Clinical nurse emergency simulation training system and method and medium

Abstract

The invention relates to the technical field of virtual simulation, in particular to a clinical nurse emergency simulation training system and method and a medium, and the system comprises AR glasses which are used for presenting a virtual scene; the brain-computer interface is worn on the head of a nurse and used for collecting electroencephalogram signals of the nurse; the physiological data acquisition device is used for acquiring physiological data of nurses; the dummy serves as an entity operated by a nurse, and a sensor is arranged at a preset part and used for collecting operation data of the nurse; the camera is used for collecting first-aid measure images and voice executed by nurses; the processor is connected with the brain-computer interface, the physiological data acquisition device, the AR glasses, the dummy and the camera, and is used for generating an instruction signal based on the electroencephalogram signal and the physiological data and adjusting or improving a virtual scene based on the instruction signal; and based on the first-aid measure image, the voice and the operation data, the accuracy of the first-aid measure of the nurse is judged, and the first-aid training effect on the clinical nurse is effectively improved through the virtual environment.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual simulation, and in particular to a clinical nurse first aid simulation training system, method and medium. Background Art

[0002] As key members of the medical emergency response team, clinical nurses must quickly and accurately implement emergency measures when patients experience sudden cardiac arrest, anaphylactic shock, arrhythmias, and pulmonary embolism. These emergency measures require prior training, but traditional emergency training, which relies primarily on theoretical lectures and simple model exercises, presents numerous drawbacks. Theoretical instruction lacks intuitiveness, making it difficult for nurses to effectively integrate theoretical knowledge with practical application. Model exercises employ a single scenario and fail to realistically replicate complex and diverse emergency scenarios.

[0003] Therefore, how to improve the training environment for first aid measures to truly restore complex and changing first aid scenarios is a technical problem that needs to be solved urgently. Summary of the Invention

[0004] In view of the above problems, the present invention provides a clinical nurse first aid simulation training system, method and medium that overcome the above problems or at least partially solve the above problems.

[0005] In a first aspect, the present invention provides a clinical nurse first aid simulation training system, comprising:

[0006] AR glasses, worn on the nurse's face, are used to present a virtual scene, wherein the virtual scene includes a virtual state of any emergency condition of a virtual patient and virtual first aid equipment;

[0007] A brain-computer interface, worn on the nurse's head, is used to collect the nurse's EEG signals;

[0008] A physiological data collection device for collecting physiological data of nurses;

[0009] The simulator, which serves as the entity operated by the nurse, is equipped with sensors at preset locations to collect the nurse's operating data;

[0010] A camera is used to capture images and audio of the first aid measures performed by the nurse in response to any virtual emergency situation;

[0011] The processor is connected to the brain-computer interface, the physiological data acquisition device, the AR glasses, the simulator and the camera, and is used to generate a command signal based on the EEG signal and the physiological data, adjust or improve the virtual scene based on the command signal; and judge the standardization of the nurse's first aid measures based on the first aid measures images, voice and operation data.

[0012] Preferably, the physiological data acquisition device is specifically a smart bracelet or a patch sensor.

[0013] Preferably, the virtual emergency equipment includes: a defibrillator, an emergency vehicle, a suction device, an oxygen cylinder and a central oxygen supply.

[0014] Preferably, a pressure sensor is provided on the chest of the manikin for collecting cardiopulmonary resuscitation operation data of the nurse.

[0015] Preferably, the processor is specifically configured to:

[0016] Analyze and determine nurses’ awareness of target needs based on EEG signals and physiological data;

[0017] Based on the target demand awareness, generating corresponding command signals;

[0018] Based on the instruction signal, new virtual objects are added to the virtual scene according to the nurse's demand status to adjust or improve the virtual scene.

[0019] Preferably, the processor is specifically configured to:

[0020] Obtain historical EEG signals, historical physiological data and corresponding historical demand awareness of historical trainees;

[0021] Based on historical EEG signals, historical physiological data and corresponding historical demand awareness, a neural network model is trained to obtain a demand awareness prediction model;

[0022] Based on EEG signals and physiological data as well as the demand awareness prediction model, the target demand awareness of nurses is analyzed and determined.

[0023] Preferably, the processor is specifically configured to:

[0024] Based on the operation data, determining whether the first aid operation performed by the nurse is correct, and obtaining a first determination result;

[0025] Based on the first aid operation image, determining whether the action steps of the first aid operation performed by the nurse are correct, and obtaining a second determination result;

[0026] Based on the first judgment result and the second judgment result, the standardization of the nurse's first aid measures is judged.

[0027] In a second aspect, the present invention further provides a clinical nurse first aid simulation training method, which is applied to any of the clinical nurse first aid simulation training systems described in the first aspect, comprising:

[0028] Presenting a virtual scene to the nurse, wherein the virtual scene includes a virtual state of any emergency situation of the patient and virtual first aid equipment;

[0029] Collect nurses' EEG signals, physiological data, and nurses' operation data;

[0030] Collect images and voices of first aid measures performed by nurses in virtual situations of any emergency situation;

[0031] generating a command signal based on the EEG signal and the physiological data, and adjusting or improving the virtual scene based on the command signal;

[0032] Based on the first aid measure images, voice and operation data, the standardization of the nurse's first aid measures is judged.

[0033] In a third aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the method described in the second aspect when the program is executed by a processor.

[0034] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0035] The present invention provides a clinical nurse first aid simulation training system, comprising: AR glasses, worn on the nurse's face, for presenting a virtual scene, which includes a virtual state of any emergency situation of a patient and virtual first aid equipment; a brain-computer interface, worn on the nurse's head, for collecting the nurse's electroencephalogram (EEG) signal; a physiological data acquisition device, for collecting the nurse's physiological data; a simulator, as an entity operated by the nurse, provided with sensors at preset positions for collecting the nurse's operation data; a camera, for collecting images and voice of first aid measures performed by the nurse for any virtual situation of an emergency; a processor, connected to the brain-computer interface, the physiological data acquisition device, the AR glasses, the simulator, and the camera, for generating command signals based on EEG signals and physiological data, adjusting or improving the virtual scene based on the command signals; and judging the accuracy of the nurse's first aid measures based on the images of the first aid measures and the operation data, thereby effectively improving the effect of clinical nurses' first aid training through the creation of a virtual environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. Throughout the drawings, the same reference figures denote the same components. In the drawings:

[0037] Figure 1 A schematic structural diagram of a clinical nurse first aid simulation training system according to an embodiment of the present invention is shown;

[0038] Figure 2A schematic diagram of a simulated person in an embodiment of the present invention is shown;

[0039] Figure 3 A schematic flow chart of the steps of a clinical nurse first aid simulation training method according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0040] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0041] Example 1:

[0042] The embodiment of the present invention provides a clinical nurse first aid simulation training system, such as Figure 1 As shown, including:

[0043] AR glasses 101 are worn on the nurse's face and are used to present a virtual scene, which includes a virtual state of any emergency condition of a virtual patient and virtual first aid equipment;

[0044] The brain-computer interface 102 is worn on the nurse's head and is used to collect the nurse's brain electrical signals;

[0045] Physiological data collection device 103, used to collect physiological data of nurses;

[0046] The manikin 104, serving as the entity operated by the nurse, is provided with sensors at predetermined locations for collecting the nurse's operating data;

[0047] Camera 105 is used to collect images and voice of the first aid measures performed by the nurse in response to any virtual situation of emergency;

[0048] The processor 106 is connected to the brain-computer interface 102, the physiological data acquisition device 103, the AR glasses 101, the simulator 104 and the camera 105, and is used to generate command signals based on EEG signals and physiological data, adjust or improve the virtual scene based on the command signals; and judge the accuracy of the nurse's first aid measures based on the first aid measures images, voice and operation data.

[0049] In a specific embodiment, a patient may experience a sudden cardiac arrest, anaphylactic shock, arrhythmia, or pulmonary embolism, requiring a nurse to urgently administer appropriate first aid measures. For example, cardiopulmonary resuscitation may be performed for cardiac arrest, medication injection and oxygen inhalation may be performed for anaphylactic shock, a defibrillator or cardiopulmonary resuscitation may be used for arrhythmia, and cardiopulmonary resuscitation and anticoagulant medication may be used for pulmonary embolism.

[0050] When training clinical nurses on any emergency situation, AR glasses can be used to present a corresponding virtual scene. The virtual scene here mainly simulates the condition of a virtual patient.

[0051] For example, during cardiac arrest, the virtual patient will show signs of hypoxia such as disappearance of heart sounds, sudden loss of consciousness or transient convulsions, dilated pupils, intermittent breathing, and possible cyanosis or pale skin.

[0052] Anaphylactic shock involves a variety of symptoms, with the virtual patient experiencing symptoms of respiratory obstruction, circulatory failure, digestive tract symptoms, and skin and mucous membrane symptoms. Symptoms of respiratory obstruction include laryngeal obstruction, chest tightness, shortness of breath, difficulty breathing, suffocation, and cyanosis. Symptoms of circulatory failure include palpitations, pallor, sweating, a rapid and weak pulse, cold extremities, decreased blood pressure, and shock. Myocardial infarction is common in patients with coronary heart disease. Gastrointestinal symptoms include nausea and vomiting. Skin and mucous membrane symptoms include flushing of the skin in pairs, followed by various rashes.

[0053] When arrhythmia occurs, the virtual patient will experience symptoms such as palpitations, fatigue, dizziness, and in severe cases, blackouts and syncope.

[0054] In the case of pulmonary embolism, the virtual patient presented with symptoms such as dyspnea, cough and sputum, and chest pain.

[0055] Based on the above symptoms presented by the virtual patient, the nurse can identify which scenario the virtual patient is in that requires emergency treatment.

[0056] In any of the above virtual scenarios, the nurse wears the brain-computer interface 102 and, while observing the virtual patient, determines which emergency scenario it is by thinking.

[0057] At the same time, the physiological data acquisition module 103 collects the nurse's physiological data. Specifically, the physiological data acquisition module 103 is a smart bracelet or a patch sensor, so as to collect the nurse's physiological data, which includes: heart rate, respiratory rate, skin electrical response, etc.

[0058] The consciousness and status data of the nurses during the simulated training of first aid operations are collected through the brain-computer interface 102 and the physiological data acquisition module 103, so as to prepare for subsequent analysis and control.

[0059] Next, the nurse performs corresponding first aid measures for any of the above-mentioned first aid scenarios. Specifically, the camera 105 is used to collect images and voices of the first aid measures performed by the nurse for any virtual emergency situation.

[0060] In order to facilitate the nurse's operation, a simulator 104 is provided to support the nurse's first aid operation.

[0061] The simulator 104 is as follows Figure 2 As shown, a pressure sensor is set on the chest to identify the pressure strength and depth of the nurse during the cardiopulmonary resuscitation first aid operation, so as to judge the standardization of the cardiopulmonary resuscitation operation.

[0062] Of course, for other first aid measures, other sensors may be provided on the manikin 104 to identify operation data of other first aid operations.

[0063] For example, identification of drug injection sites, etc.

[0064] After the above data are collected, the processor 106 processes the data.

[0065] Specifically, it includes the adjustment and improvement of virtual scenarios and the normative evaluation of nurses' first aid measures.

[0066] When adjusting and improving the virtual scene, specifically including:

[0067] Analyze and determine nurses’ awareness of target needs based on EEG signals and physiological data;

[0068] Generate corresponding command signals based on target command demand awareness;

[0069] Based on the instruction signal, new virtual objects are added to the virtual scene according to the nurse's demand status to adjust and improve the virtual scene.

[0070] In a specific implementation, in order to analyze and determine the target demand awareness of nurses, EEG signals and physiological data are used as basic data and a demand awareness prediction model is used to perform predictions to improve the response efficiency of the system.

[0071] The construction process of the demand awareness prediction model is as follows:

[0072] Obtain historical EEG signals, historical physiological data and corresponding historical demand awareness of historical trainees;

[0073] Based on historical EEG signals, historical physiological data and corresponding historical demand awareness, a neural network model is trained to obtain a demand awareness prediction model.

[0074] Then, based on EEG signals and physiological data as well as the demand awareness prediction model, the target demand awareness of nurses was analyzed and determined.

[0075] Specifically, the collected EEG signals and physiological data are input into the demand awareness prediction model, thereby outputting the nurse's target demand awareness.

[0076] For example, the output target demand awareness is that it is currently necessary to take corresponding rescue measures for emergency scenarios, prepare rescue equipment, inform doctors, etc.

[0077] After determining the nurse's target demand awareness, a corresponding command signal is generated based on the target demand awareness. The command signal corresponds to a scene presentation of a virtual rescue device. The nurse can obtain the corresponding virtual rescue device through gesture operation.

[0078] Of course, the instruction signal may also be to adjust the posture of the virtual patient relative to the nurse so that the nurse can perform cardiopulmonary resuscitation as quickly as possible.

[0079] Therefore, the virtual scene can be adjusted and improved according to the command signal to quickly achieve the rescue purpose.

[0080] Normative evaluation of nurses' first aid measures, including:

[0081] Based on the operation data, determine whether the first aid operation performed by the nurse is correct, and obtain a first judgment result;

[0082] Based on the first aid operation image, determining whether the action steps of the first aid operation performed by the nurse are correct, and obtaining a second determination result;

[0083] Based on the first judgment result and the second judgment result, the standardization of the nurse's first aid measures is judged.

[0084] Since the operation data is collected by sensors on the simulator 104, the operation data collected by the corresponding sensors varies depending on the emergency situation. For example, in the case of cardiac arrest, the chest pressure sensor collects information about whether the nurse is performing corresponding chest compressions, thereby obtaining the first judgment result.

[0085] Next, determine whether the steps of the first aid operation performed by the nurse are correct. The steps of cardiopulmonary resuscitation include: 1. Pat and call the patient to determine whether he is conscious, touch the carotid pulse, and observe whether he is breathing. The evaluation time cannot exceed 10 seconds. 2. If he is unconscious. No breathing or only has dying sigh-like breathing, call for help immediately and ask others to call the emergency number; 3. Let the patient lie on his back, overlap his hands, place the base of his palms on the midpoint of the line connecting the two nipples of the patient, and press vertically downward with a frequency of at least 100 times / minute and a depth of at least 5cm. The pressing and relaxation time should be roughly equal; 4. Clean foreign objects from the patient's mouth and use the head tilt and jaw lift method to open the airway; 5. Pinch the patient's nose and perform mouth-to-mouth artificial respiration, each lasting more than 1 second, and observe the chest rise and fall.

[0086] By analyzing and judging the first aid operation images and voice according to the above steps, each step can be judged by extracting key frame images for identification, so as to determine whether the action steps of the first aid operation performed by the nurse are correct and whether the interactive voice meets the scene requirements, thereby obtaining the second judgment result.

[0087] Finally, the weighted sum of the first judgment result and the second judgment result is calculated according to the weight value, thereby obtaining the normative evaluation result of the nurse's first aid measures, and finally outputting an evaluation report to evaluate the results of the nurse's first aid simulation training.

[0088] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0089] The present invention provides a clinical nurse first aid simulation training system, comprising: AR glasses, worn on the nurse's face, for presenting a virtual scene, which includes a virtual state of any emergency situation of a patient and virtual first aid equipment; a brain-computer interface, worn on the nurse's head, for collecting the nurse's electroencephalogram (EEG) signal; a physiological data acquisition device, for collecting the nurse's physiological data; a simulator, as an entity operated by the nurse, provided with sensors at preset positions for collecting the nurse's operation data; a camera, for collecting images and voice of first aid measures performed by the nurse for any virtual situation of an emergency; a processor, connected to the brain-computer interface, the physiological data acquisition device, the AR glasses, the simulator, and the camera, for generating command signals based on EEG signals and physiological data, adjusting or improving the virtual scene based on the command signals; and judging the accuracy of the nurse's first aid measures based on the images, voice, and operation data of the first aid measures, thereby effectively improving the effect of clinical nurses' first aid training through the creation of a virtual environment.

[0090] Example 2

[0091] Based on the same inventive concept, the present invention also provides a clinical nurse first aid simulation training method, which is applied to the clinical nurse first aid simulation training system described in Example 1. Figure 3 As shown, including:

[0092] S301, presenting a virtual scene to a nurse, wherein the virtual scene includes a virtual state of any emergency situation of a patient and virtual first aid equipment;

[0093] S302, collecting the nurse's EEG signal, physiological data, and operation data;

[0094] S303, collecting images and voices of first aid measures performed by the nurse in response to any virtual state of emergency;

[0095] S304, generating a command signal based on the EEG signal and the physiological data, and adjusting or improving the virtual scene based on the command signal;

[0096] S305: Based on the first aid measure images, voice and operation data, the standardization of the first aid measures taken by the nurse is evaluated.

[0097] In an optional implementation, S304 includes:

[0098] Analyze and determine nurses’ awareness of target needs based on EEG signals and physiological data;

[0099] Based on the target demand awareness, generating corresponding command signals;

[0100] Based on the instruction signal, new virtual objects are added to the virtual scene according to the nurse's demand status to adjust or improve the virtual scene.

[0101] In an optional implementation, S304 further includes:

[0102] Obtain historical EEG signals, historical physiological data and corresponding historical demand awareness of historical trainees;

[0103] Based on historical EEG signals, historical physiological data and corresponding historical demand awareness, a neural network model is trained to obtain a demand awareness prediction model;

[0104] Based on EEG signals and physiological data as well as the demand awareness prediction model, the target demand awareness of nurses is analyzed and determined.

[0105] In an optional implementation, S305 includes:

[0106] Based on the operation data, determining whether the first aid operation performed by the nurse is correct, and obtaining a first determination result;

[0107] Based on the first aid operation image, determining whether the action steps of the first aid operation performed by the nurse are correct, and obtaining a second determination result;

[0108] Based on the first judgment result and the second judgment result, the standardization of the nurse's first aid measures is judged.

[0109] Example 3:

[0110] Based on the same inventive concept, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned clinical nurse first aid simulation training method when executed by a processor.

[0111] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general-purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing this type of system. In addition, the present invention is not directed to any specific programming language. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the best mode of the present invention.

[0112] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0113] Similarly, it should be understood that in order to streamline the present invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed invention requires more features than those explicitly recited in each embodiment. Rather, as reflected in each embodiment, inventive aspects lie in fewer than all the features of the individual embodiments previously disclosed. Accordingly, the claims that follow the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the present invention.

[0114] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0115] Furthermore, those skilled in the art will appreciate that although some embodiments herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, in a specific embodiment, any one of the claimed embodiments may be used in any combination.

[0116] The various component embodiments of the present invention can be implemented in hardware, or in a software module running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the clinical nurse first aid simulation training system according to an embodiment of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., computer program and computer program product) for executing a part or all of the method described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0117] It should be noted that the above embodiments illustrate rather than limit the invention, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

Claims

1. A clinical nurse first aid simulation training system, characterized in that: include: AR glasses, worn on the nurse's face, are used to present a virtual scene, wherein the virtual scene includes a virtual state of any emergency condition of a virtual patient and virtual first aid equipment; A brain-computer interface, worn on the nurse's head, is used to collect the nurse's EEG signals; A physiological data collection device for collecting physiological data of nurses; The simulator, which serves as the entity operated by the nurse, is equipped with sensors at preset locations to collect the nurse's operating data; A camera is used to capture images and audio of the first aid measures performed by the nurse in response to any virtual emergency situation; a processor connected to the brain-computer interface, the physiological data acquisition device, the AR glasses, the simulator, and the camera, and configured to generate a command signal based on the EEG signal and the physiological data, and to adjust or improve the virtual scene based on the command signal; And based on the first aid measure images, voice and operation data, the standardization of the nurse's first aid measures is judged.

2. The system according to claim 1, wherein The physiological data acquisition device is specifically a smart bracelet or a patch sensor.

3. The system according to claim 1, wherein: Virtual emergency equipment includes: defibrillator, emergency vehicle, suction machine, oxygen cylinder and central oxygen supply.

4. The system according to claim 1, wherein A pressure sensor is provided on the chest of the manikin for collecting cardiopulmonary resuscitation operation data of the nurse.

5. The system according to claim 1, wherein: The processor is specifically configured to: Analyze and determine nurses’ awareness of target needs based on EEG signals and physiological data; Based on the target demand awareness, generating corresponding command signals; Based on the instruction signal, new virtual objects are added to the virtual scene according to the nurse's demand status to adjust or improve the virtual scene.

6. The system according to claim 5, wherein: The processor is specifically configured to: Obtain historical EEG signals, historical physiological data and corresponding historical demand awareness of historical trainees; Based on historical EEG signals, historical physiological data and corresponding historical demand awareness, a neural network model is trained to obtain a demand awareness prediction model; Based on EEG signals and physiological data as well as the demand awareness prediction model, the target demand awareness of nurses is analyzed and determined.

7. The system according to claim 1, wherein: The processor is specifically configured to: Based on the operation data, determining whether the first aid operation performed by the nurse is correct, and obtaining a first determination result; Based on the first aid operation image, determining whether the action steps of the first aid operation performed by the nurse are correct, and obtaining a second determination result; Based on the first judgment result and the second judgment result, the standardization of the nurse's first aid measures is judged.

8. A clinical nurse first aid simulation training method, applied to the clinical nurse first aid simulation training system according to any one of claims 1 to 7, characterized in that: include: Presenting a virtual scene to the nurse, wherein the virtual scene includes a virtual state of any emergency situation of the patient and virtual first aid equipment; Collect nurses' EEG signals, physiological data, and nurses' operation data; Collect images and voices of first aid measures performed by nurses in virtual situations of any emergency situation; generating a command signal based on the EEG signal and the physiological data, and adjusting or improving the virtual scene based on the command signal; Based on the first aid measure images, voice and operation data, the standardization of the nurse's first aid measures is judged.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to claim 8 is implemented.

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