VR fall training data processing method and system based on virtual hospital

Abstract

The present application provides a method and system for processing VR fall training data based on a virtual hospital. The method obtains the first identity information of the first user through the first VR device in the VR device cluster, and sends the first identity information to the management server, so that the management server determines the fall prevention training task according to the first identity information, and then determines the virtual element model set according to the fall prevention training task, and generates the virtual scene to be trained according to the basic virtual scene and the virtual element model set, and then sends the scene model data corresponding to the virtual scene to be trained to the first VR device, so as to display the virtual scene to be trained in the first VR device, thereby training the first user in the fall prevention training task, effectively avoiding the risks brought by real falls, and effectively ensuring the safety of training.

Description

Technical Field

[0001] The present application relates to data processing technology, and in particular to a VR fall training data processing method and system based on a virtual hospital. Background Art

[0002] With the aging of the population and the continuous development of the medical and health field, the demand for rehabilitation treatment is increasing, especially fall prevention training for the elderly and postoperative patients has become particularly important.

[0003] Traditional fall prevention training often relies on physical facilities in the real world, such as balance beams, obstacles, etc. This approach has certain limitations, including space constraints, high costs, inability to precisely control the training environment, and potential safety risks. Summary of the invention

[0004] The present application provides a VR fall training data processing method and system based on a virtual hospital, which is used to solve the limitations of traditional fall prevention training.

[0005] In a first aspect, the present application provides a method for processing VR fall training data based on a virtual hospital, which is applied to a virtual hospital rehabilitation management system, wherein the virtual hospital rehabilitation management system comprises: a VR device cluster and a management server, wherein each VR device in the VR device cluster is respectively connected to the management server in communication; the method comprises:

[0006] Acquire first identity information of a first user through a first VR device in the VR device cluster, and send the first identity information to the management server;

[0007] The management server determines a fall prevention task to be trained according to the first identity information;

[0008] The management server determines a virtual element model set according to the fall prevention task to be trained, wherein the virtual element model set includes virtual element models required to construct a virtual scene to be trained on a basic virtual scene, and the virtual scene to be trained is a virtual scene corresponding to the fall prevention task to be trained;

[0009] The management server generates the virtual scene to be trained according to the basic virtual scene and the virtual element model set, and sends the scene model data corresponding to the virtual scene to be trained to the first VR device;

[0010] The first VR device displays the virtual scene to be trained so as to train the first user on the fall prevention task to be trained.

[0011] In the above scheme, the first identity information of the first user is obtained through the first VR device in the VR device cluster, and the first identity information is sent to the management server, so that the management server determines the fall prevention training task according to the first identity information, and then determines the virtual element model set according to the fall prevention training task, and generates the virtual scene to be trained according to the basic virtual scene and the virtual element model set, and then sends the scene model data corresponding to the virtual scene to be trained to the first VR device, so as to display the virtual scene to be trained in the first VR device, thereby training the first user in the fall prevention training task, effectively avoiding the risk brought by the real fall, and effectively ensuring the safety of the training, and determining the training task according to the user identity information, and then determining the virtual element model set based on the determined training task, dynamically generating the virtual scene to be trained that meets the training goal, and enhancing the diversity and pertinence of the training. In addition, after the generation of the fall prevention training tasks for different users is completed in the management server, they are sent to the corresponding VR devices, so as to ensure that the fall prevention training tasks generated for each user are pertinent, and also avoid storing too much task data in the VR device, thereby causing the problem of overloading the VR device.

[0012] Optionally, the virtual hospital rehabilitation management system further includes: a monitoring terminal, the monitoring terminal is communicatively connected to the management server, and each VR device in the VR device cluster is respectively connected to the monitoring terminal; before the management server determines the fall prevention training task according to the first identity information, it also includes:

[0013] The monitoring terminal obtains first physiological indicator data of the first user, and sends the first physiological indicator data to the management server, where the physiological indicator data includes the first identity information, first age information, first body mass index information, first muscle indicator information, first balance indicator information, and first fall record information;

[0014] Correspondingly, the management server determines the fall prevention training task to be performed according to the first identity information, including:

[0015] The management server obtains the first physiological indicator data according to the first identity information;

[0016] The management server determines the fall prevention training level to be trained according to the first age information, the first body mass index information, the first muscle index information, the first balance index information, and the first fall record information, wherein the first fall record information includes first fall number information;

[0017] The management server determines the fall prevention task to be trained based on the fall prevention training difficulty level and the first fall record information, the fall prevention task to be trained includes a fall prevention sub-task sequence to be trained composed of multiple fall prevention sub-tasks to be trained, the basic virtual scenes corresponding to each fall prevention sub-task to be trained in the fall prevention sub-task sequence to be trained are the same virtual scene, and the first fall record information includes first fall scene type information.

[0018] In the above scheme, the monitoring terminal collects and sends physiological index data to the management server, so as to determine the training level and task according to the physiological index data. The training is further personalized, and the training difficulty is adjusted based on the actual health status of the user, thereby improving the scientificity and safety of the training, and the training plan is adjusted in time through the monitoring data to avoid excessive load.

[0019] Optionally, the management server determines a set of virtual element models according to the fall prevention task to be trained, including:

[0020] The management server determines a virtual element sequence according to each fall prevention subtask to be trained in the fall prevention subtask to be trained sequence, wherein the virtual elements in the virtual element sequence are used to construct a virtual scene to be trained corresponding to the fall prevention subtask to be trained on the basis of the basic virtual scene;

[0021] The management server generates the virtual element model set according to the virtual element sequences determined by each fall prevention subtask to be trained.

[0022] In the above scheme, the virtual element sequence is determined according to the training subtasks, and a virtual element model set is generated. The scene construction is further refined to ensure that each training subtask has targeted virtual element support. This will increase the diversity and complexity of the training scenes and simulate a real fall risk environment.

[0023] Optionally, the management server generates the to-be-trained virtual scene according to the basic virtual scene and the virtual element model set, including:

[0024] The management server loads the basic virtual scene through a preset VR processing engine, and reads each virtual element model in the virtual element model set;

[0025] The management server adds each virtual element model in the virtual element model set to the basic virtual scene to generate the scene model data corresponding to the virtual scene to be trained.

[0026] In the above solution, the basic scene and virtual element model are loaded through the VR processing engine to generate scene model data. Efficient scene generation is achieved to ensure the instant response and high-quality rendering of the virtual environment. This promotes immersive experience, enhances user participation and training effects.

[0027] Optionally, the management server adds each virtual element model in the virtual element model set to the basic virtual scene to generate the scene model data corresponding to the virtual scene to be trained, including:

[0028] The management server obtains a reference element model set in the basic virtual scene , and according to the reference element model set Determine the corresponding reference element position set ,in, The reference element model set The A reference element model, for The corresponding position;

[0029] The management server obtains the preset fall prevention training original path in the basic virtual scene , the preset fall prevention training original path For a reference element model set The original path defined, the preset fall prevention training original path The reference element position set There is no position interference relationship;

[0030] The management server determines the fall prevention training level to be trained and the preset fall prevention training original path. Determine the setting position of each virtual element model in the virtual element model set to generate an original path to be trained for fall prevention , wherein the scenario model data includes the original path to be trained for fall prevention .

[0031] In the above scheme, by determining the setting position of the virtual element model in the basic scene, the virtual element layout is accurately controlled, the training path is optimized, and the training efficiency and safety are improved.

[0032] Optionally, the management server determines the fall prevention training level to be trained and the preset fall prevention training original path. Determining the setting position of each virtual element model in the virtual element model set includes:

[0033] The management server obtains the preset fall prevention training original path The first route boundary and the second route boundary ;

[0034] The management server obtains model size information of a target virtual element model in the virtual element model set;

[0035] The management server determines the fall prevention training level to be trained, the first route boundary , Second route boundary And the model size information determines the target setting position corresponding to the target virtual element model.

[0036] In the above scheme, the first route boundary, the second route boundary and the model size information are used to determine the target setting position of the virtual element model, improve the efficiency and accuracy of scene construction, ensure the dynamic adaptability of the training scene, and intelligently layout according to the training level and model size.

[0037] Optionally, after the first VR device displays the virtual scene to be trained so as to train the first user on the fall prevention training task to be trained, the method further includes:

[0038] The first VR device generates training video data corresponding to the fall prevention training task, and sends the training video data to the monitoring terminal so that the training video data is displayed on the monitoring terminal.

[0039] In the above scheme, the first VR device generates training video data and sends it to the monitoring terminal, which can monitor the training process and provide visual feedback to facilitate coaches or doctors to evaluate the training effect. In addition, it can further support remote guidance and monitoring to enhance the flexibility and accessibility of training.

[0040] In a second aspect, the present application provides a virtual hospital rehabilitation management system, comprising: a VR device cluster and a management server, wherein each VR device in the VR device cluster is respectively connected to the management server for communication;

[0041] Acquire first identity information of a first user through a first VR device in the VR device cluster, and send the first identity information to the management server;

[0042] The management server determines a fall prevention task to be trained according to the first identity information;

[0043] The management server determines a virtual element model set according to the fall prevention task to be trained, wherein the virtual element model set includes virtual element models required to construct a virtual scene to be trained on a basic virtual scene, and the virtual scene to be trained is a virtual scene corresponding to the fall prevention task to be trained;

[0044] The management server generates the virtual scene to be trained according to the basic virtual scene and the virtual element model set, and sends the scene model data corresponding to the virtual scene to be trained to the first VR device;

[0045] The first VR device displays the virtual scene to be trained so as to train the first user on the fall prevention task to be trained.

[0046] Optionally, the virtual hospital rehabilitation management system further includes: a monitoring terminal, the monitoring terminal is communicatively connected to the management server, and each VR device in the VR device cluster is respectively connected to the monitoring terminal; before the management server determines the fall prevention training task according to the first identity information, it also includes:

[0047] The monitoring terminal obtains first physiological indicator data of the first user, and sends the first physiological indicator data to the management server, where the physiological indicator data includes the first identity information, first age information, first body mass index information, first muscle indicator information, first balance indicator information, and first fall record information;

[0048] Correspondingly, the management server determines the fall prevention training task to be performed according to the first identity information, including:

[0049] The management server obtains the first physiological indicator data according to the first identity information;

[0050] The management server determines the fall prevention training level to be trained according to the first age information, the first body mass index information, the first muscle index information, the first balance index information, and the first fall record information, wherein the first fall record information includes first fall number information;

[0051] The management server determines the fall prevention task to be trained based on the fall prevention training difficulty level and the first fall record information, the fall prevention task to be trained includes a fall prevention sub-task sequence to be trained composed of multiple fall prevention sub-tasks to be trained, the basic virtual scenes corresponding to each fall prevention sub-task to be trained in the fall prevention sub-task sequence to be trained are the same virtual scene, and the first fall record information includes first fall scene type information.

[0052] Optionally, the management server determines a set of virtual element models according to the fall prevention task to be trained, including:

[0053] The management server determines a virtual element sequence according to each fall prevention subtask to be trained in the fall prevention subtask to be trained sequence, wherein the virtual elements in the virtual element sequence are used to construct a virtual scene to be trained corresponding to the fall prevention subtask to be trained on the basis of the basic virtual scene;

[0054] The management server generates the virtual element model set according to the virtual element sequences determined by each fall prevention subtask to be trained.

[0055] Optionally, the management server generates the to-be-trained virtual scene according to the basic virtual scene and the virtual element model set, including:

[0056] The management server loads the basic virtual scene through a preset VR processing engine, and reads each virtual element model in the virtual element model set;

[0057] The management server adds each virtual element model in the virtual element model set to the basic virtual scene to generate the scene model data corresponding to the virtual scene to be trained.

[0058] Optionally, the management server adds each virtual element model in the virtual element model set to the basic virtual scene to generate the scene model data corresponding to the virtual scene to be trained, including:

[0059] The management server obtains a reference element model set in the basic virtual scene , and according to the reference element model set Determine the corresponding reference element position set ,in, The reference element model set The A reference element model, for The corresponding position;

[0060] The management server obtains the preset fall prevention training original path in the basic virtual scene , the preset fall prevention training original path For a reference element model set The original path defined, the preset fall prevention training original path The reference element position set There is no position interference relationship;

[0061] The management server determines the fall prevention training level to be trained and the preset fall prevention training original path. Determine the setting position of each virtual element model in the virtual element model set to generate an original path to be trained for fall prevention , wherein the scenario model data includes the original path to be trained for fall prevention .

[0062] Optionally, the management server determines the fall prevention training level to be trained and the preset fall prevention training original path. Determining the setting position of each virtual element model in the virtual element model set includes:

[0063] The management server obtains the preset fall prevention training original path The first route boundary and the second route boundary ;

[0064] The management server obtains model size information of a target virtual element model in the virtual element model set;

[0065] The management server determines the fall prevention training level to be trained, the first route boundary , Second route boundary And the model size information determines the target setting position corresponding to the target virtual element model.

[0066] Optionally, the management server determines the fall prevention training level , the first route boundary , Second route boundary and determining a target setting position corresponding to the target virtual element model based on the model size information, including:

[0067] The management server uses formula 1 and calculates the fall prevention training level according to the fall prevention training level. , the first route boundary The second route boundary And the model size information determines the target setting position , the formula 1 is:

[0068]

[0069] in, To set the minimum fall prevention training level, To preset the highest fall prevention training level, is the length dimension of the target virtual element model, is the width size of the target virtual element model, The first route boundary The position points of each boundary feature point in the preset coordinate system, The first route boundary The second route borders The distance between The preset minimum passing distance.

[0070] Optionally, after the first VR device displays the virtual scene to be trained so as to train the first user on the fall prevention training task to be trained, the method further includes:

[0071] The first VR device generates training video data corresponding to the fall prevention training task, and sends the training video data to the monitoring terminal so that the training video data is displayed on the monitoring terminal.

[0072] In a third aspect, the present application provides an electronic device, including:

[0073] processor; and,

[0074] A memory, configured to store executable instructions of the processor;

[0075] The processor is configured to perform any possible method described in the first aspect by executing the executable instructions.

[0076] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement any possible method described in the first aspect.

[0077] The VR fall training data processing method and system based on a virtual hospital provided in the present application obtains the first identity information of the first user through the first VR device in the VR device cluster, and sends the first identity information to the management server, so that the management server determines the fall prevention training task to be trained according to the first identity information, and then determines the virtual element model set according to the fall prevention training task to be trained, and generates the virtual scene to be trained according to the basic virtual scene and the virtual element model set, and then sends the scene model data corresponding to the virtual scene to be trained to the first VR device to display the virtual scene to be trained in the first VR device, so as to train the first user in the fall prevention training task to be trained, effectively avoiding the risks brought by real falls, and effectively ensuring the safety of training, and, in addition, determines the training task according to the user identity information, and then determines the virtual element model set based on the determined training task, and dynamically generates the virtual scene to be trained that meets the training objectives, thereby enhancing the diversity and pertinence of the training. In addition, after the fall prevention training tasks for different users are generated in the management server, they are sent to the corresponding VR devices. This not only ensures that the fall prevention training tasks generated for each user are targeted, but also avoids storing too much task data in the VR device, which in turn causes the problem of overloading the VR device. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0079] Figure 1 is a flowchart of a method for processing VR fall training data based on a virtual hospital according to an example embodiment of the present application;

[0080] Figure 2 is a flowchart of a VR fall training data processing method based on a virtual hospital according to another exemplary embodiment of the present application;

[0081] Figure 3 is a structural diagram of a virtual hospital rehabilitation management system according to an exemplary embodiment of the present application;

[0082] Figure 4 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application.

[0083] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0084] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0085] In order to solve the above problems, the embodiments provided in this application are

[0086] Figure 1 FIG. 1 is a flow chart of a method for processing VR fall training data based on a virtual hospital according to an exemplary embodiment of the present application. Figure 1 As shown, the method provided in this embodiment includes:

[0087] S101. Obtain first identity information of a first user through a first VR device in a VR device cluster.

[0088] In this step, the first identity information of the first user is obtained through the first VR device in the VR device cluster, and the first identity information is sent to the management server.

[0089] Specifically, when the user is ready to start fall prevention training, the first VR device first asks the user to provide identity information, which may include but is not limited to the user's name, age, gender, historical health records, etc. This information can be obtained through the user's registered account, biometrics, or direct input. Once the information is obtained, the first VR device can encrypt it and send it to the management server through a secure network connection.

[0090] S102. The management server determines a fall prevention task to be trained based on the first identity information.

[0091] Specifically, after receiving the identity information of the first user from the first VR device, the management server will query the user profile in its database to obtain the health and fall risk assessment data related to the user. Based on this information, the server uses a specific algorithm (such as a rule-based system, a machine learning model, etc.) to determine the fall prevention training tasks that are most suitable for the user. These tasks may include fall prevention training tasks in ward scenarios, bathroom scenarios, and corridor scenarios.

[0092] In a possible implementation, the above-mentioned virtual hospital rehabilitation management system may further include: a monitoring terminal. The monitoring terminal is connected to the management server in communication, and each VR device in the VR device cluster is connected to the monitoring terminal. It is worth noting that the above-mentioned monitoring terminal may be a medical monitoring terminal, through which the detection data of the user's relevant physical examination in the hospital can be obtained.

[0093] Specifically, before the management server determines the fall prevention training task based on the first identity information, the monitoring terminal obtains the first physiological indicator data of the first user and sends the first physiological indicator data to the management server. The physiological indicator data includes the first identity information, the first age information, the first body mass index information, the first muscle indicator information, the first balance indicator information and the first fall record information.

[0094] Correspondingly, for the management server to determine the fall prevention training task based on the first identity information, the management server may obtain the first physiological indicator data based on the first identity information. The management server determines the fall prevention training level based on the first age information, the first body mass index information, the first muscle indicator information, the first balance indicator information and the first fall record information, and the first fall record information includes the first number of falls information. The management server determines the fall prevention training task based on the fall prevention training difficulty level and the first fall record information, and the fall prevention training task includes a fall prevention training subtask sequence composed of multiple fall prevention training subtasks, and the basic virtual scene corresponding to each fall prevention training subtask in the fall prevention training subtask sequence is the same virtual scene, and the first fall record information includes the first fall scene type information.

[0095] Specifically, before the training process begins, the monitoring terminal is responsible for collecting the physiological index data of the first user. Such data includes but is not limited to:

[0096] First identity information: such as user name, ID or unique identifier, used to identify the user.

[0097] First, age information: the user’s age, because age is an important factor affecting the risk of falling.

[0098] First, body mass index information: calculated by the ratio of weight to height, it reflects the user's obesity level and may affect balance and flexibility.

[0099] First muscle index information: muscle strength and endurance data, which is crucial for preventing falls.

[0100] First balance index information: the user's balance ability measurement value when standing and walking.

[0101] The first fall record information: includes the first fall frequency information and the first fall scene type information, that is, the frequency of the user's past falls and the circumstances under which the user is prone to fall.

[0102] The above data can be obtained through wearable devices, sensor arrays or other medical monitoring devices, which can collect the above data in real time or periodically and send it to the management server via wired or wireless means.

[0103] S103: The management server determines a set of virtual element models according to the fall prevention task to be trained.

[0104] The management server determines a virtual element model set based on the fall prevention task to be trained. The virtual element model set includes virtual element models required to construct a virtual scene to be trained on the basis of a basic virtual scene. The virtual scene to be trained is a virtual scene corresponding to the fall prevention task to be trained.

[0105] Further, the management server determines a virtual element sequence according to each fall prevention subtask to be trained in the fall prevention subtask sequence, and the virtual elements in the virtual element sequence are used to construct a virtual scene to be trained corresponding to the fall prevention subtask to be trained on the basis of the basic virtual scene. The management server generates a virtual element model set according to the virtual element sequence determined by each fall prevention subtask to be trained.

[0106] After receiving the first physiological indicator data, the management server analyzes the user's age, body mass index, muscle and balance indicators, and fall records to determine the initial difficulty level of the training. Based on the training level and the scene type in the fall record, a series of training subtasks are selected or generated to form a training subtask sequence. Each subtask is designed to address specific fall risk points, such as balance exercises or quick response training on different terrains. For each fall prevention subtask to be trained, the management server determines a set of virtual elements, such as models of beds, tables, chairs, etc., or specific parameter settings for the model, such as floor material, obstacle locations, lighting conditions, etc., to simulate real-world fall risk scenarios.

[0107] Based on the virtual element sequence, a set of virtual element models is generated or selected, and these models will be integrated into the basic virtual scene to construct the virtual scene to be trained. The management server uses the preset VR processing engine to load the basic virtual scene, read and add the virtual element model, and generate data for the virtual scene to be trained. The generated scene model data is sent to the first VR device, so that the user can perform fall prevention training in a highly customized environment. The entire system can provide each user with a personalized and targeted training experience, thereby effectively improving the effectiveness of fall prevention training. In the specific implementation, data privacy and security should also be considered to ensure that all user data is properly protected.

[0108] Optionally, the management server needs to determine a virtual element sequence for each subtask in the fall prevention training subtask sequence. These virtual elements are used to construct a virtual scene to be trained that meets the training objectives on the basis of the basic virtual scene. The virtual elements may include but are not limited to:

[0109] Floor materials: such as smooth tiles, carpets, grass, etc., used to simulate different friction coefficients and stability.

[0110] Obstacles: such as chairs, tables, stairs, thresholds, etc., are used to train users' balance and coordination abilities in complex environments.

[0111] Ambient lighting: natural light, indoor lighting, dim light, etc., to test the user's visual adaptability and reaction speed under different lighting conditions.

[0112] Sound effects: background noise, warning signals, etc., to increase the realism of the environment and the challenge of training.

[0113] Time pressure: Setting a time limit to complete a task improves the user's ability to cope with tight situations.

[0114] For each fall prevention subtask to be trained, the management server will generate a corresponding set of virtual element models based on the virtual element sequence determined above. If the virtual element model already exists in the database, it will be directly retrieved and used; if it does not exist, the required model will be automatically generated using 3D modeling software or algorithms. Then, the physical properties of the model, such as weight, hardness, friction coefficient, etc., are adjusted to meet the requirements of the specific training subtask. All virtual element models are integrated together to form a complete model set, ready for the construction of virtual scenes.

[0115] After determining the virtual element model set, the management server loads a basic virtual scene, such as an empty room, an outdoor park, or a corridor, through a preset VR processing engine. The model is read from the virtual element model set and added to the basic virtual scene to construct the virtual scene to be trained.

[0116] S104: The management server generates a virtual scene to be trained according to the basic virtual scene and the virtual element model set.

[0117] The management server generates a virtual scene to be trained according to the basic virtual scene and the virtual element model set, and sends the scene model data corresponding to the virtual scene to be trained to the first VR device.

[0118] Specifically, the management server uses a preset VR processing engine to load the basic virtual scene, which may be a blank room or any pre-set environment. Then, the server reads the model in the previously determined virtual element model set and adds it to the basic scene to form the final virtual scene to be trained. The VR processing engine in this step can be implemented using the functions of a game development engine (such as Unity or Unreal Engine). After completing the construction of the virtual scene to be trained, the management server packages the scene model data and sends it back to the first VR device. This may include the scene's geometric information, textures, lighting settings, and other necessary rendering data.

[0119] S105: The first VR device displays the virtual scene to be trained.

[0120] The first VR device displays the virtual scene to be trained to train the first user on the task to be trained for fall prevention. Specifically, after receiving the scene model data, the first VR device uses its built-in VR rendering software or engine to parse and display the virtual scene. The user will see an immersive environment corresponding to the actual training task and perform fall prevention training in it.

[0121] In this embodiment, the first identity information of the first user is obtained through the first VR device in the VR device cluster, and the first identity information is sent to the management server, so that the management server determines the fall prevention training task according to the first identity information, and then determines the virtual element model set according to the fall prevention training task, and generates the virtual scene to be trained according to the basic virtual scene and the virtual element model set, and then sends the scene model data corresponding to the virtual scene to be trained to the first VR device, so as to display the virtual scene to be trained in the first VR device, thereby training the first user in the fall prevention training task, effectively avoiding the risk brought by the real fall, and effectively ensuring the safety of the training, and determining the training task according to the user identity information, and then determining the virtual element model set based on the determined training task, dynamically generating the virtual scene to be trained that meets the training goal, and enhancing the diversity and pertinence of the training. In addition, after the generation of the fall prevention training tasks for different users is completed in the management server, they are sent to the corresponding VR devices, so as to ensure that the fall prevention training tasks generated for each user are pertinent, and also avoid storing too much task data in the VR device, thereby causing the problem of overloading the VR device.

[0122] Figure 2 FIG. 1 is a flow chart of a method for processing VR fall training data based on a virtual hospital according to another exemplary embodiment of the present application. Figure 2 As shown, the VR fall training data processing method based on a virtual hospital provided in this embodiment includes:

[0123] S201. Obtain first identity information of a first user through a first VR device in a VR device cluster.

[0124] In this step, the first identity information of the first user is obtained through the first VR device in the VR device cluster, and the first identity information is sent to the management server.

[0125] Specifically, when the user is ready to start fall prevention training, the first VR device first asks the user to provide identity information, which may include but is not limited to the user's name, age, gender, historical health records, etc. This information can be obtained through the user's registered account, biometrics, or direct input. Once the information is obtained, the first VR device can encrypt it and send it to the management server through a secure network connection.

[0126] S202. The management server determines a fall prevention task to be trained based on the first identity information.

[0127] Specifically, after receiving the identity information of the first user from the first VR device, the management server will query the user profile in its database to obtain the health and fall risk assessment data related to the user. Based on this information, the server uses a specific algorithm (such as a rule-based system, a machine learning model, etc.) to determine the fall prevention training tasks that are most suitable for the user. These tasks may include fall prevention training tasks in ward scenarios, bathroom scenarios, and corridor scenarios.

[0128] In a possible implementation, the above-mentioned virtual hospital rehabilitation management system may further include: a monitoring terminal. The monitoring terminal is connected to the management server in communication, and each VR device in the VR device cluster is connected to the monitoring terminal. It is worth noting that the above-mentioned monitoring terminal may be a medical monitoring terminal, through which the detection data of the user's relevant physical examination in the hospital can be obtained.

[0129] Specifically, before the management server determines the fall prevention training task based on the first identity information, the monitoring terminal obtains the first physiological indicator data of the first user and sends the first physiological indicator data to the management server. The physiological indicator data includes the first identity information, the first age information, the first body mass index information, the first muscle indicator information, the first balance indicator information and the first fall record information.

[0130] Correspondingly, for the management server to determine the fall prevention training task based on the first identity information, the management server may obtain the first physiological indicator data based on the first identity information. The management server determines the fall prevention training level based on the first age information, the first body mass index information, the first muscle indicator information, the first balance indicator information and the first fall record information, and the first fall record information includes the first number of falls information. The management server determines the fall prevention training task based on the fall prevention training difficulty level and the first fall record information, and the fall prevention training task includes a fall prevention training subtask sequence composed of multiple fall prevention training subtasks, and the basic virtual scene corresponding to each fall prevention training subtask in the fall prevention training subtask sequence is the same virtual scene, and the first fall record information includes the first fall scene type information.

[0131] Specifically, before the training process begins, the monitoring terminal is responsible for collecting the physiological index data of the first user. Such data includes but is not limited to:

[0132] First identity information: such as user name, ID or unique identifier, used to identify the user.

[0133] First, age information: the user’s age, because age is an important factor affecting the risk of falling.

[0134] First, body mass index information: calculated by the ratio of weight to height, it reflects the user's obesity level and may affect balance and flexibility.

[0135] First muscle index information: muscle strength and endurance data, which is crucial for preventing falls.

[0136] First balance index information: the user's balance ability measurement value when standing and walking.

[0137] The first fall record information: includes the first fall frequency information and the first fall scene type information, that is, the frequency of the user's past falls and the circumstances under which the user is prone to fall.

[0138] The above data can be obtained through wearable devices, sensor arrays or other medical monitoring devices, which can collect the above data in real time or periodically and send it to the management server via wired or wireless means.

[0139] S203: The management server determines a set of virtual element models according to the fall prevention task to be trained.

[0140] The management server determines a virtual element model set based on the fall prevention task to be trained. The virtual element model set includes virtual element models required to construct a virtual scene to be trained on the basis of a basic virtual scene. The virtual scene to be trained is a virtual scene corresponding to the fall prevention task to be trained.

[0141] Further, the management server determines a virtual element sequence according to each fall prevention subtask to be trained in the fall prevention subtask sequence, and the virtual elements in the virtual element sequence are used to construct a virtual scene to be trained corresponding to the fall prevention subtask to be trained on the basis of the basic virtual scene. The management server generates a virtual element model set according to the virtual element sequence determined by each fall prevention subtask to be trained.

[0142] Furthermore, the management server may load the basic virtual scene through a preset VR processing engine and read each virtual element model in the virtual element model set. The management server adds each virtual element model in the virtual element model set to the basic virtual scene to generate scene model data corresponding to the virtual scene to be trained.

[0143] Specifically, the management server obtains a reference element model set in the basic virtual scene , and based on the reference element model set Determine the corresponding reference element position set ,in, Reference element model collection The A reference element model, for The management server obtains the preset fall prevention training original path in the basic virtual scene , preset original path for fall prevention training For a reference element model set The original path defined by the user, the original path for fall prevention training With reference element position collection There is no position interference relationship. The management server sets the fall prevention training level to be trained and the preset fall prevention training original path. Determine the setting position of each virtual element model in the virtual element model set to generate an original path to be trained for fall prevention , where the scenario model data includes the original path to be trained for fall prevention .

[0144] It is worth noting that the management server obtains a preset reference element model set in the basic virtual scene, and determines the position set of these reference elements in the scene, providing a benchmark for subsequent training path planning and element placement. Then, based on the reference element model set, the management server determines an original training path that is not interfered by the reference element position set as the basic path for training. According to the user's fall prevention training level and the original training path, the management server calculates and determines the setting position of each model in the virtual element model set to build a virtual scene that meets the training objectives. Thus, through the pre-set reference elements and original training paths, the management server can ensure the rationality and safety of the training path, and avoid training interference or safety hazards that may be caused by the random placement of virtual element models. According to the user's specific fall risk level, the setting position of the virtual element model is dynamically adjusted so that the training difficulty matches the user's ability, which can challenge the user without exceeding his ability range, and realize personalized training. Through the determined virtual element placement, the virtual scene can more realistically simulate the complex environment in daily life, such as furniture layout, ground materials, etc., enhance the interactivity and immersion of training, and make the training closer to the actual life scene. Moreover, the reasonable placement of virtual element models can ensure the continuity and smoothness of the training path, avoid unnecessary obstacles or pauses, and thus improve the training efficiency. At the same time, by simulating fall risk scenarios of different difficulty levels, the user's coping ability and balance control skills can be enhanced. In addition, dynamic scene generation based on user data and training level ensures that each training is based on the latest user information and training goals, thereby providing the most optimized training experience and promoting the user's continuous progress and rehabilitation.

[0145] Further, the management server is based on the fall prevention training level to be trained and the preset fall prevention training original path Determine the setting position of each virtual element model in the virtual element model set, which can be a management server to obtain the preset fall prevention training original path The first route boundary and the second route boundary The management server obtains model size information of the target virtual element model in the virtual element model set. The management server obtains model size information of the target virtual element model in the virtual element model set according to the fall prevention training level, the first route boundary, , Second route boundary The target setting position corresponding to the target virtual element model is determined based on the model size information.

[0146] It is worth noting that in the process of constructing the virtual scene to be trained, the management server accurately calculates the setting position of the target virtual element model based on the fall prevention training level to be trained, the first and second route boundaries of the preset fall prevention training original path, and the size information of the target virtual element model. By combining the user's fall training level to be trained with the path boundary information of the virtual scene, the management server can generate a training path that meets both the training goal and the user's ability level. This means that users of different levels will be trained in a tailored environment to ensure the effectiveness and safety of the training. In addition, the size information of the target virtual element model is combined with the training path boundary, so that the virtual scene can be dynamically adjusted according to the difficulty of the training, and obstacles or other virtual elements can be placed, so as to ensure the safety of the user while gradually increasing the complexity of the training to meet the rehabilitation needs of different stages. In addition, by determining the setting position of the target virtual element model, unnecessary obstacles on the training path are avoided, and the smoothness of the training process is ensured. At the same time, a reasonable element layout can also allow users to focus more on the training itself, reduce distractions or troubles caused by improper scene design, and thus improve training efficiency and user experience. In addition, since the calculation of the target setting position is based on the user's training level and the size information of the virtual elements, this means that the system can respond to changes in the user's ability in real time, dynamically adjust the training scene, and ensure that the training content always matches the user's current rehabilitation status. It can be seen that by placing virtual elements on the training path, more realistic life scenes can be created, such as crowded corridors, uneven ground, etc., thereby enhancing the immersion of training, allowing users to feel close to real fall risks in the virtual environment, and enhancing the practical application value of training.

[0147] Further, the management server determines the fall prevention training level. , the first route boundary , Second route boundary and model size information to determine the target setting position corresponding to the target virtual element model, including:

[0148] The management server uses formula 1 and sets the fall prevention training level according to the , the first route boundary , Second route boundary And model size information to determine the target setting position , Formula 1 is:

[0149]

[0150] in, To set the minimum fall prevention training level, To preset the highest fall prevention training level, is the length dimension of the target virtual element model, is the width size of the target virtual element model, The first route boundary The position points of each boundary feature point in the preset coordinate system, The first route boundary Border with the second route The distance between The preset minimum passing distance.

[0151] It is worth noting that the management server uses Formula 1 to determine the setting position of the target virtual element model according to the fall prevention training level, the first route boundary, the second route boundary and the size information of the target virtual element model. Thus, by calculating the setting position of the target virtual element, the high accuracy of the virtual scene construction is ensured. This means that each virtual element will be placed in the most suitable position to achieve the training purpose while avoiding interference or affecting the continuity of the training path. In addition, the application of Formula 1 enables the virtual scene to dynamically adapt to the training level of different users and automatically adjust the layout of virtual elements to match the specific needs of users. This not only improves the pertinence of the training, but also ensures that the training scene is appropriately adjusted as the user progresses, maintaining the challenge and effectiveness of the training. In addition, the management server can accurately control the placement of virtual elements to avoid potential safety risks caused by random layout. This precise control also helps to monitor the training process and ensure that the training environment is both challenging and does not exceed the user's ability. On the other hand, it also helps to rationally plan the spatial resources of the virtual scene to ensure that the distribution of virtual elements meets the training needs without wasting space. This is particularly important in limited virtual scenes and helps to create a compact and feature-rich training environment. In addition, precisely set virtual elements can create more attractive and immersive training scenarios, improving the user's participation and concentration during training. At the same time, a well-planned training path reduces the possibility of users getting confused or frustrated during training, improving the overall training experience. It can be seen that the above solution can provide strong support for users to develop personalized rehabilitation training plans. By quantifying the training level and other key parameters, a training plan suitable for the user's rehabilitation needs can be designed based on the user's specific situation.

[0152] S204: The management server generates a virtual scene to be trained according to the basic virtual scene and the virtual element model set.

[0153] The management server generates a virtual scene to be trained according to the basic virtual scene and the virtual element model set, and sends the scene model data corresponding to the virtual scene to be trained to the first VR device.

[0154] Specifically, the management server uses a preset VR processing engine to load the basic virtual scene, which may be a blank room or any pre-set environment. Then, the server reads the model in the previously determined virtual element model set and adds it to the basic scene to form the final virtual scene to be trained. The VR processing engine in this step can be implemented using the functions of a game development engine (such as Unity or Unreal Engine). After completing the construction of the virtual scene to be trained, the management server packages the scene model data and sends it back to the first VR device. This may include the scene's geometric information, textures, lighting settings, and other necessary rendering data.

[0155] S205: The first VR device displays the virtual scene to be trained.

[0156] The first VR device displays the virtual scene to be trained to train the first user on the task to be trained for fall prevention. Specifically, after receiving the scene model data, the first VR device uses its built-in VR rendering software or engine to parse and display the virtual scene. The user will see an immersive environment corresponding to the actual training task and perform fall prevention training in it.

[0157] S206. The first VR device generates training video data corresponding to the fall prevention training task, and sends the training video data to the monitoring terminal.

[0158] The first VR device generates training video data corresponding to the fall prevention training task, and sends the training video data to the monitoring terminal so that the training video data is displayed on the monitoring terminal.

[0159] It is worth noting that after the user completes the fall prevention training task, the first VR device generates video data recorded during the training process and sends the data to the monitoring terminal for playback and analysis on the monitoring terminal. By generating training video data, rehabilitation experts or caregivers can intuitively observe the user's performance in the virtual environment on the monitoring terminal, including the accuracy of movements, reaction time, and balance control ability, thereby obtaining more comprehensive feedback than simple data reports. In addition, based on the training video, the user's movement patterns and fall risk points can be analyzed in detail, and then the training plan can be adjusted, specific types of training tasks can be added or reduced, or the settings of virtual elements can be fine-tuned to better meet the user's rehabilitation needs.

[0160] If remote monitoring and guidance are further set up, for remote rehabilitation training, the video playback function on the monitoring terminal allows rehabilitation experts to evaluate the user's training situation in real time or afterwards even if they are not on site, and provide immediate or delayed guidance suggestions, thereby enhancing the remote service capabilities of rehabilitation training.

[0161] In addition, the preservation of training video data helps verify whether the user has completed the training task as required, which is essential for tracking training progress and ensuring training quality, especially when the rehabilitation program requires insurance reimbursement or clinical research. Users can also watch the training video and improve the training effect through self-reflection. At the same time, seeing the video record of their own progress is also a positive motivation, which helps to improve the enthusiasm and continuity of training.

[0162] Figure 3 FIG. 1 is a schematic diagram of a virtual hospital rehabilitation management system according to an exemplary embodiment of the present application. Figure 3 As shown, the virtual hospital rehabilitation management system 300 provided in this embodiment includes: a VR device cluster 310 and a management server 320, wherein each VR device in the VR device cluster 310 is respectively connected to the management server 320 for communication;

[0163] Acquire first identity information of a first user through a first VR device in the VR device cluster 310, and send the first identity information to the management server 320;

[0164] The management server 320 determines a fall prevention task to be trained according to the first identity information;

[0165] The management server 320 determines a virtual element model set according to the fall prevention task to be trained, wherein the virtual element model set includes virtual element models required to construct a virtual scene to be trained on a basic virtual scene, and the virtual scene to be trained is a virtual scene corresponding to the fall prevention task to be trained;

[0166] The management server 320 generates the virtual scene to be trained according to the basic virtual scene and the virtual element model set, and sends the scene model data corresponding to the virtual scene to be trained to the first VR device;

[0167] The first VR device displays the virtual scene to be trained so as to train the first user on the fall prevention task to be trained.

[0168] Optionally, the virtual hospital rehabilitation management system further includes: a monitoring terminal 330, the monitoring terminal 330 is communicatively connected to the management server 320, and each VR device in the VR device cluster 310 is respectively connected to the monitoring terminal 330; before the management server 320 determines the fall prevention training task to be trained according to the first identity information, it also includes:

[0169] The monitoring terminal 330 obtains first physiological indicator data of the first user, and sends the first physiological indicator data to the management server 320, where the physiological indicator data includes the first identity information, first age information, first body mass index information, first muscle indicator information, first balance indicator information, and first fall record information;

[0170] Correspondingly, the management server 320 determines the fall prevention training task to be performed according to the first identity information, including:

[0171] The management server 320 obtains the first physiological indicator data according to the first identity information;

[0172] The management server 320 determines the fall prevention training level to be trained according to the first age information, the first body mass index information, the first muscle index information, the first balance index information and the first fall record information, wherein the first fall record information includes first fall number information;

[0173] The management server 320 determines the fall prevention task to be trained based on the fall prevention training difficulty level and the first fall record information, the fall prevention task to be trained includes a fall prevention sub-task sequence to be trained composed of multiple fall prevention sub-tasks to be trained, the basic virtual scenes corresponding to each fall prevention sub-task to be trained in the fall prevention sub-task sequence to be trained are the same virtual scene, and the first fall record information includes first fall scene type information.

[0174] Optionally, the management server 320 determines a set of virtual element models according to the fall prevention task to be trained, including:

[0175] The management server 320 determines a virtual element sequence according to each fall prevention subtask to be trained in the fall prevention subtask to be trained sequence, and the virtual elements in the virtual element sequence are used to construct a virtual scene to be trained corresponding to the fall prevention subtask to be trained on the basis of the basic virtual scene;

[0176] The management server 320 generates the virtual element model set according to the virtual element sequences determined by each fall prevention subtask to be trained.

[0177] Optionally, the management server 320 generates the to-be-trained virtual scene according to the basic virtual scene and the virtual element model set, including:

[0178] The management server 320 loads the basic virtual scene through a preset VR processing engine, and reads each virtual element model in the virtual element model set;

[0179] The management server 320 adds each virtual element model in the virtual element model set to the basic virtual scene to generate the scene model data corresponding to the virtual scene to be trained.

[0180] Optionally, the management server 320 adds each virtual element model in the virtual element model set to the basic virtual scene to generate the scene model data corresponding to the virtual scene to be trained, including:

[0181] The management server 320 obtains a reference element model set in the basic virtual scene. , and according to the reference element model set Determine the corresponding reference element position set ,in, The reference element model set The A reference element model, for The corresponding position;

[0182] The management server 320 obtains the preset fall prevention training original path in the basic virtual scene , the preset fall prevention training original path For a reference element model set The original path defined, the preset fall prevention training original path The reference element position set There is no position interference relationship;

[0183] The management server 320 determines the fall prevention training level to be trained and the preset fall prevention training original path. Determine the setting position of each virtual element model in the virtual element model set to generate an original path to be trained for fall prevention , wherein the scenario model data includes the original path to be trained for fall prevention .

[0184] Optionally, the management server 320 determines the fall prevention training level to be trained and the preset fall prevention training original path. Determining the setting position of each virtual element model in the virtual element model set includes:

[0185] The management server 320 obtains the preset fall prevention training original path The first route boundary and the second route boundary ;

[0186] The management server 320 obtains model size information of a target virtual element model in the virtual element model set;

[0187] The management server 320 generates a fall prevention training program based on the fall prevention training level and the first route boundary. , Second route boundary And the model size information determines the target setting position corresponding to the target virtual element model.

[0188] Optionally, the management server 320 determines the fall prevention training level to be trained , the first route boundary , Second route boundary and determining a target setting position corresponding to the target virtual element model based on the model size information, including:

[0189] The management server 320 uses formula 1 and the fall prevention training level to be trained , the first route boundary The second route boundary And the model size information determines the target setting position , the formula 1 is:

[0190]

[0191] in, To set the minimum fall prevention training level, To preset the highest fall prevention training level, is the length dimension of the target virtual element model, is the width size of the target virtual element model, The first route boundary The position points of each boundary feature point in the preset coordinate system, The first route boundary The second route borders The distance between The preset minimum passing distance.

[0192] Optionally, after the first VR device displays the virtual scene to be trained so as to train the first user on the fall prevention training task to be trained, the method further includes:

[0193] The first VR device generates training video data corresponding to the fall prevention training task, and sends the training video data to the monitoring terminal 330 so that the training video data is displayed on the monitoring terminal 330.

[0194] Figure 4 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. Figure 4 As shown, an electronic device 400 provided in this embodiment includes: a processor 401 and a memory 402; wherein:

[0195] The memory 402 is used to store computer programs, and the memory may also be a flash memory.

[0196] The processor 401 is used to execute the execution instructions stored in the memory to implement each step in the above method. For details, please refer to the relevant description in the above method embodiment.

[0197] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0198] When the memory 402 is a device independent of the processor 401, the electronic device 400 may further include:

[0199] The bus 403 is used to connect the memory 402 and the processor 401 .

[0200] This embodiment further provides a readable storage medium, in which a computer program is stored. When at least one processor of an electronic device executes the computer program, the electronic device executes the methods provided in the above-mentioned various implementation modes.

[0201] This embodiment also provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device implements the methods provided in the above various embodiments.

[0202] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0203] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A VR fall training data processing method based on a virtual hospital, characterized in that: Applied to a virtual hospital rehabilitation management system, the virtual hospital rehabilitation management system includes: a VR device cluster and a management server, wherein each VR device in the VR device cluster is respectively connected to the management server for communication; the method includes: Acquire first identity information of a first user through a first VR device in the VR device cluster, and send the first identity information to the management server; The management server determines a fall prevention task to be trained according to the first identity information; The management server determines a virtual element model set according to the fall prevention task to be trained, wherein the virtual element model set includes virtual element models required to construct a virtual scene to be trained on a basic virtual scene, and the virtual scene to be trained is a virtual scene corresponding to the fall prevention task to be trained; The management server generates the virtual scene to be trained according to the basic virtual scene and the virtual element model set, and sends the scene model data corresponding to the virtual scene to be trained to the first VR device; The first VR device displays the virtual scene to be trained, so as to train the first user on the fall prevention task to be trained; The virtual hospital rehabilitation management system further includes: a monitoring terminal, the monitoring terminal is connected to the management server in communication, and each VR device in the VR device cluster is connected to the monitoring terminal respectively; before the management server determines the fall prevention training task to be trained according to the first identity information, it also includes: The monitoring terminal obtains first physiological indicator data of the first user, and sends the first physiological indicator data to the management server, where the physiological indicator data includes the first identity information, first age information, first body mass index information, first muscle indicator information, first balance indicator information, and first fall record information; Correspondingly, the management server determines the fall prevention training task to be performed according to the first identity information, including: The management server obtains the first physiological indicator data according to the first identity information; The management server determines the fall prevention training level to be trained according to the first age information, the first body mass index information, the first muscle index information, the first balance index information, and the first fall record information, wherein the first fall record information includes first fall number information; The management server determines the fall prevention training task according to the fall prevention training difficulty level and the first fall record information, the fall prevention training task includes a fall prevention training subtask sequence consisting of a plurality of fall prevention training subtasks, the basic virtual scene corresponding to each fall prevention training subtask in the fall prevention training subtask sequence is the same virtual scene, and the first fall record information includes first fall scene type information; The management server determines a set of virtual element models according to the fall prevention task to be trained, including: The management server determines a virtual element sequence according to each fall prevention subtask to be trained in the fall prevention subtask to be trained sequence, wherein the virtual elements in the virtual element sequence are used to construct a virtual scene to be trained corresponding to the fall prevention subtask to be trained on the basis of the basic virtual scene; The management server generates the virtual element model set according to the virtual element sequences determined by each fall prevention subtask to be trained; The management server generates the virtual scene to be trained according to the basic virtual scene and the virtual element model set, including: The management server loads the basic virtual scene through a preset VR processing engine, and reads each virtual element model in the virtual element model set; The management server adds each virtual element model in the virtual element model set to the basic virtual scene to generate the scene model data corresponding to the virtual scene to be trained; The management server adds each virtual element model in the virtual element model set to the basic virtual scene to generate the scene model data corresponding to the virtual scene to be trained, including: The management server obtains a reference element model set in the basic virtual scene , and according to the reference element model set Determine the corresponding reference element position set ,in, The reference element model set The A reference element model, for The corresponding position; the management server obtains the preset fall prevention training original path in the basic virtual scene , the preset fall prevention training original path For a reference element model set The original path defined, the preset fall prevention training original path The reference element position set There is no position interference relationship; The management server determines the fall prevention training level to be trained and the preset fall prevention training original path. Determine the setting position of each virtual element model in the virtual element model set to generate an original path to be trained for fall prevention , wherein the scenario model data includes the original path to be trained for fall prevention ; The management server determines the fall prevention training level to be trained and the preset fall prevention training original path. Determining the setting position of each virtual element model in the virtual element model set includes: The management server obtains the preset fall prevention training original path The first route boundary and the second route boundary ; The management server obtains model size information of a target virtual element model in the virtual element model set; The management server determines the fall prevention training level to be trained, the first route boundary Second route boundary And the model size information determines the target setting position corresponding to the target virtual element model.

2. The VR fall training data processing method based on a virtual hospital according to claim 1 is characterized in that: After the first VR device displays the virtual scene to be trained to train the first user on the fall prevention task to be trained, the method further includes: The first VR device generates training video data corresponding to the fall prevention training task, and sends the training video data to the monitoring terminal so that the training video data is displayed on the monitoring terminal.

3. A virtual hospital rehabilitation management system, characterized in that: include: A VR device cluster and a management server, wherein each VR device in the VR device cluster is respectively connected to the management server for communication; Acquire first identity information of a first user through a first VR device in the VR device cluster, and send the first identity information to the management server; The management server determines a fall prevention task to be trained according to the first identity information; The management server determines a virtual element model set according to the fall prevention task to be trained, and sends the virtual element model set to the first VR device, wherein the virtual element model set includes virtual element models required to construct a virtual scene to be trained on a basic virtual scene, and the virtual scene to be trained is a virtual scene corresponding to the fall prevention task to be trained; The first VR device generates the virtual scene to be trained according to the basic virtual scene and the set of virtual element models, and displays the virtual scene to be trained, so as to train the first user in the fall prevention training task, wherein the basic virtual scene is preset in the first VR device; The virtual hospital rehabilitation management system further includes: a monitoring terminal, the monitoring terminal is connected to the management server in communication, and each VR device in the VR device cluster is connected to the monitoring terminal respectively; before the management server determines the fall prevention training task to be trained according to the first identity information, it also includes: The monitoring terminal obtains first physiological indicator data of the first user, and sends the first physiological indicator data to the management server, where the physiological indicator data includes the first identity information, first age information, first body mass index information, first muscle indicator information, first balance indicator information, and first fall record information; Correspondingly, the management server determines the fall prevention training task to be performed according to the first identity information, including: The management server obtains the first physiological indicator data according to the first identity information; The management server determines the fall prevention training level to be trained according to the first age information, the first body mass index information, the first muscle index information, the first balance index information, and the first fall record information, wherein the first fall record information includes first fall number information; The management server determines the fall prevention training task according to the fall prevention training difficulty level and the first fall record information, the fall prevention training task includes a fall prevention training subtask sequence consisting of a plurality of fall prevention training subtasks, the basic virtual scene corresponding to each fall prevention training subtask in the fall prevention training subtask sequence is the same virtual scene, and the first fall record information includes first fall scene type information; The management server determines a set of virtual element models according to the fall prevention task to be trained, including: The management server determines a virtual element sequence according to each fall prevention subtask to be trained in the fall prevention subtask to be trained sequence, wherein the virtual elements in the virtual element sequence are used to construct a virtual scene to be trained corresponding to the fall prevention subtask to be trained on the basis of the basic virtual scene; The management server generates the virtual element model set according to the virtual element sequences determined by each fall prevention subtask to be trained; The management server generates the virtual scene to be trained according to the basic virtual scene and the virtual element model set, including: The management server loads the basic virtual scene through a preset VR processing engine, and reads each virtual element model in the virtual element model set; The management server adds each virtual element model in the virtual element model set to the basic virtual scene to generate the scene model data corresponding to the virtual scene to be trained; The management server adds each virtual element model in the virtual element model set to the basic virtual scene to generate the scene model data corresponding to the virtual scene to be trained, including: the management server obtains the reference element model set in the basic virtual scene , and according to the reference element model set Determine the corresponding reference element position set ,in, The reference element model set The A reference element model, for The corresponding position; the management server obtains the preset fall prevention training original path in the basic virtual scene , the preset fall prevention training original path For a reference element model set The original path defined, the preset fall prevention training original path The reference element position set There is no position interference relationship; The management server determines the fall prevention training level to be trained and the preset fall prevention training original path. Determine the setting position of each virtual element model in the virtual element model set to generate an original path to be trained for fall prevention , wherein the scenario model data includes the original path to be trained for fall prevention ; The management server determines the fall prevention training level to be trained and the preset fall prevention training original path. Determining the setting position of each virtual element model in the virtual element model set includes: The management server obtains the preset fall prevention training original path The first route boundary and the second route boundary ; The management server obtains model size information of a target virtual element model in the virtual element model set; The management server determines the fall prevention training level to be trained, the first route boundary Second route boundary And the model size information determines the target setting position corresponding to the target virtual element model.

4. An electronic device, characterized in that: include: processor; as well as, A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to perform the method of claim 1 or 2 by executing the executable instructions.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to claim 1 or 2 when executed by a processor.

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