A simulation diagnosis system based on VR technology

Abstract

The present invention relates to the field of VR technology, and discloses a simulation diagnosis system based on VR technology, 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; by providing the synchronous analysis module, the interactive module and the feedback module, the user is diagnosed through the VR technology, the VR technology is combined with the medical imaging examination, a more convenient and comfortable examination experience is provided for the patient, and the training requirements of the medical imaging examination can be met, and at the same time, the user's fitness during the medical imaging examination is analyzed, and 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, the scanning effect is guaranteed, and the user's emotions are taken into account during the diagnosis, so as to prevent the phenomenon that some patients have poor scanning effects due to their own psychological reasons.

Description

Technical Field

[0001] The present invention relates to the field of VR technology, and more particularly 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 application of VR technology has brought a new diagnostic experience for doctors and patients; medical imaging examination is an important means of modern medical diagnosis. As an important auxiliary method of medical diagnosis, it can greatly improve the accuracy and diagnostic efficiency of diagnostic results.

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

[0004] Therefore, a simulation diagnosis system based on VR technology is needed to perform medical imaging examinations using VR technology, combine VR technology with medical imaging examinations, provide a more convenient and comfortable examination experience for patients, and meet the needs of medical imaging examination training, and provide more accurate and efficient diagnoses based on the medical imaging examination results obtained by 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 art.

[0006] The present invention provides the following technical solution: 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 interaction module, a feedback module, and a storage module;

[0007] 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;

[0008] The control module is used to perform data interaction between the VR device and the virtual environment, and the user can select a medical imaging device in the virtual environment for imaging examination;

[0009] The real-time acquisition module is used to collect the user's body data and scanning data in real time through a collection device;

[0010] The data preprocessing module is used to perform preprocessing operations on the data of the real-time acquisition module and transmit it to the synchronous analysis module;

[0011] The synchronization analysis module is used to receive the data preprocessed by the data preprocessing module for analysis. The synchronization analysis module includes a body data analysis unit and a scan data analysis unit;

[0012] The interaction module is used to receive the interaction instructions from the body data analysis unit and provide an interactive operation interface for the user;

[0013] The feedback module is used to calculate the relief coefficient, and at the same time feedback the diagnosis result to the human-computer interaction interface for the user and the doctor to view, and adopt corresponding optimization solutions based on the value of the relief coefficient;

[0014] The storage module is used to store all data.

[0015] Preferably, 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 body data of the user, and the scan data acquisition unit is used to acquire the scan data of the user; 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 from the body data acquisition unit and perform preprocessing operations on the body data, and the scan data preprocessing unit is used to receive the data from the scan data acquisition unit and perform preprocessing operations on the scan data.

[0016] Preferably, the body data analysis unit is used to perform real-time analysis on the body data of the user, obtain the user fitness coefficient, and perform fitness judgment, and send interaction instructions to the interaction module according to the judgment result; the scan data analysis unit is used to construct a neural network model to analyze the scan data of the user, obtain the diagnosis result and transmit it to the feedback module.

[0017] Preferably, the specific method for the body data analysis unit to obtain the user fitness coefficient is as follows:

[0018] Step S01: Calculate the heart rate fluctuation factor: The calculation formula is expressed as: ; where B h is the heart rate fluctuation factor, h t is the heart rate of the user at the t-th moment, is the average heart rate of the user within T moments, ; t = 1, 2, 3,..., T;

[0019] Step S02: Calculate the blood pressure fluctuation factor: The calculation formula is expressed as: ; where B b is the blood pressure fluctuation factor, b t is the blood pressure of the user at the t-th moment, is the average blood pressure of the user within T moments, ; t = 1, 2, 3, …, T;

[0020] Step S03: Calculate the body temperature fluctuation factor: The calculation formula is expressed as: ; where B w is the body temperature fluctuation factor, w t is the user's body temperature at the t-th moment, is the average body temperature of the user within T moments, ; t = 1, 2, 3, …, T;

[0021] Step S04: Calculate the user fitness coefficient: Based on the heart rate fluctuation factor in Step S01, the blood pressure fluctuation factor in Step S02, and the body temperature fluctuation factor in Step S03, conduct a comprehensive analysis of the user fitness coefficient. The calculation formula is expressed as: , where δ B is the user fitness coefficient, λ 1 is the weight parameter of the heart rate fluctuation factor, λ 2 is the weight parameter of the heart rate fluctuation factor, λ 3 is the weight parameter of the body temperature fluctuation factor.

[0022] Preferably, the weight parameter λ 1 of the heart rate fluctuation factor, the weight parameter λ 2 of the blood pressure fluctuation factor, and the weight parameter λ 3 of the body temperature fluctuation factor in Step S04 are respectively expressed by the following calculation formulas:

[0023] , where h t is the user's heart rate at the t-th moment, T is the total number of moments, t = 1, 2, 3, …, T;

[0024] , where b t is the user's blood pressure at the t-th moment, T is the total number of moments, t = 1, 2, 3, …, T;

[0025] , where w t is the user's body temperature at the t-th moment, T is the total number of moments, t = 1, 2, 3, …, T.

[0026] Preferably, the specific process of the body data analysis unit for fitness judgment is:

[0027] If the user fitness coefficient satisfies δ B ≥ YU1, it is determined that the user has a high fitness and meets the set threshold requirement. At this time, there is no need to send an interaction instruction to the interaction module;

[0028] If the user fitness coefficient satisfies δ BIf YU1 < YU1, it is determined that the user's fitness is poor and does not meet the set threshold requirement. At this time, an interaction instruction is sent to the interaction module;

[0029] The YU1 is the instruction threshold.

[0030] Preferably, the specific method for the feedback module to calculate the mitigation coefficient is:

[0031] Record the moment when the interaction module receives the interaction instruction as T + 1, and the moment when the user ends the medical image examination as T + N. Then 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 body temperature fluctuation factor from T + 1 to T + N, λ 1 ´ is the weight parameter of the heart rate fluctuation factor from T + 1 to T + N, λ 2 ´ is the weight parameter of the heart rate fluctuation factor from T + 1 to T + N, λ 3 ´ is the weight parameter of the body temperature fluctuation factor from T + 1 to T + N;

[0032] Then the mitigation coefficient can be expressed as:

[0033] , where ζ is the mitigation coefficient, δ B is the user fitness coefficient.

[0034] Preferably, the calculation formulas of the B h ´ , B b ´ , B w ´ , λ 1 ´ , λ 2 ´ , λ 3 ´ are respectively:

[0035] ; where h T+i is the heart rate of the user at the T + i-th moment, is the average heart rate of the user from T + 1 to T + N moments, ; i = 1, 2, 3, …, N;

[0036] ; where, b T+i is the heart rate of the user at the (T + i)-th moment, is the average heart rate of the user within the time periods from T + 1 to T + N, ; i = 1, 2, 3, …, N;

[0037] ; where, w T+i is the heart rate of the user at the (T + i)-th moment, is the average heart rate of the user within the time periods from T + 1 to T + N, ; i = 1, 2, 3, …, N;

[0038] , , .

[0039] Technical effects and advantages of the present invention:

[0040] By providing a synchronous analysis module, an interaction module, and a feedback module, the present invention is conducive to analyzing the user's physical data in real time, obtaining the user's fitness coefficient, and performing fitness judgment, and sending an interaction instruction to the interaction module according to the judgment result; constructing a neural network model to analyze the user's scan data, obtaining a diagnosis result and transmitting it to the feedback module; the interaction module can provide an interaction operation interface to the user by receiving the interaction instruction from the physical data analysis unit to relieve the discomfort phenomenon generated by the user, and the feedback module calculates a relief coefficient and adopts a corresponding optimization scheme based on the value of the relief coefficient. By diagnosing the user through VR technology and combining VR technology with medical imaging examination, a more convenient and comfortable examination experience is provided for patients, and the training requirements of medical imaging examination can be met. At the same time, the fitness of the user during the medical imaging examination is analyzed. When the user shows a phenomenon that may cause discomfort, the interaction module relieves the user's nervousness and other emotions to improve the user's fitness, so that the scanning work of the scanning device can continue, ensuring the scanning effect, taking into account the user's emotions during the diagnosis, and preventing the phenomenon that the scanning effect is poor due to the user's own psychological reasons for some patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a structural diagram of the simulation diagnosis system based on VR technology of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. In addition, the forms of the various structures described in the following embodiments are merely examples, and a simulation diagnosis system based on VR technology involved in the present invention is not limited to the various structures described in the following embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0043] 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 interaction module, a feedback module, and a storage module.

[0044] The virtual environment construction module generates a virtual medical imaging examination environment based on the real medical imaging examination environment, that is, the virtual environment; collects the 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 can be any one of scanning devices such as CT or magnetic resonance. The device scanning parameters include but are not limited to device scanning range and slice thickness, etc. The external environment is the surrounding environment where the scanning device is located; the virtual environment construction module builds the virtual medical imaging examination environment by means of existing technology, and this embodiment will not elaborate too much here.

[0045] The control module is used to perform data interaction between the VR device and the virtual environment, and the user can select a medical imaging device in the virtual environment for imaging examination; the VR device includes but is not limited to input devices such as a head-mounted display, a handle, and gloves.

[0046] The real-time acquisition module is used to collect the user's body data and scanning data in real time through the 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 data such as the user's heartbeat, blood pressure, and body temperature, and the scanning data is the medical image scanned by the user through the medical imaging scanning device in the virtual environment; 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.

[0047] 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;

[0048] The synchronous analysis module is used to analyze the data preprocessed by the data preprocessing module. The synchronous analysis module includes a body data analysis unit and a scan data analysis unit; the body data analysis unit is used to analyze the user's body data in real time, obtain the user fitness coefficient, and perform fitness judgment, and send an interaction instruction to the interaction module according to 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;

[0049] The interaction module is used to receive the interaction instruction of the body data analysis unit and provide an interaction operation interface for the user. The interaction operation interface is a voice dialogue interface with the doctor. At this time, the user can communicate with the doctor through the interaction operation interface to relieve adverse reactions and bring psychological comfort to the user;

[0050] The feedback module is used to calculate the relief coefficient, and at the same time feedback the diagnosis result to the human-computer interaction interface for the user and the doctor to view, and adopt corresponding optimization solutions based on the value of the relief coefficient;

[0051] The storage module is used to store all data.

[0052] In this embodiment, it should be specifically noted that the specific method for the body data analysis unit to obtain the user fitness coefficient is as follows:

[0053] Step S01: Calculate the heart rate fluctuation factor: By calculating the fluctuation of the user's heart rate as the basis for judging whether the user is adapted during the medical imaging scan examination, the calculation formula is expressed as: ; where B h is the heart rate fluctuation factor, h t is the user's heart rate at the t-th moment, is the average heart rate within T moments of the user, ; t = 1, 2, 3,..., T;

[0054] The larger the value of the heart rate fluctuation factor B h , the more unstable the heart rate, so the higher the possibility that the user has discomfort such as tension, and thus the lower the user's fitness;

[0055] Step S02: Calculate the blood pressure fluctuation factor: By calculating 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: ; where, B b is the blood pressure fluctuation factor, b t is the user's blood pressure at the t-th moment, is the average blood pressure of the user within T moments, ; t = 1, 2, 3, …, T;

[0056] The larger the value of the blood pressure fluctuation factor B b , the more unstable the blood pressure, so the higher the possibility that the user has discomfort such as nervousness, and thus the lower the adaptability of the user;

[0057] Step S03: Calculate the body temperature fluctuation factor: By calculating 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: ; where, B w is the body temperature fluctuation factor, w t is the user's body temperature at the t-th moment, is the average body temperature of the user within T moments, ; t = 1, 2, 3, …, T;

[0058] The larger the value of the body temperature fluctuation factor B w , the more unstable the body temperature, so the higher the possibility that the user has discomfort such as nervousness, and thus the lower the adaptability of the user;

[0059] Step S04: Calculate the user adaptability coefficient: Based on the heart rate fluctuation factor in Step S01, the blood pressure fluctuation factor in Step S02, and the body temperature fluctuation factor in Step S03, comprehensively analyze the user adaptability coefficient, and the calculation formula is expressed as: , where, δ B is the user adaptability coefficient, λ 1 is the weight parameter of the heart rate fluctuation factor, λ 2 is the weight parameter of the heart rate fluctuation factor, λ 3 is the weight parameter of the body temperature fluctuation factor;

[0060] The user adaptability coefficient δ BThe smaller the value is, it indicates that the fluctuations of the three data items of the user's heart rate, blood pressure, and body temperature are all large, that is, the possibility of discomfort occurs is higher. Through comprehensive analysis of the three aspects of data, comprehensiveness can be increased, thereby improving accuracy. Exemplarily, if the user's own blood pressure is relatively high, even if the blood pressure increases due to discomfort during the imaging scan, the data fluctuation will not be too large, that is, the value of the blood pressure fluctuation factor will not be too large. However, the fluctuations in heart rate and body temperature caused by discomfort can still ensure the accuracy of the final result.

[0061] In this embodiment, it should be specifically noted that the weight parameter λ of the heart rate fluctuation factor in step S04 1 , the weight parameter λ of the blood pressure fluctuation factor 2 , and the weight parameter λ of the body temperature fluctuation factor 3 are respectively expressed by the following calculation formulas:

[0062] , where h t is the heart rate of the user at the t-th moment, T is the total number of moments, and t = 1, 2, 3, …, T;

[0063] , where b t is the blood pressure of the user at the t-th moment, T is the total number of moments, and t = 1, 2, 3, …, T;

[0064] , where w t is the body temperature of the user at the t-th moment, T is the total number of moments, and t = 1, 2, 3, …, T;

[0065] By obtaining the corresponding weight parameters of the heart rate fluctuation factor, heart rate fluctuation factor, and body temperature fluctuation factor, when calculating the user's fitness coefficient, analysis can be carried out based on the weights of each factor, improving the accuracy.

[0066] In this embodiment, it should be specifically noted 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 convolutional layer, a pooling layer, a fully connected layer, and an output layer;

[0067] The input data is the user's scan data. Multiple convolutional layers and pooling layers are constructed to extract the features of the image. The convolutional layer uses convolutional operations to extract the features of the input data. Usually, a pooling layer is added after the convolutional layer. The pooling operation helps to reduce the size of the feature map, improve the computational efficiency of the model, and an activation function, such as the Sigmoid function or Tanh function, is added after the convolutional operation;

[0068] The pooling layer uses pooling operations to reduce the dimension of the feature map by downsampling, thereby reducing the computational amount of the model, improving the computational efficiency of the model, and enhancing the translational invariance of the model to the input data. The pooling operation can use max pooling or average pooling. Max pooling selects the maximum value in each pooling window as the output, uses max pooling to retain the most significant features, and reduces the dimension of the feature map. Average pooling calculates the average value of the features in each pooling window 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;

[0069] 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 features are extracted by the convolutional layer, the fully connected layer flattens these features into a vector as the input, converting the two-dimensional or three-dimensional feature map into a one-dimensional vector for subsequent processing in the fully connected layer;

[0070] The output layer is used to output the final diagnosis result, that is, the disease category existing in the medical image.

[0071] In this embodiment, it should be specifically noted that the specific process of the body data analysis unit for fitness judgment is as follows:

[0072] If the user's fitness coefficient satisfies δ B ≥YU1, it is determined that the user's fitness is relatively high and meets the set threshold requirement. At this time, the user can adapt to this medical image examination process and does not need to send an interaction instruction to the interaction module;

[0073] If the user's fitness coefficient satisfies δ B <YU1, it is determined that the user's fitness is relatively poor and does not meet the set threshold requirement. At this time, the user is more likely to be uncomfortable during the medical image examination process. At this time, an interaction instruction is sent to the interaction module to relieve the discomfort that the user may experience in a timely manner;

[0074] The YU1 is an instruction threshold, and YU1≥80% is satisfied. This embodiment does not specifically limit this specific value.

[0075] In this embodiment, it should be specifically noted that the specific method for the feedback module to calculate the relief coefficient is as follows:

[0076] Record the moment when the interaction module receives the interaction instruction as T+1, and the moment when the user ends the medical image examination as T+N. Then the user's fitness coefficient from T+1 to T+N can be expressed as: , where δ B ´ is the user's fitness coefficient from T+1 to T+N, and B h ´is the heart rate fluctuation factor from T+1 to T+N moments, B b ´ is the blood pressure fluctuation factor from T+1 to T+N moments, B w ´ is the body temperature fluctuation factor from T+1 to T+N moments, λ 1 ´ is the weight parameter of the heart rate fluctuation factor from T+1 to T+N moments, λ 2 ´ is the weight parameter of the heart rate fluctuation factor from T+1 to T+N moments, λ 3 ´ is the weight parameter of the body temperature fluctuation factor from T+1 to T+N moments;

[0077] The calculation formulas are respectively:

[0078] ; where h T+i is the heart rate of the user at the T+i-th moment, is the average heart rate of the user within the T+1 to T+N moments, ; i = 1, 2, 3,..., N;

[0079] ; where b T+i is the heart rate of the user at the T+i-th moment, is the average heart rate of the user within the T+1 to T+N moments, ; i = 1, 2, 3,..., N;

[0080] ; where w T+i is the heart rate of the user at the T+i-th moment, is the average heart rate of the user within the T+1 to T+N moments, ; i = 1, 2, 3,..., N;

[0081] , , ;

[0082] Then the relief coefficient can be expressed as:

[0083] , where ζ is the relief coefficient. The larger the value of the relief coefficient, the better the relief effect brought by the interaction module to the user, and the discomfort degree of the user gradually decreases.

[0084] In this embodiment, it should be specifically noted that the specific manner in which the feedback module adopts the corresponding optimization scheme based on the relief coefficient is:

[0085] 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.

[0086] 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:

[0087] A1: Use VR technology to build a virtual medical imaging examination environment;

[0088] 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;

[0089] 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.

[0090] 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.

[0091] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

[0092] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or replacements, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application shall be subject to 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; 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.

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: , where 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 6, characterized in that: 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; ， ， 。

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