Simulation diagnosis system based on VR technology

Abstract

The invention relates to the technical field of VR, and discloses a simulation diagnosis system based on the VR technology, which comprises a virtual environment construction module, a control module, a real-time acquisition module, a data preprocessing module, a synchronous analysis module, an interaction module, a feedback module and a storage module. By arranging the synchronous analysis module, the interaction module and the feedback module, a user is diagnosed through the VR technology, the VR technology is combined with medical image examination, more convenient and comfortable examination experience is provided for a patient, the medical image examination training requirement can be met, meanwhile, the fitness of the user in the medical image examination process is analyzed, and the user experience is improved. When the user may have uncomfortable conditions, the interaction module relieves the emotion such as tension of the user, so that the user fitness is improved, the scanning work of the scanning equipment continues, the scanning effect is ensured, the emotion of the user is considered while diagnosis is performed, and the user experience is improved. And the phenomenon of poor scanning effect caused by own psychological reasons of some patients is prevented.

Description

Technical Field

[0001] The present invention relates to the field of VR technology, and more specifically to a simulation diagnosis system based on VR technology. Background Art

[0002] With the development of virtual reality technology, its application in the medical field has gradually attracted attention, especially in medical imaging examinations. The use of VR technology has brought a new diagnostic experience to doctors and patients. Medical imaging examinations are an important means of modern medical diagnosis. As an important auxiliary method of medical diagnosis, it can greatly improve the accuracy and efficiency of diagnostic results.

[0003] However, traditional medical imaging examination methods often have certain limitations, such as long examination time and low patient comfort. Some patients have poor scanning results due to their own psychological reasons. In addition, real medical imaging equipment is often required for medical imaging examination training, which is not only costly but also poses certain safety risks.

[0004] Therefore, there is a need for a simulation diagnosis system based on VR technology, which uses VR technology to perform medical imaging examinations and combines VR technology with medical imaging examinations to provide patients with a more convenient and comfortable examination experience. It can also meet the training needs of medical imaging examinations and provide more accurate and efficient diagnosis based on the medical imaging examination results obtained based on VR technology. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a simulation diagnosis system based on VR technology to solve the problems existing in the above-mentioned background technology.

[0006] The present invention provides the following technical solutions: a simulation diagnosis system based on VR technology, comprising a virtual environment construction module, a control module, a real-time acquisition module, a data preprocessing module, a synchronous analysis module, an interactive module, a feedback module and a storage module; The virtual environment construction module generates a virtual medical imaging examination environment, i.e., a virtual environment, based on a real medical imaging examination environment; The control module is used to exchange data between the VR device and the virtual environment, and the user can select the medical imaging device in the virtual environment to perform imaging examination; The real-time acquisition module is used to acquire the user's body data and scan data in real time through an acquisition device; The data preprocessing module is used to preprocess the data of the real-time acquisition module and transmit it to the synchronization analysis module; The synchronous analysis module is used to receive and analyze the data preprocessed by the data preprocessing module, and the synchronous analysis module includes a body data analysis unit and a scan data analysis unit; The interactive module is used to receive interactive instructions from the body data analysis unit and provide an interactive operation interface to the user; The feedback module is used to calculate the remission coefficient, and at the same time feed back the diagnosis result to the human-computer interaction interface for users and doctors to view, and adopt corresponding optimization solutions based on the value of the remission coefficient; The storage module is used to store all data.

[0007] Preferably, the real-time acquisition module includes a body data acquisition unit and a scanning data acquisition unit, the body data acquisition unit is used to collect the user's body data, and the scanning data acquisition unit is used to collect the user's scanning data; the data preprocessing module includes a body data preprocessing unit and a scanning data preprocessing unit, the body data preprocessing unit is used to receive data from the body data acquisition unit and perform preprocessing operations on the body data, and the scanning data preprocessing unit is used to receive data from the scanning data acquisition unit and perform preprocessing operations on the scanning data.

[0008] Preferably, the body data analysis unit is used to perform real-time analysis on the user's body data, obtain the user's fitness coefficient, and make a fitness judgment, and send interactive instructions to the interactive module based on the judgment result; the scan data analysis unit is used to construct a neural network model to analyze the user's scan data, obtain the diagnosis result and transmit it to the feedback module.

[0009] Preferably, the specific method of the body data analysis unit to obtain the user fitness coefficient is: Step S01: Calculate the heart rate fluctuation factor: The calculation formula is expressed as: Among them, B h is the heart rate fluctuation factor, h t is the user's heart rate at the tth moment, is the user's average heart rate within T moments, ; t=1, 2, 3, …, T; Step S02: Calculate the blood pressure fluctuation factor: The calculation formula is expressed as: Among them, B b is the blood pressure fluctuation factor, b t is the user's blood pressure at the tth moment, is the average blood pressure of the user in T moments, ; t=1, 2, 3, …, T; Step S03: Calculate the body temperature fluctuation factor: The calculation formula is expressed as: Among them, B w is the body temperature fluctuation factor, w t is the user's body temperature at the tth moment, is the average body temperature of the user in T time periods, ; t=1, 2, 3, …, T; Step S04: Calculate the user fitness coefficient: Based on the heart rate fluctuation factor of step S01, the blood pressure fluctuation factor of step S02, and the body temperature fluctuation factor of step S03, a comprehensive analysis is performed on the user fitness coefficient, and the calculation formula is expressed as: , where δ B is the user fitness coefficient, λ1 is the weight parameter of the heart rate fluctuation factor, λ2 is the weight parameter of the heart rate fluctuation factor, and λ3 is the weight parameter of the body temperature fluctuation factor.

[0010] Preferably, the calculation formulas for the weight parameter λ1 of the heart rate fluctuation factor, the weight parameter λ2 of the blood pressure fluctuation factor, and the weight parameter λ3 of the body temperature fluctuation factor in step S04 are respectively expressed as: , where h t is the user's heart rate at the tth moment, T is the total time, t=1, 2, 3, ..., T; , where b t is the user's blood pressure at the tth moment, T is the total time, t=1, 2, 3, ..., T; , where w t is the user's body temperature at the tth moment, T is the total time, t=1, 2, 3, …, T.

[0011] Preferably, the specific process of the physical data analysis unit performing fitness judgment is: If the user fitness coefficient satisfies δ B ≥YU1, it is judged that the user's fitness is high and reaches the set threshold requirement. At this time, there is no need to send interactive instructions to the interactive module; If the user fitness coefficient satisfies δ B <YU1, it is judged that the user's adaptability is poor and does not meet the set threshold requirement, and an interactive instruction is sent to the interactive module; The YU1 is the instruction threshold.

[0012] Preferably, the feedback module calculates the mitigation coefficient in the following specific manner: The time when the interactive module receives the interactive instruction is recorded as T+1, and the time when the user finishes the medical imaging examination is recorded as T+N. The user fitness coefficient from T+1 to T+N can be expressed as: , where δ B ´ is the user fitness coefficient from T+1 to T+N, B h ´ is the heart rate fluctuation factor from T+1 to T+N, Bb ´ is the blood pressure fluctuation factor from T+1 to T+N, B w ´ is the temperature fluctuation factor from T+1 to T+N, λ1 ´ is the weight parameter of the heart rate fluctuation factor from time T+1 to time T+N, λ2 ´ is the weight parameter of the heart rate fluctuation factor from time T+1 to time T+N, λ3 ´ is the weight parameter of the body temperature fluctuation factor from T+1 to T+N; The mitigation coefficient can be expressed as: , where ζ is the mitigation coefficient, δ B is the user fitness coefficient.

[0013] Preferably, the B h ´ , B b ´ , B w ´ ,λ1 ´ ,λ2 ´ ,λ3 ´ The calculation formulas are: ; Among them, h T+i is the user's heart rate at the T+ith moment, is the user's average heart rate from time T+1 to time T+N, ; i = 1, 2, 3, ..., N; ; Among them, b T+i is the user's heart rate at the T+ith moment, is the user's average heart rate from time T+1 to time T+N, ; i = 1, 2, 3, ..., N; ; Among them, w T+i is the user's heart rate at the T+ith moment, is the user's average heart rate from time T+1 to time T+N, ; i = 1, 2, 3, ..., N; , , .

[0014] Technical effects and advantages of the present invention: The present invention is provided with a synchronous analysis module, an interactive module and a feedback module, which is conducive to obtaining the user's fitness coefficient and making a fitness judgment through real-time analysis of the user's body data, and sending an interactive instruction to the interactive module according to the judgment result; constructing a neural network model to analyze the user's scanned data, obtaining the diagnosis result and transmitting it to the feedback module; the interactive module can provide the user with an interactive operation interface by receiving the interactive instruction of the body data analysis unit to alleviate the discomfort caused by the user, the feedback module calculates the relief coefficient and adopts a corresponding optimization plan based on the value of the relief coefficient, diagnoses the user through VR technology, combines VR technology with medical imaging examination, provides patients with a more convenient and comfortable examination experience, and can meet the training needs of medical imaging examination, and analyzes the user's fitness during the medical imaging examination. When the user may experience discomfort, the user's tension and other emotions are relieved through the interactive module to improve the user's fitness, so that the scanning work of the scanning device continues, and the scanning effect is guaranteed. While diagnosing, the user's emotions are taken into account to prevent some patients from having poor scanning effects due to their own psychological reasons. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a structural diagram of the simulation diagnosis system based on VR technology of the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are merely illustrative. The VR-based simulation diagnostic system involved in the present invention is not limited to the various structures recorded in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0017] The present invention provides a simulation diagnosis system based on VR technology, including a virtual environment construction module, a control module, a real-time acquisition module, a data preprocessing module, a synchronous analysis module, an interactive module, a feedback module and a storage module; The virtual environment construction module generates a virtual medical imaging examination environment, i.e., a virtual environment, based on a real medical imaging examination environment; collects three-dimensional point cloud data of the real medical imaging examination environment, performs 3D modeling to construct a three-dimensional model, and forms a virtual medical imaging examination environment; the medical imaging examination environment includes a medical imaging scanning device, an external environment, and device scanning parameters. The medical imaging scanning device may be any one of scanning devices such as CT or MRI, and the device scanning parameters include but are not limited to the device scanning range and layer thickness, etc. The external environment is the surrounding environment of the scanning device; the virtual environment construction module constructs a virtual medical imaging examination environment as an existing technical means, and this embodiment will not be described in detail here; The control module is used to exchange data between the VR device and the virtual environment, and the user can select the medical imaging device in the virtual environment to perform imaging examination; the VR device is not limited to input devices such as head-mounted displays, handles, gloves, etc.; The real-time acquisition module is used to acquire the user's body data and scan data in real time through an acquisition device; the acquisition device includes but is not limited to various sensors and scanning devices in the virtual environment; the body data includes but is not limited to the user's heartbeat, blood pressure, body temperature and other data, and the scan data is the medical image scanned by the user through a medical imaging scanning device in the virtual environment; the real-time acquisition module includes a body data acquisition unit and a scan data acquisition unit, the body data acquisition unit is used to acquire the user's body data, and the scan data acquisition unit is used to acquire the user's scan data; The data preprocessing module is used to preprocess the data of the real-time acquisition module and transmit it to the synchronous analysis module; the data preprocessing module includes a body data preprocessing unit and a scan data preprocessing unit, the body data preprocessing unit is used to receive the data of the body data acquisition unit and preprocess the body data, and the scan data preprocessing unit is used to receive the data of the scan data acquisition unit and preprocess the scan data; The synchronous analysis module is used to receive and analyze the data preprocessed by the data preprocessing module, and the synchronous analysis module includes a body data analysis unit and a scan data analysis unit; the body data analysis unit is used to perform real-time analysis on the user's body data, obtain the user's fitness coefficient, and make fitness judgments, and send interactive instructions to the interactive module according to the judgment results; the scan data analysis unit is used to construct a neural network model to analyze the user's scan data, obtain the diagnosis results and transmit them to the feedback module; The interactive module is used to receive interactive instructions from the body data analysis unit and provide an interactive operation interface to the user. The interactive operation interface is a voice dialogue interface with the doctor. At this time, the user can communicate with the doctor through the interactive operation interface to alleviate adverse reactions and bring psychological comfort to the user; The feedback module is used to calculate the remission coefficient, and at the same time feed back the diagnosis result to the human-computer interaction interface for users and doctors to view, and adopt corresponding optimization solutions based on the value of the remission coefficient; The storage module is used to store all data.

[0018] In this embodiment, it should be specifically explained that the specific manner in which the body data analysis unit obtains the user fitness coefficient is: Step S01: Calculate the heart rate fluctuation factor: Calculate the fluctuation of the user's heart rate as the basis for judging whether the user is adaptable during the medical imaging scan. The calculation formula is expressed as: Among them, B h is the heart rate fluctuation factor, h t is the user's heart rate at the tth moment, is the user's average heart rate within T moments, ; t=1, 2, 3, …, T; Heart rate fluctuation factor B h The larger the value, the more unstable the heart rate is, so the user is more likely to experience discomfort such as nervousness, and the user's fitness is lower; Step S02: Calculate the blood pressure fluctuation factor: Calculate the fluctuation of the user's blood pressure as the basis for judging whether the user is adaptable during the medical imaging scan. The calculation formula is expressed as: Among them, B b is the blood pressure fluctuation factor, b t is the user's blood pressure at the tth moment, is the average blood pressure of the user in T moments, ; t=1, 2, 3, …, T; Blood pressure fluctuation factor B b The larger the value, the more unstable the blood pressure is, so the user is more likely to experience discomfort such as tension, and the user's fitness is lower; Step S03: Calculate the body temperature fluctuation factor: Calculate the fluctuation of the user's body temperature as the basis for judging whether the user is adaptable during the medical imaging scan. The calculation formula is expressed as: Among them, B w is the body temperature fluctuation factor, w t is the user's body temperature at the tth moment, is the average body temperature of the user in T time periods, ; t=1, 2, 3, …, T; Temperature fluctuation factor B w The larger the value, the more unstable the body temperature is, so the user is more likely to experience discomfort such as nervousness, and the user's fitness is lower; Step S04: Calculate the user fitness coefficient: Based on the heart rate fluctuation factor of step S01, the blood pressure fluctuation factor of step S02, and the body temperature fluctuation factor of step S03, a comprehensive analysis is performed on the user fitness coefficient, and the calculation formula is expressed as: , where δ B is the user fitness coefficient, λ1 is the weight parameter of the heart rate fluctuation factor, λ2 is the weight parameter of the heart rate fluctuation factor, and λ3 is the weight parameter of the body temperature fluctuation factor; The user fitness coefficient δ B The smaller the value is, the greater the fluctuations in the user's heart rate, blood pressure, and body temperature are, that is, the higher the possibility of maladaptation. Comprehensive analysis of the three aspects of data can increase comprehensiveness and thus improve accuracy. For example, if the user's own blood pressure is high, even if the blood pressure rises due to maladaptation during the image scanning process, the data fluctuation will not be too large, that is, the value of the blood pressure fluctuation factor will not be too large, but the fluctuations in heart rate and body temperature caused by maladaptation can still keep the final result accurate.

[0019] In this embodiment, it should be specifically explained that the calculation formulas of the weight parameter λ1 of the heart rate fluctuation factor, the weight parameter λ2 of the blood pressure fluctuation factor, and the weight parameter λ3 of the body temperature fluctuation factor in step S04 are respectively expressed as: , where h t is the user's heart rate at the tth moment, T is the total time, t=1, 2, 3, ..., T; , where b t is the user's blood pressure at the tth moment, T is the total time, t=1, 2, 3, ..., T; , where w t is the user's body temperature at the t-th moment, T is the total time, t=1, 2, 3, ..., T; By obtaining the corresponding weight parameters of the heart rate fluctuation factor, the heart rate fluctuation factor and the body temperature fluctuation factor, the user fitness coefficient can be analyzed based on the weight of each factor when calculating, thereby improving accuracy.

[0020] In this embodiment, it should be specifically explained that the neural network model constructed by the scan data analysis unit for analyzing the user's scan data is a convolutional neural network, including an input layer, a convolution layer, a pooling layer, a fully connected layer, and an output layer; The input data is the user's scanned data. Multiple convolutional layers and pooling layers are constructed to extract the features of the image. The convolutional layer uses the convolution operation to extract the features of the input data. The pooling layer is usually added after the convolutional layer. The pooling operation helps to reduce the size of the feature map and improve the computational efficiency of the model. An activation function, such as the Sigmoid function or the Tanh function, is added after the convolution operation. The pooling layer uses pooling operation and uses downsampling to reduce the dimension of the feature map, thereby reducing the amount of model calculation, improving the model's calculation efficiency, and enhancing the model's translation invariance to input data. The pooling operation can use maximum pooling or average pooling. Maximum pooling selects the maximum value in each pooling window as the output, uses maximum pooling to retain the most significant features and reduce the dimension of the feature map. Average pooling calculates the average value of each pooling window feature as the output. The size and stride of the pooling window can be selected according to the size of the feature map and the requirements of the model. The fully connected layer flattens the features extracted by the convolutional layer and connects them to the output layer for classification or regression tasks. Feature flattening means that after the convolutional layer extracts the features, the fully connected layer is used to flatten these features into a vector as input, converting the two-dimensional or three-dimensional feature map into a one-dimensional vector so that it can be input into the fully connected layer for subsequent processing; The output layer is used to output the final diagnosis result, that is, the disease category existing in the medical image.

[0021] In this embodiment, it should be specifically explained that the specific process of the physical data analysis unit performing fitness judgment is as follows: If the user fitness coefficient satisfies δ B ≥YU1, it is judged that the user's adaptability is high and reaches the set threshold requirement. At this time, the user can adapt to the medical imaging examination process and does not need to send interactive instructions to the interactive module; If the user fitness coefficient satisfies δ B <YU1, it is judged that the user's adaptability is poor and does not meet the set threshold requirement. At this time, the user is likely to be uncomfortable during the medical imaging examination. At this time, an interactive instruction is sent to the interactive module to promptly alleviate the user's possible discomfort; The YU1 is an instruction threshold, which satisfies YU1 ≥ 80%. This embodiment does not specifically limit this specific value.

[0022] In this embodiment, it should be specifically explained that the specific method in which the feedback module calculates the mitigation coefficient is: The time when the interactive module receives the interactive instruction is recorded as T+1, and the time when the user finishes the medical imaging examination is recorded as T+N. The user fitness coefficient from T+1 to T+N can be expressed as: , where δ B´ is the user fitness coefficient from T+1 to T+N, B h ´ is the heart rate fluctuation factor from T+1 to T+N, B b ´ is the blood pressure fluctuation factor from T+1 to T+N, B w ´ is the temperature fluctuation factor from T+1 to T+N, λ1 ´ is the weight parameter of the heart rate fluctuation factor from time T+1 to time T+N, λ2 ´ is the weight parameter of the heart rate fluctuation factor from time T+1 to time T+N, λ3 ´ is the weight parameter of the body temperature fluctuation factor from T+1 to T+N; The calculation formulas are: ; Among them, h T+i is the user's heart rate at the T+ith moment, is the user's average heart rate from time T+1 to time T+N, ; i = 1, 2, 3, ..., N; ; Among them, b T+i is the user's heart rate at the T+ith moment, is the user's average heart rate from time T+1 to time T+N, ; i = 1, 2, 3, ..., N; ; Among them, w T+i is the user's heart rate at the T+ith moment, is the user's average heart rate from time T+1 to time T+N, ; i = 1, 2, 3, ..., N; , , ; The mitigation coefficient can be expressed as: , where ζ is the relief coefficient. The larger the value of the relief coefficient, the better the relief effect brought by the interactive module to the user, and the user's discomfort gradually decreases.

[0023] In this embodiment, it should be specifically explained that the specific manner in which the feedback module adopts the corresponding optimization scheme based on the mitigation coefficient is: If the value of the relief coefficient is a positive number, the interactive content of the interactive module is continuously used and appropriately optimized. If the value of the relief coefficient is a negative number, the interactive content of the interactive module is changed, and the interactive operation interface can be replaced. For example, the interactive operation interface is changed from a voice dialogue interface with a doctor to a soothing music interface.

[0024] In this embodiment, it should be specifically explained that a method of using a simulation diagnosis system based on VR technology includes the following steps: A1: Use VR technology to build a virtual medical imaging examination environment; A2: The user enters a virtual medical imaging examination environment through a head-mounted device (such as a VR helmet) and selects a scanning device for scanning; A3: The system collects and analyzes the user's data, and obtains the diagnosis results through the real-time acquisition module, data preprocessing module, synchronous analysis module, interactive module and feedback module. At the same time, during the diagnosis process, when the medical imaging examination is performed through the scanning device, the user's fitness will be analyzed. When the user may feel uncomfortable, the interactive module will be used to relieve the user's tension and other emotions to improve the user's fitness, so that the scanning device can continue scanning and ensure the scanning effect. The user's emotions are taken into account while diagnosing.

[0025] In this embodiment, it should be specifically explained that the difference between this embodiment and the prior art is mainly that this embodiment has a synchronous analysis module, an interactive module and a feedback module, which perform real-time analysis on the user's body data, obtain the user's fitness coefficient, and make a fitness judgment, and send an interactive instruction to the interactive module based on the judgment result; construct a neural network model to analyze the user's scanned data, obtain the diagnosis result and transmit it to the feedback module; the interactive module can provide the user with an interactive operation interface by receiving the interactive instruction of the body data analysis unit to alleviate the discomfort caused by the user, the feedback module calculates the relief coefficient and adopts a corresponding optimization plan based on the value of the relief coefficient, diagnoses the user through VR technology, combines VR technology with medical imaging examination, provides patients with a more convenient and comfortable examination experience, and can meet the training needs of medical imaging examination, and analyzes the user's fitness during the medical imaging examination. When the user may experience discomfort, the user's tension and other emotions are relieved through the interactive module to improve the user's fitness, so that the scanning work of the scanning device continues, and the scanning effect is guaranteed. While diagnosing, the user's emotions are taken into account to prevent some patients from having poor scanning effects due to their own psychological reasons.

[0026] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0027] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A simulation diagnosis system based on VR technology, characterized by: It includes a virtual environment construction module, a control module, a real-time acquisition module, a data preprocessing module, a synchronous analysis module, an interactive module, a feedback module and a storage module; The virtual environment construction module generates a virtual medical imaging examination environment, i.e., a virtual environment, based on a real medical imaging examination environment; The control module is used to exchange data between the VR device and the virtual environment, and the user can select the medical imaging device in the virtual environment to perform imaging examination; The real-time acquisition module is used to acquire the user's body data and scan data in real time through an acquisition device; The data preprocessing module is used to preprocess the data of the real-time acquisition module and transmit it to the synchronization analysis module; The synchronous analysis module is used to receive and analyze the data preprocessed by the data preprocessing module, and the synchronous analysis module includes a body data analysis unit and a scan data analysis unit; The interactive module is used to receive interactive instructions from the body data analysis unit and provide an interactive operation interface to the user; The feedback module is used to calculate the remission coefficient, and at the same time feed back the diagnosis result to the human-computer interaction interface for users and doctors to view, and adopt corresponding optimization solutions based on the value of the remission coefficient; The storage module is used to store all data.

2. The VR-based simulation diagnostic system according to claim 1, characterized in that: The real-time acquisition module includes a body data acquisition unit and a scanning data acquisition unit. The body data acquisition unit is used to collect the user's body data, and the scanning data acquisition unit is used to collect the user's scanning data; the data preprocessing module includes a body data preprocessing unit and a scanning data preprocessing unit. The body data preprocessing unit is used to receive data from the body data acquisition unit and perform preprocessing operations on the body data, and the scanning data preprocessing unit is used to receive data from the scanning data acquisition unit and perform preprocessing operations on the scanning data.

3. The VR-based simulation diagnosis system according to claim 1, characterized in that: The body data analysis unit is used to perform real-time analysis on the user's body data, obtain the user's fitness coefficient, and make a fitness judgment, and send interactive instructions to the interactive module based on the judgment result; the scan data analysis unit is used to construct a neural network model to analyze the user's scan data, obtain the diagnosis result and transmit it to the feedback module.

4. The VR-based simulation diagnosis system according to claim 3, characterized in that: The specific method for the body data analysis unit to obtain the user fitness coefficient is: Step S01: Calculate the heart rate fluctuation factor: The calculation formula is expressed as: Among them, B h is the heart rate fluctuation factor, h t is the user's heart rate at the tth moment, is the user's average heart rate within T moments, ; t=1, 2, 3, …, T; Step S02: Calculate the blood pressure fluctuation factor: The calculation formula is expressed as: Among them, B b is the blood pressure fluctuation factor, b t is the user's blood pressure at the tth moment, is the average blood pressure of the user in T moments, ; t=1, 2, 3, …, T; Step S03: Calculate the body temperature fluctuation factor: The calculation formula is expressed as: Among them, B w is the body temperature fluctuation factor, w t is the user's body temperature at the tth moment, is the average body temperature of the user in T time periods, ; t=1, 2, 3, …, T; Step S04: Calculate the user fitness coefficient: Based on the heart rate fluctuation factor of step S01, the blood pressure fluctuation factor of step S02, and the body temperature fluctuation factor of step S03, a comprehensive analysis is performed on the user fitness coefficient, and the calculation formula is expressed as: , where δ B is the user fitness coefficient, λ1 is the weight parameter of the heart rate fluctuation factor, λ2 is the weight parameter of the heart rate fluctuation factor, and λ3 is the weight parameter of the body temperature fluctuation factor.

5. The VR-based simulation diagnosis system according to claim 4, characterized in that: In step S04, the calculation formulas for the weight parameter λ1 of the heart rate fluctuation factor, the weight parameter λ2 of the blood pressure fluctuation factor, and the weight parameter λ3 of the body temperature fluctuation factor are respectively expressed as: , where h t is the user's heart rate at the tth moment, T is the total time, t=1, 2, 3, ..., T; , where b t is the user's blood pressure at the tth moment, T is the total time, t=1, 2, 3, ..., T; , where w t is the user's body temperature at the tth moment, T is the total time, t=1, 2, 3, …, T.

6. The VR-based simulation diagnosis system according to claim 3, characterized in that: The specific process of fitness determination by the body data analysis unit is as follows: If the user fitness coefficient satisfies δ B ≥YU1, it is judged that the user's fitness is high and reaches the set threshold requirement. At this time, there is no need to send interactive instructions to the interactive module; If the user fitness coefficient satisfies δ B <YU1, it is judged that the user's adaptability is poor and does not meet the set threshold requirement, and an interactive instruction is sent to the interactive module; The YU1 is the instruction threshold.

7. The VR-based simulation diagnosis system according to claim 1, characterized in that: The specific method for calculating the mitigation coefficient by the feedback module is: The time when the interactive module receives the interactive instruction is recorded as T+1, and the time when the user finishes the medical imaging examination is recorded as T+N. The user fitness coefficient from T+1 to T+N can be expressed as: , where δ B ´ is the user fitness coefficient from T+1 to T+N, B h ´ is the heart rate fluctuation factor from T+1 to T+N, B b ´ is the blood pressure fluctuation factor from T+1 to T+N, B w ´ is the temperature fluctuation factor from T+1 to T+N, λ1 ´ is the weight parameter of the heart rate fluctuation factor from time T+1 to time T+N, λ2 ´ is the weight parameter of the heart rate fluctuation factor from time T+1 to time T+N, λ3 ´ is the weight parameter of the body temperature fluctuation factor from T+1 to T+N; The mitigation coefficient can be expressed as: , where ζ is the mitigation coefficient, δ B is the user fitness coefficient.

8. The VR-based simulation diagnosis system according to claim 7, characterized in that: B h ´ , B b ´ , B w ´ ,λ1 ´ ,λ2 ´ ,λ3 ´ The calculation formulas are: ; Among them, h T+i is the user's heart rate at the T+ith moment, is the user's average heart rate from time T+1 to time T+N, ; i = 1, 2, 3, ..., N; ; Among them, b T+i is the user's heart rate at the T+ith moment, is the user's average heart rate from time T+1 to time T+N, ; i = 1, 2, 3, ..., N; ; Among them, w T+i is the user's heart rate at the T+ith moment, is the user's average heart rate from time T+1 to time T+N, ; i = 1, 2, 3, ..., N; ， ， 。

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