Training and evaluation method and system based on virtual reality

Abstract

The invention discloses a training and evaluation method and system based on virtual reality, and the method comprises the steps: creating a virtual reality ship operation working scene model, and associating a corresponding virtual reality VR device; acquiring physiological state data of the related personnel in real time by utilizing physiological feedback monitoring equipment, and calculating a workload index WLI of the related personnel based on the physiological state data; based on the operation performance data of the related personnel, evaluating the operation skills of the related personnel to obtain a result; and according to the workload index WLI and the evaluation result, generating a personalized training suggestion. The system has the beneficial effects of providing immersive training experience, objective skill evaluation, real-time feedback and dynamic adjustment, comprehensively considering the physiological state and improving safety and efficiency.

Description

Technical Field

[0001] The invention belongs to the field of virtual reality, and in particular relates to a training and evaluation method and system based on virtual reality. Background Art

[0002] In the marine transportation and shipping industry, the crew's operational skills and ability to cope with high-load working environments are crucial. With the growth of global trade, the carrying capacity and navigation complexity of ships are increasing, and the work pressure and task complexity faced by crew members are also increasing. Traditional training methods usually rely on classroom lectures, field operations and simulation equipment, but these methods have some significant limitations.

[0003] First, the limitation of the training environment is a major shortcoming of traditional methods. Although some training centers have simulation equipment, these devices often cannot fully replicate the real sailing environment. In actual operations, crew members may encounter various unforeseen situations, such as bad weather, equipment failure or emergencies. Traditional training cannot allow crew members to fully experience these situations, resulting in their insufficient adaptability in real sailing. This training method lacks practical experience and may affect the crew's decision-making ability and operational safety at critical moments.

[0004] Secondly, the subjectivity of skill assessment is also a problem worthy of attention. In traditional training, the skill assessment of crew members often relies on the trainer's personal judgment and experience, lacking objectivity and consistency. Different trainers may have different views on the performance of the same crew member. This subjectivity makes it difficult to quantify the assessment results and provide clear guidance for subsequent training. In addition, the lack of unified evaluation standards also makes it difficult to compare the training results horizontally, affecting the overall quality of training.

[0005] Furthermore, insufficient feedback and adaptability is another shortcoming of traditional training. Usually, after completing the training, the crew will be evaluated a few weeks or months later, rather than providing real-time feedback during the training process. This delayed feedback mechanism makes it impossible for the crew to adjust their skills in time during actual operations, resulting in a disconnect between training and actual work. Therefore, the training content cannot be quickly adjusted according to the performance of the crew to meet their individual needs.

[0006] In addition, the lack of physiological status monitoring is also a major shortcoming of traditional training methods. In a high-load working environment, the physical and mental state of the crew has a significant impact on their operational performance. Traditional training often ignores this point and does not monitor the crew's physiological indicators in real time, such as heart rate, respiratory rate, etc., resulting in the inability to assess the crew's psychological load and physical fatigue. This neglect may cause the crew to make wrong decisions in a high-pressure environment, thus affecting navigation safety. Summary of the invention

[0007] In view of the defects in the above-mentioned prior art, the present invention provides a training and evaluation method based on virtual reality, comprising the following steps:

[0008] Step S101, creating a virtual reality ship operation work scene model and associating corresponding virtual reality VR equipment;

[0009] Step S103, using physiological feedback monitoring equipment to obtain physiological status data of the relevant personnel in real time, wherein the physiological status data includes heart rate, respiratory rate and skin electrical response, and calculating the workload index WLI of the relevant personnel based on the physiological status data;

[0010] Step S105: evaluating the operation skills of the relevant personnel based on the operation performance data of the relevant personnel;

[0011] Step S107: Generate personalized training suggestions based on the workload index WLI and the evaluation results.

[0012] The workload index WLI in step S103 is calculated using the following formula:

[0013] HR i Indicates the i-th heart rate data, BR i Represents the i-th respiratory rate data, EDR i represents the ith skin electrical response data, C i represents the corresponding weight coefficient, and n represents the number of monitoring times.

[0014] The operation performance data in step S105 includes a task completion time index T, an operation accuracy index A, a reaction time index R, a step compliance rate index S and a physiological index B.

[0015] The following calculation formula for the comprehensive skill score is defined in step S105:

[0016] , where C is the comprehensive skill score; D is the actual duration of the task; λ is the decay constant, which represents the decay of performance over time; t is the time variable, which represents the instantaneous value within the duration of the task;

[0017] T target is the target task completion time; R target is the target reaction time; B max is the maximum value of physiological index; h A (A) h T (T), h R (R), h P(P), h B (B) W (WLI) is a normalization function.

[0018] The training suggestion in step S107 is generated based on the training demand, and the training demand is expressed by the following formula:

[0019]

[0020] , where N is the training demand; M is the number of samples; WLI target is the target value of each indicator; k1, k2, k3, k4, k5, k6 are weight coefficients, reflecting the importance of each indicator in the training needs calculation.

[0021] Wherein, the step S107 includes:

[0022] If C <C target , then N>0, indicating that the individual’s skill level is below the target level and needs training;

[0023] If WLI>WLI target , then training on stress management and efficiency improvement is needed;

[0024] If N>0 and WLI≤WLI target Then the training content focuses on operational accuracy and reaction time;

[0025] If N>0 and WLI>WLI target Training recommendations should focus on both operational accuracy and reaction time and workload management.

[0026] Wherein, in step S101, Blender or Maya is used for 3D modeling to create an environment and a ship scene, thereby constructing a virtual reality ship operation work scene model.

[0027] The ship scenes include control room, deck operation or emergency response.

[0028] The VR device includes: one or more of Oculus Quest 2, VR controller or HTC Vive Tracker.

[0029] The present invention also proposes a training and evaluation device based on virtual reality, comprising

[0030] A VR scene creation module, which is used to create a virtual reality ship operation work scene model and associate it with corresponding virtual reality VR equipment;

[0031] A workload index generation module, which is used to obtain the physiological state data of the relevant personnel in real time by using a physiological feedback monitoring device, wherein the physiological state data includes heart rate, respiratory rate and skin electrical response, and calculate the workload index WLI of the relevant personnel based on the physiological state data;

[0032] An evaluation result generating module, which is used to evaluate the operation skills of the relevant personnel based on the operation performance data of the relevant personnel;

[0033] The training suggestion module is used to generate personalized training suggestions according to the workload index WLI and the evaluation results.

[0034] Compared with the prior art, the present invention has the following advantages:

[0035] VR technology provides a highly simulated virtual environment, allowing crew members to simulate real work scenarios in a safe environment and improve their practical operational capabilities.

[0036] The crew’s operational performance is quantitatively analyzed through machine learning models to generate comprehensive scores, provide accurate skill assessments, and eliminate bias from subjective judgments.

[0037] The system can monitor the crew's physiological data and operational performance in real time, dynamically adjust the training content based on the evaluation results, achieve personalized training, and ensure that the training plan matches the actual needs of the crew.

[0038] Incorporating physiological monitoring data into the skill assessment system ensures that the crew's mental state and physical fatigue are fully taken into account in a high-load working environment, thereby improving the scientific nature and effectiveness of training.

[0039] Through targeted training and real-time feedback, the crew's adaptability and coping strategies in the face of complex working environments have been enhanced, significantly improving operational safety and work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present disclosure will become readily understood. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0041] Figure 1 The figure is a flow chart showing a training and assessment method based on virtual reality according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0044] It should be understood that although the terms first, second, third, etc. may be used to describe ... in the embodiments of the present invention, these ... should not be limited to these terms. These terms are only used to distinguish .... For example, without departing from the scope of the embodiments of the present invention, the first ... may also be referred to as the second ..., and similarly, the second ... may also be referred to as the first ....

[0045] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0046] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0047] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0048] The optional embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0049] Embodiment 1

[0050] like Figure 1 As shown, the present invention discloses a training and evaluation method based on virtual reality, comprising the following steps:

[0051] Step S101, creating a virtual reality ship operation work scene model and associating corresponding virtual reality VR equipment;

[0052] Step S103, using physiological feedback monitoring equipment to obtain physiological status data of the relevant personnel in real time, wherein the physiological status data includes heart rate, respiratory rate and skin electrical response, and calculating the workload index WLI of the relevant personnel based on the physiological status data;

[0053] Step S105: evaluating the operation skills of the relevant personnel based on the operation performance data of the relevant personnel;

[0054] Step S107: Generate personalized training suggestions based on the workload index WLI and the evaluation results.

[0055] In step S101, the goal is first determined: to improve the crew's skills in actual operations and their ability to deal with emergencies. The scope of application of scenarios includes ship control, emergency response, equipment operation, etc.

[0056] The ship model uses a high-precision 3D ship model, including the hull, deck, control room, etc. The environmental settings involve the marine environment, weather changes (such as wind and waves, haze), etc. Interactive elements include dashboards, joysticks, buttons and other operable objects. Key scenes involve: the control room, which simulates the ship's control interface and displays information such as navigation and engine status; deck operations, which simulate various operations on the deck, such as cargo handling and equipment maintenance; emergency response, which designs emergency scenarios (such as fire and water leakage) to train the crew's emergency response capabilities.

[0057] Among them, use software (such as Blender, Maya, Unity) for 3D modeling to create ships and environmental scenes. Choose a suitable VR development platform, such as Unity, which is suitable for creating interactive 3D scenes and supports a variety of VR devices; UnrealEngine, which provides high-quality graphics support and is suitable for complex scenes.

[0058] VR devices include head-mounted displays (HMD): Oculus Quest 2, wireless and highly flexible, suitable for training environments; HTC Vive Pro, high resolution, suitable for scenarios requiring fine manipulation. Input devices include: VR controllers, used to manipulate objects in the virtual environment, such as joysticks and buttons; gesture recognition devices (such as Leap Motion), used to enhance the natural interaction experience; full-body tracking devices, such as HTC Vive Tracker, can be used for full-body motion capture to enhance immersion.

[0059] Embodiment 2

[0060] The present invention proposes a training and evaluation method based on virtual reality, comprising the following steps:

[0061] Step S101, creating a virtual reality ship operation work scene model and associating corresponding virtual reality VR equipment;

[0062] Step S103, using physiological feedback monitoring equipment to obtain physiological status data of the relevant personnel in real time, wherein the physiological status data includes heart rate, respiratory rate and skin electrical response, and calculating the workload index WLI of the relevant personnel based on the physiological status data;

[0063] Step S105: evaluating the operation skills of the relevant personnel based on the operation performance data of the relevant personnel;

[0064] Step S107: Generate personalized training suggestions based on the workload index WLI and the evaluation results.

[0065] The workload index WLI in step S103 is calculated using the following formula:

[0066] HR i Indicates the i-th heart rate data, BR i Represents the i-th respiratory rate data, EDR i represents the ith skin electrical response data, C i represents the corresponding weight coefficient, and n represents the number of monitoring times.

[0067] The operation performance data in step S105 includes a task completion time index T, an operation accuracy index A, a reaction time index R, a step compliance rate index S and a physiological index B.

[0068] The following calculation formula for the comprehensive skill score is defined in step S105:

[0069] , where C is the comprehensive skill score; D is the actual duration of the task; λ is the decay constant, which represents the decay of performance over time; t is the time variable, which represents the instantaneous value within the duration of the task;

[0070] Ttarget is the target task completion time; Rtarget is the target reaction time; Bmax is the maximum value of the physiological index; hA(A), hT(T), hR(R), hP(P), hB(B), and hW(WLI) are normalized functions.

[0071] The specific definitions are as follows:

[0072] h A (A) = k A *A 2 ;

[0073] h T (T) = k T *(T target -T);

[0074] h R (R) = k R *(R target -R);

[0075] h P (P) = k P *P 2 ;

[0076] h B (B) = k B *(B max -B);

[0077] h W (WLI) = k W *WLI 2 , where k is the normalized coefficient of the corresponding function.

[0078] The training suggestion in step S107 is generated based on the training demand, and the training demand is expressed by the following formula:

[0079]

[0080] , where N is the training demand; M is the number of samples; WLI target is the target value of each indicator; k1, k2, k3, k4, k5, k6 are weight coefficients, reflecting the importance of each indicator in the training needs calculation.

[0081] Wherein step S105 comprises the following steps:

[0082] Step 1: Data collection and preprocessing

[0083] First, it is necessary to collect the crew's operational performance data, including the following indicators: task completion time index T, operation accuracy index A, reaction time index R, step compliance rate index S, physiological index B and workload index WLI.

[0084] Among them, task completion time (T): records the time required for crew members to complete a specific task. It can be compared with the target completion time (T target ) for comparison.

[0085] Operation accuracy (A): can be calculated by the ratio of the number of correct operations to the total number of operations:

[0086] A = (number of correct operations / total number of operations) × 100%.

[0087] Reaction time (R): records the crew's reaction speed to emergencies and the target reaction time (R target ) for comparison.

[0088] Step Compliance Rate (S): Calculated by the frequency with which the crew follows the predetermined operating steps:

[0089] S = (number of times a step is followed / total number of steps) x 100%.

[0090] Physiological indicators (B): can be real-time data such as heart rate and respiratory rate, which usually need to be standardized in order to be compared with other indicators.

[0091] Data preprocessing

[0092] Before using the machine learning model, the data needs to be preprocessed, including:

[0093] Missing value handling: fill in or delete missing data.

[0094] Feature scaling: Normalize numerical features (e.g., using Z-score normalization).

[0095] Feature selection: Select the features that are most influential on skill estimation.

[0096] Step 2: Construct feature matrix

[0097] The processed data is constructed into a feature matrix X and a label vector y.

[0098] The feature matrix XX contains all the operational performance indicators:

[0099] Where n is the number of monitoring times.

[0100] The label vector y represents the crew's operational skill score C:

[0101]

[0102] Step 3: Select a machine learning model

[0103] You can choose a suitable machine learning model for skill assessment. Commonly used models include:

[0104] Linear Regression: Used for regression problems.

[0105] Support Vector Machine: Suitable for classification and regression tasks.

[0106] Random Forest: Used to improve prediction accuracy.

[0107] Neural network: suitable for processing complex nonlinear relationships.

[0108] Step 4: Model training

[0109] Using the training dataset X train and train Perform model training.

[0110] Assuming that the linear regression model is selected, the model formula is:

[0111]

[0112] in, is the predicted value, β is the model parameter, and ∈ is the error term.

[0113] Step 5: Model evaluation

[0114] Using the test dataset X test , calculate the predicted operational skill score.

[0115] Prediction formula:

[0116]

[0117] The mean square error (MSE) can be used as the evaluation indicator:

[0118]

[0119] Step 6: Interpretation and application of results

[0120] The operational skill scores obtained through the model can be used to:

[0121] Assess crew skill level: Determine whether the crew meets the operational requirements based on the scores.

[0122] Develop personalized training recommendations: Provide targeted training to crew members based on the assessment results.

[0123] Wherein, the step S107 includes:

[0124] If C <C target , then N>0, indicating that the individual’s skill level is below the target level and training is required;

[0125] If WLI>WLI target , then training on stress management and efficiency improvement is needed;

[0126] If N>0 and WLI≤WLI target Then the training content focuses on operational accuracy and reaction time;

[0127] If N>0 and WLI>WLI target Training recommendations should focus on both operational accuracy and reaction time and workload management.

[0128] Wherein, in step S101, Blender or Maya is used for 3D modeling to create an environment and a ship scene, thereby constructing a virtual reality ship operation work scene model.

[0129] The ship scenes include control room, deck operation or emergency response.

[0130] The VR device includes: one or more of Oculus Quest 2, VR controller or HTC Vive Tracker.

[0131] Embodiment 3

[0132] The present invention also proposes a training and evaluation device based on virtual reality.

[0133] A VR scene creation module, which is used to create a virtual reality ship operation work scene model and associate it with corresponding virtual reality VR equipment;

[0134] A workload index generation module, which is used to obtain the physiological state data of the relevant personnel in real time by using a physiological feedback monitoring device, wherein the physiological state data includes heart rate, respiratory rate and skin electrical response, and calculate the workload index WLI of the relevant personnel based on the physiological state data;

[0135] An evaluation result generating module, which is used to evaluate the operation skills of the relevant personnel based on the operation performance data of the relevant personnel;

[0136] The training suggestion module is used to generate personalized training suggestions according to the workload index WLI and the evaluation results.

[0137] Embodiment 4:

[0138] An embodiment of the present disclosure provides a non-volatile computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the method steps described in the above embodiment.

[0139] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0140] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0141] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (AN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0142] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0143] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0144] The above introduces the preferred embodiments of the present invention, which is intended to make the spirit of the present invention clearer and easier to understand, but is not intended to limit the present invention. All modifications, substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection outlined by the claims attached to the present invention.

Claims

1. A training and assessment method based on virtual reality, characterized in that: The following steps are involved: Step S101, creating a virtual reality ship operation work scene model and associating corresponding virtual reality VR equipment; Step S103, using physiological feedback monitoring equipment to obtain physiological status data of the relevant personnel in real time, wherein the physiological status data includes heart rate, respiratory rate and skin electrical response, and calculating the workload index WLI of the relevant personnel based on the physiological status data; Step S105: evaluating the operation skills of the relevant personnel based on the operation performance data of the relevant personnel; Step S107: Generate personalized training suggestions based on the workload index WLI and the evaluation results.

2. The method according to claim 1, characterized in that: The workload index WLI in step S103 is calculated using the following formula: HR i Indicates the i-th heart rate data, BR i Represents the i-th respiratory rate data, EDR i represents the ith skin electrical response data, C i represents the corresponding weight coefficient, and n represents the number of monitoring times.

3. The method according to claim 2, characterized in that: The operation performance data in step S105 includes a task completion time index T, an operation accuracy index A, a reaction time index R, a step compliance rate index S and a physiological index B.

4. The method according to claim 3, characterized in that: The calculation formula for the comprehensive skill score is defined in step S105 as follows: C=∫0 D [e (-λt) *(A*h A (A)+(1-T / T target )*h T (T)+(1-R / R target )*h R (R)+P*h P (P)+(1-B / B max )*h B (B)-WLI*h W (WLI))]dt, Where C is the comprehensive skill score; D is the actual duration of the task; λ is the decay constant, which represents the decay of performance over time; t is the time variable, which represents the instantaneous value within the duration of the task; T target is the target task completion time; R target is the target reaction time; B max is the maximum value of physiological index; h A (A) h T (T), h R (R), h P (P), h B (B) w (WLI) is a normalization function.

5. The method according to claim 4, characterized in that: The training suggestion in step S107 is generated based on the training demand, and the training demand is expressed by the following formula: Where N is the training demand; M is the number of samples; WLI target is the target value of each indicator; k1, k2, k3, k4, k5, k6 are weight coefficients, reflecting the importance of each indicator in the training needs calculation.

6. The method according to claim 5, characterized in that The step S107 includes: If C <C target , then N>0, indicating that the individual’s skill level is below the target level and needs training; If WLI>WLI target , then training on stress management and efficiency improvement is needed; If N>0 and WLI≤WLI target Then the training content focuses on operational accuracy and reaction time; If N>0 and WLI>WLI target Training recommendations should focus on both operational accuracy and reaction time and workload management.

7. The method according to claim 1, characterized in that: In step S101, Blender or Maya is used for 3D modeling to create an environment and a ship scene, thereby constructing a virtual reality ship operation work scene model.

8. The method according to claim 1, characterized in that: The ship scenarios include control room, deck operations or emergency response.

9. The method according to claim 1, characterized in that: The VR device includes: one or more of Oculus Quest 2, VR controller or HTC Vive Tracker.

10. A training and assessment device based on virtual reality, comprising: A VR scene creation module, which is used to create a virtual reality ship operation work scene model and associate it with corresponding virtual reality VR equipment; A workload index generation module, which is used to obtain the physiological state data of the relevant personnel in real time by using a physiological feedback monitoring device, wherein the physiological state data includes heart rate, respiratory rate and skin electrical response, and calculate the workload index WLI of the relevant personnel based on the physiological state data; An evaluation result generating module, which is used to evaluate the operation skills of the relevant personnel based on the operation performance data of the relevant personnel; The training suggestion module is used to generate personalized training suggestions according to the workload index WLI and the evaluation results.

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