response to the selection operation, the manipulator is controlled to collect action information of the action performed by the user's hand, wherein the action information includes joint angle information, joint movement angle information, and joint rotation direction information of each joint of the manipulator when the user's hand performs the action;

[0009] Determining image update information for updating the image of the target part according to the first image information and the action information;

[0010] The display device is controlled to display the image update information.

[0011] In a second aspect, an embodiment of the present application provides a device for processing a virtual training object in a hand function training system, which is applied to a computing device of the hand function training system, wherein the hand function training system includes the computing device, a display device, and a manipulator, wherein the computing device is connected to the display device and the manipulator, respectively, and the device includes:

[0012] a first determining unit, configured to determine first image information of the target training object according to historical training information of the user and initial image information of the target training object selected by the user, wherein the initial image information refers to an image used to generate a preset initial state of the target training object, and the first image information refers to an image used to update the initial state of the target training object;

[0013] A first control unit, used for controlling the display device to display the first image information;

[0014] A detection unit, configured to detect a selection operation of the user on a target part in the first image;

[0015] a second control unit, configured to respond to the selection operation and control the manipulator to collect motion information of the motion performed by the user's hand, wherein the motion information includes joint angle information, joint movement angle information, and joint rotation direction information of each joint of the manipulator when the user's hand performs the motion;

[0016] a second determining unit, configured to determine image update information for updating the image of the target part according to the first image information and the action information;

[0017] A third control unit is used to control the display device to display the image update information.

[0018] In a third aspect, an embodiment of the present application provides a terminal device, comprising at least one processor, a communication interface and a memory, wherein the communication interface is used to send and / or receive data, the memory is used to store a computer program, and the at least one processor is used to call the computer program stored in the memory to implement any method as described in the first aspect of the present application.

[0019] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory, wherein the memory is used to store computer program code, and the computer program code comprises computer instructions. When the processor executes the computer instructions, the electronic device executes instructions such as the steps in any method of the first aspect of the present application.

[0020] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes part or all of the steps described in the steps of any method in the first aspect of the embodiment of the present application.

[0021] In a sixth aspect, the present application provides a computer program, wherein the computer program is operable to cause a computer to execute some or all of the steps described in any method of the first aspect of the present application. The computer program may be a software installation package.

[0022] It can be seen that in the embodiment of the present application, the initial image information refers to an image used to generate a preset initial state of the target training object. First, the first image information of the target training object is determined based on the user's historical training information and the initial image information of the target training object selected by the user. The first image information refers to an image used to update the initial state of the target training object. Then the display device is controlled to display the first image information; then the user's selection operation for the target part in the first image is detected; in response to the selection operation, the manipulator is controlled to collect the action information of the action performed by the user's hand, so as to determine the image update information for updating the image of the target part according to the first image information and the action information, so that the image of the target part updated in the image update information is more matched with the action information, that is, the interactivity is higher. Finally, the display device is controlled to display the image update information. In the present application, the image of the target part of the target training object is updated based on the manipulator data, so that the manipulator data is more correlated with the image of the target training object, the interactivity is increased, and the training interest is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 A schematic diagram of the structure of a hand function training system provided in an embodiment of the present application;

[0025] Figure 2 A flowchart of a method for processing a virtual training object in a hand function training system provided in an embodiment of the present application;

[0026] Figure 3 A schematic diagram of a display interface of a display device provided in an embodiment of the present application;

[0027] Figure 4 A schematic diagram of an initial image of a target training object provided in an embodiment of the present application;

[0028] Figure 5 A schematic diagram of a first image of a target training object provided in an embodiment of the present application;

[0029] Figure 6 A schematic diagram of the structure of a manipulator provided in an embodiment of the present application;

[0030] Figure 7 A schematic diagram of a portion of the structure of a finger joint drive module in a manipulator provided in an embodiment of the present application;

[0031] Figure 8 A schematic diagram of an updated first image of a target training object provided in an embodiment of the present application;

[0032] Fig. 9 A schematic diagram of another target training object after the first image is updated according to an embodiment of the present application;

[0033] Fig.10 A schematic diagram of an updated first image of another target training object provided in an embodiment of the present application;

[0034] Fig.11 A block diagram of the functional units of a device for processing a virtual training object in a hand function training system provided in an embodiment of the present application;

[0035] Fig.12 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0037] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0038] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0039] See also Figure 1 , Figure 1 This is a schematic diagram of the structure of a hand function training system provided in an embodiment of the present application. Figure 1 As shown, the hand function training system 1 includes a computing device, a manipulator and a display device. The computing device is connected to the manipulator and the display device, respectively, so as to facilitate data interaction between the computing device and the manipulator, and between the computing device and the display device. It is understandable that the number of manipulators connected to the computing device can be set according to actual needs, for example, two or more manipulators can be connected, and there is no specific limitation here.

[0040] The computing device in this application can be a terminal device such as a smart phone, tablet computer, laptop computer, desktop computer, wearable device, head-mounted device, vehicle-mounted terminal, etc., which is installed with a control application (i.e., application client). The type of terminal device is not specifically limited here. It should be understood that when the application client runs on the computing device, it can be respectively connected to Figure 1 The robot arm shown interacts with the display device.

[0041] Please combine the following Figure 2 The method in the embodiment of the present application is described in detail:

[0042] See also Figure 2 , Figure 2 The present invention provides a flowchart of a method for processing a virtual training object in a hand function training system. Figure 2 As shown, a method for processing a virtual training object in a hand function training system is applied to a computing device of the hand function training system. The hand function training system 1 includes the computing device, a display device and a manipulator. The computing device is connected to the display device and the manipulator respectively. The method includes:

[0043] Step S201 : determining first image information of the target training object according to the historical training information of the user and the initial image information of the target training object selected by the user.

[0044] The initial image information refers to an image used to generate a preset initial state of the target training object, and the first image information refers to an image used to update the preset initial state of the target training object. Figure 3 , Figure 3 A schematic diagram of a display interface of a display device provided in an embodiment of the present application. Figure 3 As shown, the display interface of the display device includes a first display area 301 and a second display area 302, wherein the first display area 301 includes fixed icons, such as a return icon representing return on the left side of the first display area 301, and a display icon representing the year XXXX / XX / XX and the specific time XX:XX on the right side of the first display area 301. The user can select the target training object in the second display area 302, and the second display area 302 displays the final image of the target training object, so that the user can more intuitively understand the target training object to be generated and improve the selection efficiency. The second display area 302 also includes prompt information and function buttons, and the prompt information is "Please confirm whether to generate this training object." The function button includes a confirmation button "Yes" for confirming the generation of the training object, and a denial button "No" for denying the generation of the training object, so as to facilitate user selection. The computing device determines the initial image information based on the image of the initial state of the selected target training object. For example, please combine Figure 4 , Figure 4 A schematic diagram of an initial image of a target training object provided in an embodiment of the present application. Figure 4 As shown, if the target training object selected by the user is a sheep, in order to make the training process more interactive, the image of the sheep in its initial state can be set as an image of a sheep-like object. The first image information of the target training object is determined based on the initial image information of the target training object selected by the user and the user's historical training information, so that the first image is more personalized.

[0045] Step S202: Control the display device to display the first image information.

[0046] After the first image information is determined, the display device is controlled to display the first image information to provide support for subsequent acquisition of robot data.

[0047] Step S203: detecting a selection operation of the user on a target part in the first image.

[0048] After the display device displays the first image information, the user can select the part to interact with according to the needs. Figure 4 and Figure 5 , Figure 5 A schematic diagram of a first image of a target training object provided in an embodiment of the present application. Figure 5As shown, the display device displays a first image generated according to the first image information, wherein the first image includes a sheep-like object, wherein, to facilitate user selection, the optional parts can be indicated by specific icons, such as Figure 5 In the figure, there are first icons 402, second icons 403, third icons 404 and fourth icons 405. If the second part corresponding to the third icon 404 is selected based on the user selection operation, the selected second part is the target part. After the target part is determined, the manipulator data only corresponds to the target part in the first image, which increases the control accuracy requirements for the training action, increases the difficulty of training, and thus improves the training effect. And because of different parts, different actions can achieve different shaping effects when used for shaping, which increases the probability of using different actions, thereby increasing the training range.

[0049] Step S204 , in response to the selection operation, controlling the robot arm to collect motion information of the motion performed by the user's hand.

[0050] Among them, see Figure 6 and Figure 7 , Figure 6 A schematic diagram of the structure of a manipulator provided in an embodiment of the present application is shown in FIG. Figure 7 This is a partial structural diagram of the finger joint drive module in the manipulator provided in the embodiment of the present application. Figure 6 As shown, the manipulator includes a manipulator palm module 52 and a finger joint drive module 51. The finger joint drive module 51 is connected to the manipulator palm module 52. A power supply and a Bluetooth module are provided in the manipulator palm module 52, and the power supply is used to store electrical energy and supply energy to the manipulator. The manipulator can exchange data with a computing device through the Bluetooth module.

[0051] Each finger of the finger joint driving module 51 corresponds to at least one joint driving component, so that each finger of the manipulator can be driven independently. Figure 7, each joint drive assembly includes a worm member 511, a turbine member 512 and a driving member 513. The worm member 511 is connected to the driving member 513, and the worm member 511 and the turbine member 512 are meshed. Based on the training instruction, under the drive of the driving member 513, the turbine member 512 and the driving member 513 are driven to transmit. Since the degree of the joint is adjusted by using the turbine member 512 and the driving member 513, the manipulator can move more smoothly when performing training actions, reduce the noise when the manipulator performs actions, and the control accuracy of the degree of the joint is higher. And the manipulator structure is made compact, the volume and weight of the manipulator are further reduced, and the user experience is improved. The manipulator is also provided with a palm fixing belt and a finger fixing belt. When the user uses the manipulator for training, the palm fixing belt and the finger fixing belt of the manipulator are used to bind the hand to the manipulator, and then the manipulator performs training actions based on the training instructions, such as grasping, holding, pinching, etc. And drive the user's hand to make corresponding training actions to achieve passive training of the user. And the robot can also collect data on actions performed by the user.

[0052] Specifically, the action information includes the joint angles, joint movement angle information and joint rotation direction information of each joint of the manipulator when the user's hand performs the action. In response to the selection operation, the manipulator collects the action information of the action performed by the user's hand to provide data support for subsequent processing.

[0053] Step S205: determining image update information for updating the image of the target part according to the first image information and the action information.

[0054] Among them, when facing different parts of the target training object, different training actions cause different deformations to different parts. By obtaining action information, the training action to be performed can be determined according to the action information, thereby determining the deformation caused to the target part, making the interaction more accurate and improving the fun of the interaction process. Specifically, the training action and the deformation caused by the training action to the target part are determined according to the action information, and the image update information is generated in combination with the first image information, so that the image determined for updating the target part is more matched with the manipulator data.

[0055] Step S206: controlling the display device to display the image update information.

[0056] Among them, the display device is controlled to display image update information.

[0057] It can be seen that in this example, the image of the target part of the target training object is updated based on the robot data, which improves interactivity and thus increases the user's enthusiasm for training.

[0058] In one possible example, when controlling the robot to collect motion information of actions performed by the user's hand, if it is detected that the robot has not collected motion information within a preset time period, the preset training action corresponding to the target part and the historical training information of the user are obtained, and the training action performed by the robot is determined according to the preset training action and the historical training information, thereby driving the user to perform the action.

[0059] Specifically, the preset training action is an action that can be used to shape the target part, and the historical training information includes the historical training actions performed by the user in the period before the current time point, the physical state information when performing each historical training action, and the training intensity of each historical training action. The physical state information refers to the degree of spasm of the user's body when the user is driven by the manipulator to perform the historical training action, or the degree of spasm of the user's body when the user actively drives the manipulator to perform the training action. The training intensity characterizes the range value of the joint adjustment degree when the manipulator performs the training action. For example, the joint adjustment range is 30°~40°. The greater the training intensity, the greater the joint adjustment range. The magnitude of the training intensity is determined according to the joint adjustment range, and the training intensity is visualized to make the control more accurate. Specifically, based on the preset training action, the target historical training action that can be used to shape the target part in the historical training action is determined, and then the physical state information and training intensity of the target historical training action are found. If the degree of spasm in the physical state information is greater than the preset degree, the training intensity is adjusted down to obtain the reduced training intensity. The manipulator is controlled to execute the target historical training action of the lowered training intensity to drive the user to perform the action, and the action information of the user's hand performing the action is collected through the manipulator. Alternatively, after the manipulator is controlled to perform the target historical training action for a preset number of times, the user is prompted to actively perform the target historical training action, and the action information of the user's hand performing the action is collected through the manipulator. The intelligence of the hand function training system 1 is improved.

[0060] In a possible example, the historical training information includes a first training action performed by the user and a first maximum joint movement angle corresponding to the first training action, and determining the first image information of the target training object based on the historical training information of the user and the initial image information of the target training object selected by the user includes: obtaining the first training action in the historical training information and the first maximum joint movement angle corresponding to the first training action; if a first target training action identical to the first training action is found from a preset training action set, obtaining a second maximum joint movement angle corresponding to the first target training action from the training action set, the training action set including a plurality of preset reference training actions corresponding to the target training object, and a maximum joint movement angle required for each reference training action to process the target training object, the first target training action being a training action within the plurality of reference training actions; if the second maximum joint movement angle is greater than the first maximum joint movement angle, determining the first image information of the target training object based on the second maximum joint movement angle, the first training action and the initial image information.

[0061] Among them, when the same training action is used to process the same part, when the joint movement angles of each joint of the finger in the training action are different, the deformation of the part will also be different, thereby improving the user's control accuracy of the training action. For example, the training action used is pinching the fingers twice. When the action is performed, the smaller the distance between the thumb and the index finger, the larger the joint movement angle of the thumb and the index finger in the pinching training action, and the greater the deformation caused to the training object. The preset training action set includes multiple reference training actions corresponding to the target training object, that is, reference training actions that can process the target training object, and the maximum joint movement angle required for each reference training action to process the target training object. Among them, the maximum joint movement angle is the largest joint movement angle among the joint movement angles corresponding to the various finger joints of the same training action. For example, the first reference training action can process the first part and the second part of the target training object. When processing the first part of the target training object, the index finger joint requires the first joint movement angle, and the middle finger joint requires the second joint movement angle; when processing the second part of the target training object, the index finger joint requires the third joint movement angle, and the middle finger joint requires the fourth joint movement angle. If the first joint movement angle is greater than the second joint movement angle, the third joint movement angle, and the fourth joint movement angle, the first joint movement angle is set as the maximum joint movement angle required for the first reference training action to process the target training object.

[0062] In a specific example, the historical training information includes the first training action performed by the user and the first maximum joint movement angle corresponding to the first training action, and the first training action in the historical training information and the first maximum joint movement angle corresponding to the first training action are obtained. Then, a search is made from the training action set to determine whether there is a first target training action that is the same as the first training action. If there is a reference training action that is the same as the first training action, the reference training action is set as the first target training action, and the second maximum joint movement angle corresponding to the first target training action is obtained. The second maximum joint movement angle is the maximum joint movement angle required when the first target training action is used to process the target training object in the training action set. The size of the second maximum joint movement angle and the first maximum joint movement angle is determined. If the second maximum joint movement angle is greater than the first maximum joint movement angle, it means that the user cannot perform the first target training action of the second maximum joint movement angle. Therefore, the first image information of the target training object is determined based on the second maximum joint movement angle, the first training action and the initial image information.

[0063] If there is no reference training action identical to the first training action in the training action set, then find the reference training action with the greatest similarity to the first training action in the training action set, and obtain the maximum joint movement angle corresponding to the reference training action. Then obtain the number of times the reference training action with the maximum joint movement angle has been executed in the historical training data of different users, and if the number is less than the preset number, determine the first image information of the target training object according to the maximum joint movement angle corresponding to the reference training action, the reference training action and the initial image information.

[0064] It can be seen that in this example, the first image information is determined according to the user's historical training information and the initial image information, so that the first image based on the first image information is more personalized, thereby improving the user's training enthusiasm.

[0065] In a possible example, if the historical training information includes multiple first training actions. When searching, search for reference training actions corresponding to each first training action from the training action set. For example, if two first training actions are included, search for a reference training action corresponding to one of the two first training actions from the training action set, and search for a reference training action corresponding to the other of the two first training actions from the training action set. Then compare the first maximum joint movement angle corresponding to each first training action with the corresponding second maximum joint movement angle. Determine the first image information of the target training object according to the second maximum joint movement angle greater than the first maximum joint movement angle, the first training action corresponding to the second maximum joint movement angle, and the initial image information. For example, if the second maximum joint movement angle corresponding to the two first training actions is greater than the corresponding first maximum joint movement angle, the first image information of the target training object is determined according to the two first training actions, the second maximum joint movement angles corresponding to the two first training actions, and the initial image information. Confirm the training action of the joint movement angle that the user cannot perform, thereby generating the first image information, avoiding the unshaping parts in the target training object, improving the intelligence, and thus improving the fun of the user interaction process.

[0066] In a possible example, if the second maximum joint movement angle is greater than the first maximum joint movement angle, the first image information of the target training object is determined according to the second maximum joint movement angle, the first training action and the initial image information, including: if the second maximum joint movement angle is greater than the first maximum joint movement angle, the initial update part in the target training object is determined according to the second maximum joint movement angle and the first training action, the initial update part representing the part in the target training object that needs to be shaped using the first training action with the second maximum joint movement angle; obtaining the preset optimal image information of the initial update part; and determining the first image information of the target training object according to the optimal image information of the initial update part and the initial image information.

[0067] In a specific example, if the second maximum joint movement angle is greater than the first maximum joint movement angle, the initial update part in the target training object is determined according to the second maximum joint movement angle and the first training action. Since the user cannot perform the first training action of the second maximum joint movement angle, after determining the initial update part, the optimal image information corresponding to the initial update part is directly obtained. Finally, the first graphic information is determined according to the optimal image information and the initial image information. For example, please refer to Figure 4 and Figure 5 , if the initial update part is the lamb leg, Figure 4After determining the optimal image information corresponding to the first part 401, the first graphic information is determined according to the optimal image information and the initial image information, that is, Figure 4 The middle ellipse shape becomes Figure 5 The shape of the lamb leg is avoided from the initial state of the target training object, saving time. In this solution, the first image information of the target training object is determined according to the optimal image information and the initial image information, so that the initial update part in the first image corresponding to the first image information is already the optimal shape, avoiding the target training object from failing to reach the ideal shape and affecting the user's training enthusiasm.

[0068] It can be seen that in this example, after determining the training action with the maximum joint movement angle that the user cannot perform, the part of the target training object that the user cannot shape is determined based on the training action, the optimal image information of the part is obtained, and then the first image information is generated based on the optimal image information, so that the first image generated based on the first image information is more personalized, thereby improving the user's enthusiasm for training.

[0069] In a possible example, the image update information used to update the image of the target part according to the first image information and the action information includes: obtaining the joint angle information, the joint movement angle information and the joint rotation direction information in the action information; determining the second training action corresponding to the action performed by the user's hand according to the joint angle information; determining the relative distance information between each finger of the robot according to the joint angle information, the joint movement angle information and the joint rotation direction information, the relative distance information including the relative distance value between each two fingers of the user; determining the deformation degree value according to the second training action, the relative distance information and the target part; obtaining the deformation area corresponding to the target part when the target part is shaped using the second training action; obtaining the target image information of the target part in the first image information; and determining the image update information according to the deformation area, the deformation degree value and the target image information.

[0070] In a specific example, the joint angle information, joint movement angle information and joint rotation direction information in the action information are obtained, and the joint angle information includes the joint angles of each joint of the manipulator collected by the manipulator during the user's hand performing the action. The joint movement angle information includes the joint movement angles of each joint of the manipulator's fingers. The joint rotation direction information includes the joint rotation direction of each joint of each finger of the manipulator relative to the reference finger joint in the preparatory action. The reference finger joints can use finger joints such as the thumb joint and the index finger joint, and there is no specific limitation here.

[0071] Among them, the second training action corresponding to the action performed by the user's hand is determined according to the joint angle information. The relative distance information between the fingers of the manipulator is determined according to the joint movement angle information and the joint rotation direction information. The deformation degree value is determined according to the relative distance information, the second training action and the target part. The deformation area corresponding to the target part when the second training action is used to shape the target part is obtained. After obtaining the target image corresponding to the target part, the image update information for updating the target part is determined according to the deformation area and deformation degree value caused to the target part. The deformation area represents the size area of ​​the deformation, for example, please refer to Figure 8 , Figure 8 A schematic diagram of an updated first image of a target training object provided in an embodiment of the present application. If the user chooses to process the third part corresponding to the first icon 402 in the first image, and the determined deformation area is smaller than the entire area of ​​the third part, only the shape of the object within the deformation area in the third part is changed. Figure 8 The first sub-portion 4021 of the third portion is shown. After the first sub-portion 4021 is shaped, the other sub-portions of the third portion are deformed in sequence.

[0072] The deformation degree value is the value of the final shape change of the target part caused by each training action. The size of the deformation degree value is determined according to the relative distance information. Specifically, the relative distance values ​​of each finger relative to the thumb of the second training action are obtained, and the deformation degree value is determined according to each relative distance value. For example, if the second training action is pinching the fingers, the smaller the relative distance value, the larger the deformation degree value, and the relative distance values ​​between the thumb and the other four fingers in the relative distance information are obtained, and the deformation degree value corresponding to each relative distance value is obtained from the preset database. For example, please refer to Fig. 9 and Fig.10 , Fig. 9 This is a schematic diagram of another updated first image of a target training object provided in an embodiment of the present application. Fig.10 A schematic diagram of another updated first image of a target training object provided in an embodiment of the present application. If the user chooses to process the fourth part corresponding to the second icon 403 in the first image, when the robot detects that the second training action is being performed, the relative distance values ​​between the thumb and the other four fingers are obtained from the relative distance information, and then the deformation degree values ​​corresponding to the relative distance values ​​are obtained. If the deformation degree value is 50%, image update information is generated based on the deformation degree value, and the first image corresponding to the target training object is updated based on the image update information. The updated image is as follows: Fig. 9 As shown, the fourth part corresponding to the second icon 403 is Figure 8 The middle ellipse becomes Fig. 9The ear-like shape in the figure has a similarity of 50% with the final shape of the fourth part. When the robot detects that the user performs the second training action with the relative distance information again, the corresponding deformation degree value is 50%, and image update information is generated based on the deformation degree value. The image update information is updated again, and the fourth part corresponding to the second icon 403 is changed from Fig. 9 The ear-like shape changes to Fig.10 It is understandable that the deformation degree value caused by the training actions with different relative distance information on the parts of the target training object can be set according to the needs, and there is no specific limitation here.

[0073] It can be seen that in this example, the training action to be performed is determined based on the action information, and the deformation area and degree of deformation caused by the training action on the target part are determined, thereby determining the deformation of the target part, so that the generated image update information is more consistent with the training, thereby improving the interactive interest.

[0074] In a possible example, after determining the image update information for updating the image of the target part according to the first image information and the action information, the method further includes:

[0075] The joint angle information in the action information is obtained, and the third training action corresponding to the action performed by the user's hand is determined according to the joint angle information. The standard joint angle information of the third training action is obtained, and the manipulator is controlled to perform the third training action according to the joint angle information in the action information and the standard joint angle information.

[0076] Specifically, the similarity between the joint angle information in the action information and the joint angle of each finger in the standard joint angle information is compared respectively. The numerical value of the statistical similarity is greater than the preset similarity; if the numerical value is less than the preset numerical value, the manipulator is controlled to perform a third training action of a preset training intensity to drive the user to perform training. For example, the first final angle value of the first joint of the index finger when the action is completed in the joint angle information, and the second final angle value of the first joint of the index finger when the training action is completed in the standard joint angle information are obtained. If the similarity between the first final angle value and the second final angle value is greater than the preset similarity, the numerical value is increased by 1.

[0077] Furthermore, when the manipulator performs training actions, the manipulator collects the user's body data during the training process, and determines the image correction percentage based on the body data. Specifically, the spasm degree value in the body data is obtained, and the image correction percentage is determined according to the spasm degree, and the spasm degree value is inversely proportional to the image correction percentage. The image correction percentage is a percentage used to improve the similarity between the final image of the target part in the image update information and the optimal image corresponding to the target part. For example, if the similarity between the final image of the target part in the image update information and the optimal image corresponding to the target part is 30%, and the image correction percentage is 5%, then the similarity between the final image of the target part in the image update information and the optimal image corresponding to the target part is increased to 35%. Update the image update information according to the image correction percentage to obtain the updated image update information; control the display device to display the updated image update information.

[0078] It can be seen that in this example, when it is detected that the user's action is significantly different from the corresponding third training action, the robot is controlled to perform the third training action, thereby improving the training accuracy. And based on the user's body data collected during the training process, the image of the target part is further corrected to improve the fun of the interaction.

[0079] In a possible example, the initial angle values ​​of each finger joint when the user's hand starts to perform an action in the joint angle information are obtained; the initial distance values ​​between the finger joints of the manipulator are determined according to the initial angle values ​​of the finger joints; the coordinate system of the manipulator is established according to the initial distance values ​​between the finger joints; the joint movement angles of each finger joint of the manipulator in the joint movement angle information and the joint rotation directions of each finger joint of the manipulator in the joint rotation direction information are obtained; and the relative distance information is determined according to the coordinate system, the joint movement angles of each finger joint and the joint rotation directions of each finger joint.

[0080] In a specific example, the initial angle value of each finger joint when the user's hand starts to perform an action in the joint angle information is obtained, and the initial distance and relative position between each finger of the manipulator are determined according to the initial joint angle value of each finger joint. The coordinate system of the manipulator is established according to the initial distance between each finger and the relative position between each finger, and the coordinate system takes the position of any finger in the manipulator as the origin. Then, the initial coordinate position of each finger of the manipulator in the coordinate system is obtained, and the joint movement angle of each finger joint of the manipulator in the joint movement angle information and the joint rotation direction of each finger joint of the manipulator in the joint rotation direction information are obtained. The final coordinate position of each finger in the coordinate system is determined according to the initial coordinate position of each finger in the coordinate system, the joint movement angle of each finger joint and the joint rotation direction of each finger joint, and the relative distance value between each two fingers is determined based on the final coordinate position. Finally, all relative distance values ​​corresponding to the manipulator are integrated into relative distance information.

[0081] For example, the joint movement angles of each joint of the index finger and the joint movement angles of each joint of the thumb are obtained in the joint movement angle information, and the rotation directions of each joint of the index finger and the rotation directions of each joint of the thumb are obtained in the joint rotation direction information. The initial coordinate positions of the index finger and the thumb in the coordinate system are obtained respectively. The index finger movement distance value is determined based on the joint movement angles of each joint of the index finger, and the index finger movement direction is determined based on the rotation directions of each joint of the index finger. The final coordinate position of the index finger in the coordinate system is determined according to the initial coordinate position of the index finger, the index finger movement distance value, and the index finger movement direction. The thumb movement distance value is determined based on the joint movement angles of each joint of the thumb, and the thumb movement direction is determined based on the rotation directions of each joint of the thumb. The final coordinate position of the thumb in the coordinate system is determined according to the initial coordinate position of the thumb, the thumb movement distance value, and the thumb movement direction. The relative distance value between the index finger and the thumb is determined according to the final coordinate position of the index finger and the final coordinate position of the thumb.

[0082] It can be seen that in this example, a coordinate system of the robot is established, and the relative distance value between each two fingers used to constitute the relative distance information is determined according to the final coordinate position of each finger joint of the robot in the coordinate system, thereby improving the accuracy and credibility of the obtained relative distance information, thereby improving the accuracy of data processing.

[0083] In a possible example, determining the deformation degree value according to the second training action, the relative distance information and the target part includes: obtaining a first distance value, a second distance value, a third distance value and a fourth distance value in the relative distance information, the first distance value representing the distance value between the index finger and the thumb, the second distance value representing the distance value between the middle finger and the thumb, the third distance value representing the distance value between the ring finger and the thumb, and the fourth distance value representing the distance value between the little finger and the thumb; calculating a first average value among the first distance value, the second distance value, the third distance value and the fourth distance value; obtaining a preset deformation data set corresponding to the second training action and the target part, the deformation data set including a correspondence between different reference deformation degree values ​​and different reference average values; setting a reference deformation degree value corresponding to a reference average value in the deformation data set whose difference with the first average value is less than a preset difference as the deformation degree value.

[0084] In a specific example, the first distance value, the second distance value, the third distance value, and the fourth distance value in the relative distance information are obtained. Then, the first average value among the first distance value, the second distance value, the third distance value, and the fourth distance value is calculated, and a reference deformation degree value corresponding to a reference average value whose difference with the first average value is less than a preset difference is obtained from a preset deformation data set, and then set as the deformation degree value. Among them, the deformation data set is a preset data set corresponding to the use of the second training action to process the target part, and the deformation data set includes the correspondence between different reference deformation degree values ​​and different reference average values.

[0085] It can be seen that in this example, the first average value between the fingers of the manipulator is determined according to the relative distance information, and then the deformation degree value is determined, thereby improving the rationality of the determined deformation degree value.

[0086] In a possible example, after controlling the display device to display the image update information, the method further includes: obtaining an updated image of the target part and a preset optimal image of the target part in a first interface of the display device, the first interface being an interface generated according to the image obtained after the image update information is updated; if it is detected that the similarity between the updated image and the optimal image of the target part is lower than a preset similarity, obtaining a recommended training action corresponding to the target part; generating a training action prompt message according to the recommended training action, the training action prompt message being used to instruct the display device to generate a prompt interface for prompting the user to use the recommended training action for training; and sending the training action prompt message to the display device so that the display device generates the prompt interface.

[0087] In a specific example, the target part in the first image is updated according to the image update information, and the image corresponding to the target training object after the target part is updated is obtained. The first interface of the display device is generated according to the image obtained after the image update information is updated. The updated image of the target part in the first interface and the preset optimal image of the target part are obtained. If the similarity between the updated image and the optimal image of the target part is lower than the preset similarity, that is, the image of the target part after the update does not reach the preset optimal image. Then, the recommended training action corresponding to the target part is obtained, and then a training action prompt message is generated according to the recommended training action, and finally the training action prompt message is sent to the display device, thereby prompting the user to use the recommended training action for training, thereby improving the intelligence of the hand function training system 1, and thus improving the user experience.

[0088] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to realize the above functions, the electronic device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0089] The embodiment of the present application can divide the electronic device into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0090] In the case of dividing each functional module according to each function, the following is combined Fig.11 The device for processing the virtual training object in the hand function training system in the embodiment of the present application is described in detail. Fig.11 A functional unit composition block diagram of a device for processing a virtual training object in a hand function training system provided in an embodiment of the present application, a device for processing a virtual training object in a hand function training system, a computing device applied to the hand function training system, the hand function training system comprising the computing device, a display device and a manipulator, the computing device being connected to the display device and the manipulator respectively, the device comprising:

[0091] A first determining unit 601 is used to determine first image information of the target training object according to the historical training information of the user and the initial image information of the target training object selected by the user, wherein the initial image information refers to an image used to generate a preset initial state of the target training object, and the first image information refers to an image used to update the initial state of the target training object;

[0092] A first control unit 602, configured to control the display device to display the first image information;

[0093] A detection unit 603 is used to detect a selection operation of the user on a target part in the first image;

[0094] A second control unit 604 is used to control the manipulator to collect action information of the action performed by the user's hand in response to the selection operation, wherein the action information includes joint angle information, joint movement angle information and joint rotation direction information of each joint of the manipulator when the user's hand performs the action;

[0095] A second determining unit 605 is used to determine image update information for updating the image of the target part according to the first image information and the action information;

[0096] The third control unit 606 is used to control the display device to display the image update information.

[0097] In a possible example, the historical training information includes a first training action performed by the user and a first maximum joint movement angle corresponding to the first training action, and the first determination unit 601 is further used to obtain the first training action in the historical training information and the first maximum joint movement angle corresponding to the first training action; and if a first target training action identical to the first training action is found from a preset training action set, then the second maximum joint movement angle corresponding to the first target training action is obtained from the training action set, the training action set includes a plurality of preset reference training actions corresponding to the target training object, and the maximum joint movement angle required for each reference training action to process the target training object, and the first target training action is a training action within the plurality of reference training actions; and if the second maximum joint movement angle is greater than the first maximum joint movement angle, then the first image information of the target training object is determined according to the second maximum joint movement angle, the first training action and the initial image information.

[0098] In a possible example, the first determination unit 601 is also used to determine, if the second maximum joint movement angle is greater than the first maximum joint movement angle, an initial update part in the target training object according to the second maximum joint movement angle and the first training action, the initial update part representing a part in the target training object that needs to be shaped using the first training action with the second maximum joint movement angle; and to obtain optimal image information of the preset initial update part; and to determine the first image information of the target training object according to the optimal image information of the initial update part and the initial image information.

[0099] In a possible example, the second determination unit 605 is also used to obtain the joint angle information, the joint movement angle information and the joint rotation direction information in the action information; and determine the second training action corresponding to the action performed by the user's hand according to the joint angle information; and determine the relative distance information between each finger of the robot according to the joint angle information, the joint movement angle information and the joint rotation direction information, and the relative distance information includes the relative distance value between each two fingers of the user; and determine the deformation degree value according to the second training action, the relative distance information and the target part; and obtain the deformation area corresponding to the target part when the target part is shaped using the second training action; and obtain the target image information of the target part in the first image information; and determine the image update information according to the deformation area, the deformation degree value and the target image information.

[0100] In a possible example, the second determination unit 605 is also used to obtain the initial angle value of each finger joint in the joint angle information when the user's hand starts to perform an action; and determine the initial distance value between each finger joint of the manipulator and the relative position between each finger joint according to the initial angle value of each finger joint; and establish a coordinate system according to the initial distance value between each finger joint and the relative position between each finger joint; and obtain the joint movement angle of each finger joint of the manipulator in the joint movement angle information and the joint rotation direction of each finger joint of the manipulator in the joint rotation direction information; and determine the relative distance information according to the coordinate system, the joint movement angle of each finger joint and the joint rotation direction of each finger joint.

[0101] In a possible example, the second determination unit 605 is also used to obtain a first distance value, a second distance value, a third distance value and a fourth distance value in the relative distance information, wherein the first distance value represents the distance value between the index finger and the thumb, the second distance value represents the distance value between the middle finger and the thumb, the third distance value represents the distance value between the ring finger and the thumb, and the fourth distance value represents the distance value between the little finger and the thumb; and calculate a first average value among the first distance value, the second distance value, the third distance value and the fourth distance value; and obtain a preset deformation data set corresponding to the second training action and the target part, the deformation data set including a correspondence between different reference deformation degree values ​​and different reference average values; and set a reference deformation degree value corresponding to a reference average value in the deformation data set whose difference with the first average value is less than a preset difference as the deformation degree value.

[0102] In a possible example, the apparatus further includes an acquisition unit, the acquisition unit being used to acquire an updated image of the target part and a preset optimal image of the target part in a first interface of the display device, the first interface being an interface generated according to an image obtained after updating the image update information; and if it is detected that the similarity between the updated image and the optimal image of the target part is lower than a preset similarity, acquiring a recommended training action corresponding to the target part;

[0103] And generating a training action prompt message according to the recommended training action, wherein the training action prompt message is used to instruct the display device to generate a prompt interface for prompting the user to use the recommended training action for training; and sending the training action prompt message to the display device so that the display device generates the prompt interface.

[0104] Please combine the following Fig.12 The computing device in the embodiment of the present application is introduced as follows:

[0105] See also Fig.12 , Fig.12 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. Fig.12 As shown, the computing device includes a processor 701, a memory 703, a communication module 702, a program 704, and a Bluetooth module 705. The processor 701 is connected to the memory 703, the communication module 702, and the Bluetooth module 705 through an internal communication bus. It is understood that the number of processors 701 can be set according to actual needs, for example, one, two, or more, and the number of processors 701 is not specifically limited here. The number of programs 704 can be set according to actual needs, for example, one, two, or more, and the number of programs 704 is not specifically limited here.

[0106] The program 704 is stored in the memory 703 and executed by the processor 701. The program 704 includes instructions for executing any step in the following method embodiments.

[0107] Among them, the processor 701 can be, for example, a central processing unit (CPU), a general processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, units and circuits described in conjunction with the disclosure of this application. The processor 701 can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The communication unit can be a communication module 702, a transceiver, a transceiver circuit, etc., and the storage unit can be a memory 703.

[0108] The memory 703 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Among them, the nonvolatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM).

[0109] The computing device may perform data interaction with the robot through the communication module 702 or the Bluetooth module 705, and the specific details are not limited here.

[0110] It is understandable that the display device and the computing device may be provided separately, or the display device may be integrated into the computing device for an integrated provision, and no specific limitation is made here.

[0111] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0112] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0113] An embodiment of the present application further provides a computer program product, which includes a computer program. The computer program can be operated to enable a computer to execute part or all of the steps of any method recorded in the above method embodiments.

[0114] The computer program product may be a software installation package, and the computer includes an electronic device.

[0115] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0116] In the several embodiments provided in the present application, it should be understood that the disclosed methods, devices and systems can be implemented in other ways. For example, the device embodiments described above are merely schematic; for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0117] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0118] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may be physically included separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0119] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store program codes.

[0120] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions without departing from the spirit and scope of the present invention, and can make various changes and modifications, including the combination of the above-mentioned different functions and implementation steps, including software and hardware implementation methods, all of which are within the scope of protection of the present invention.

Claims

1. A method for processing a virtual training object in a hand function training system, characterized in that: A computing device applied to a hand function training system, the hand function training system comprising the computing device, a display device and a manipulator, the computing device being connected to the display device and the manipulator respectively, the method comprising: Determining first image information of the target training object according to historical training information of the user and initial image information of the target training object selected by the user includes: Acquire a first training action performed by the user in the historical training information and a first maximum joint movement angle corresponding to the first training action; If a first target training action that is the same as the first training action is found from a preset training action set, a second maximum joint movement angle corresponding to the first target training action is obtained from the training action set, wherein the training action set includes a plurality of preset reference training actions corresponding to the target training object, and a maximum joint movement angle required for each reference training action to process the target training object, and the first target training action is a training action within the plurality of reference training actions; If the second maximum joint movement angle is greater than the first maximum joint movement angle, determining an initial update part in the target training object according to the second maximum joint movement angle and the first training action, the initial update part representing a part in the target training object that needs to be shaped using the first training action at the second maximum joint movement angle; Acquiring the preset optimal image information of the initial update part; Determining first image information of the target training object according to the optimal image information of the initial update part and the initial image information, wherein the initial image information refers to an image used to generate a preset initial state of the target training object, and the first image information refers to an image used to update the initial state of the target training object; Controlling the display device to display the first image information; detecting a selection operation by the user on a target part in the first image; In response to the selection operation, the manipulator is controlled to collect action information of the action performed by the user's hand, wherein the action information includes joint angle information, joint movement angle information, and joint rotation direction information of each joint of the manipulator when the user's hand performs the action; Determining image update information for updating the image of the target part according to the first image information and the action information; The display device is controlled to display the image update information.

2. The method according to claim 1, characterized in that The determining, according to the first image information and the action information, image update information for updating the image of the target part comprises: Acquire the joint angle information, the joint movement angle information and the joint rotation direction information in the action information; Determine a second training action corresponding to the action performed by the user's hand according to the joint angle information; Determine relative distance information between each finger of the manipulator according to the joint angle information, the joint movement angle information and the joint rotation direction information, wherein the relative distance information includes a relative distance value between each two fingers of the user; determining a deformation degree value according to the second training action, the relative distance information and the target part; Obtaining a deformation area corresponding to the target part when the target part is shaped using the second training action; Acquire target image information of the target part in the first image information; The image update information is determined according to the deformation area, the deformation degree value and the target image information.

3. The method according to claim 2, characterized in that The determining the relative distance information between the fingers of the manipulator according to the joint angle information, the joint movement angle information and the joint rotation direction information comprises: Obtaining the initial angle value of each finger joint in the joint angle information when the user's hand starts to perform an action; Determining the initial distance values ​​between the finger joints of the manipulator and the relative positions between the finger joints according to the initial angle values ​​of the finger joints; Establishing a coordinate system according to the initial distance values ​​between the finger joints and the relative positions between the finger joints; Acquire the joint movement angle of each finger joint of the manipulator in the joint movement angle information and the joint rotation direction of each finger joint of the manipulator in the joint rotation direction information; The relative distance information is determined according to the coordinate system, the joint movement angles of the respective finger joints, and the joint rotation directions of the respective finger joints.

4. The method according to claim 2, characterized in that: The step of determining the deformation degree value according to the second training action, the relative distance information and the target part includes: Obtaining a first distance value, a second distance value, a third distance value, and a fourth distance value in the relative distance information, wherein the first distance value represents the distance value between the index finger and the thumb, the second distance value represents the distance value between the middle finger and the thumb, the third distance value represents the distance value between the ring finger and the thumb, and the fourth distance value represents the distance value between the little finger and the thumb; Calculating a first average value among the first distance value, the second distance value, the third distance value and the fourth distance value; Acquire a preset deformation data set corresponding to the second training action and the target part, wherein the deformation data set includes a correspondence between different reference deformation degree values ​​and different reference average values; A reference deformation degree value corresponding to a reference average value in the deformation data set whose difference with the first average value is less than a preset difference value is set as the deformation degree value.

5. The method according to claim 1, characterized in that After controlling the display device to display the image update information, the method further includes: Acquire an updated image of the target part and a preset optimal image of the target part in a first interface of the display device, wherein the first interface is an interface generated according to an image obtained after updating the image update information; If it is detected that the similarity between the updated image and the optimal image of the target part is lower than a preset similarity, obtaining a recommended training action corresponding to the target part; generating a training action prompt message according to the suggested training action, wherein the training action prompt message is used to instruct the display device to generate a prompt interface for prompting the user to perform training using the suggested training action; The training action prompt message is sent to the display device so that the display device generates the prompt interface.

6. A device for processing virtual training objects in a hand function training system, characterized in that: A computing device applied to a hand function training system, the hand function training system comprising the computing device, a display device and a manipulator, the computing device being connected to the display device and the manipulator respectively, the device comprising: A first determining unit, configured to determine first image information of the target training object according to historical training information of the user and initial image information of the target training object selected by the user; Acquire a first training action performed by the user in the historical training information and a first maximum joint movement angle corresponding to the first training action; If a first target training action that is the same as the first training action is found from a preset training action set, a second maximum joint movement angle corresponding to the first target training action is obtained from the training action set, wherein the training action set includes a plurality of preset reference training actions corresponding to the target training object, and a maximum joint movement angle required for each reference training action to process the target training object, and the first target training action is a training action within the plurality of reference training actions; If the second maximum joint movement angle is greater than the first maximum joint movement angle, determining an initial update part in the target training object according to the second maximum joint movement angle and the first training action, the initial update part representing a part in the target training object that needs to be shaped using the first training action at the second maximum joint movement angle; Acquiring the preset optimal image information of the initial update part; Determining first image information of the target training object according to the optimal image information of the initial update part and the initial image information, wherein the initial image information refers to an image used to generate a preset initial state of the target training object, and the first image information refers to an image used to update the initial state of the target training object; A first control unit, used for controlling the display device to display the first image information; A detection unit, configured to detect a selection operation of the user on a target part in the first image; a second control unit, configured to respond to the selection operation and control the manipulator to collect motion information of the motion performed by the user's hand, wherein the motion information includes joint angle information, joint movement angle information, and joint rotation direction information of each joint of the manipulator when the user's hand performs the motion; a second determining unit, configured to determine image update information for updating the image of the target part according to the first image information and the action information; A third control unit is used to control the display device to display the image update information.

7. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and when the processor executes the computer instructions, the electronic device executes the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor is caused to execute the method according to any one of claims 1 to 5.

Images