Virtual Reality-Based Respiration Monitoring Simulation and Analysis System and Method

Abstract

The present invention discloses a breathing monitoring simulation analysis system and method based on virtual reality. The present invention obtains gas data and breathing movement abnormality analysis results during the breathing process of a patient, imports them into a breathing movement abnormality evaluation strategy for breathing movement abnormality evaluation, obtains the physiological parameter data of the patient, imports the physiological parameter data of the patient into a breathing effect abnormality analysis strategy for breathing effect abnormality analysis, imports the obtained breathing movement abnormality evaluation result and breathing effect abnormality analysis result into a breathing abnormality judgment strategy for breathing abnormality judgment, issues a breathing monitoring warning level according to the obtained breathing abnormality judgment result, and displays it on a virtual reality model to remind medical staff. The comprehensive physical sign data of the patient during the breathing process is collected, and the breathing movement and physiological parameter abnormalities of the patient are analyzed based on the physical signs of the patient during the breathing process, so as to comprehensively analyze and accurately monitor the abnormalities during the patient's breathing process.

Description

Technical Field

[0001] The present invention belongs to the technical field of healthcare, and particularly relates to a virtual reality-based respiratory monitoring simulation analysis system and method. Background Art

[0002] The application of virtual reality (VR) technology in the medical field is gradually increasing, including respiratory monitoring and training. Through VR technology, an immersive environment can be created to help doctors and patients better understand and train respiratory patterns. Doctors can remotely access the VR respiratory monitoring data of patients, monitor the respiratory conditions of patients in real time, and provide remote guidance and suggestions based on the data. This is particularly useful for patients with limited mobility or those who require long-term monitoring. Virtual reality technology can not only provide a more natural and interesting respiratory monitoring experience for patients, but also help doctors more accurately evaluate the respiratory conditions of patients and provide personalized respiratory training programs. With the progress of technology, the future application of virtual reality in respiratory monitoring and training will be more extensive and in-depth.

[0003] In the process of respiratory monitoring simulation analysis in the prior art, it is impossible to comprehensively analyze the abnormal respiratory movements and physiological parameters of patients based on the physical signs of patients during the respiratory process. Therefore, it is impossible to effectively comprehensively analyze and accurately monitor the abnormalities during the respiratory process of patients, resulting in a reduction in the accuracy and safety of respiratory monitoring. The above problems exist in the prior art;

[0004] To solve these problems, the present application designs a virtual reality-based respiratory monitoring simulation analysis system and method. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention proposes a virtual reality-based respiratory monitoring simulation analysis system and method. The present invention constructs a virtual reality model representing the physical sign data of a patient, obtains the respiratory movement data of the patient, imports it into a respiratory movement judgment strategy for abnormal analysis of respiratory movements, and demonstrates the respiratory movement data of the patient on the virtual reality model. The gas data and the abnormal analysis result of respiratory movements during the patient's breathing process are imported into a respiratory movement abnormality evaluation strategy for respiratory movement abnormality evaluation. The physiological parameter data of the patient is obtained, and the physiological parameter data of the patient is imported into a respiratory effect abnormal analysis strategy for respiratory effect abnormal analysis. The obtained respiratory movement abnormality evaluation result and respiratory effect abnormal analysis result are imported into a respiratory abnormality judgment strategy for respiratory abnormality judgment. According to the obtained respiratory abnormality judgment result, a respiratory monitoring warning level is issued, and a reminder is displayed on the virtual reality model for medical staff. The comprehensive physical sign data of the patient during the breathing process is collected, and the abnormal respiratory movements and physiological parameters of the patient are analyzed based on the comprehensive physical signs of the patient during the breathing process, so as to comprehensively analyze and accurately monitor the abnormalities during the patient's breathing process, improving the accuracy and safety of respiratory monitoring.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A virtual reality-based respiratory monitoring simulation analysis method, which includes the following specific steps:

[0008] S1. Collect the physical sign data of the patient through a data acquisition terminal, and construct a virtual reality model representing the physical sign data of the patient;

[0009] S2. Obtain the respiratory movement data of the patient, import it into a respiratory movement judgment strategy for abnormal analysis of respiratory movements, and demonstrate the respiratory movement data of the patient on the virtual reality model;

[0010] S3. Obtain the gas data and the abnormal analysis result of respiratory movements during the patient's breathing process, and import them into a respiratory movement abnormality evaluation strategy for respiratory movement abnormality evaluation;

[0011] S4. Obtain the physiological parameter data of the patient, and import the physiological parameter data of the patient into a respiratory effect abnormal analysis strategy for respiratory effect abnormal analysis;

[0012] S5. Import the obtained respiratory movement abnormality evaluation result and respiratory effect abnormal analysis result into a respiratory abnormality judgment strategy for respiratory abnormality judgment;

[0013] S6. Issue a respiratory monitoring warning level according to the obtained respiratory abnormality judgment result, and display a reminder on the virtual reality model for medical staff.

[0014] Specifically, the following specific steps are included in the S1:

[0015] S11. Collect respiratory motion data during respiration through a respiratory motion sensor carried at a specified position of the patient. The respiratory motion data includes the amplitude of thoracic cavity contraction and the amplitude of thoracic cavity expansion data, and is stored in the first storage component.

[0016] S12. Obtain gas data during the patient's respiration through an airflow sensor set at the nasal cavity position of the patient. The gas data includes the composition data of inhaled gas and the composition data of exhaled gas, and is stored in the second storage component.

[0017] S13. Obtain the patient's respiratory rate and respiratory gas volume data through a respiratory sensor, and store them in the third storage component.

[0018] S14. Construct a virtual reality model representing the patient's respiratory motion data, gas data during respiration, respiratory rate, and respiratory depth data.

[0019] Specifically, the specific steps of the respiratory motion judgment strategy in S2 are as follows:

[0020] S21. Obtain the respiratory motion data during the patient's respiration.

[0021] S22. Import the respiratory motion data during the i-th respiration into the respiratory motion outlier calculation formula to calculate the respiratory motion outlier value. The respiratory motion outlier value calculation formula for the i-th respiration is: , where a is the exhalation action proportion coefficient, ki is the amplitude of thoracic cavity contraction during the i-th respiration, km is the median of the safe range of thoracic cavity contraction amplitude during respiration, kmax is the maximum value of the safe range of thoracic cavity contraction amplitude during respiration, kmin is the minimum value of the safe range of thoracic cavity contraction amplitude during respiration, si is the thoracic cavity expansion amplitude data during the i-th respiration, sm is the median of the safe range of thoracic cavity expansion amplitude during respiration, smax is the maximum value of the safe range of thoracic cavity expansion amplitude during respiration, and smin is the minimum value of the safe range of thoracic cavity expansion amplitude during respiration.

[0022] Specifically, the specific steps of the respiratory motion abnormality evaluation strategy in S3 are as follows:

[0023] S31. Obtain the respiratory motion outlier values during all respirations within the set period obtained by calculation, and at the same time obtain the gas data during all respirations within the set period.

[0024] S32. Import the respiratory motion anomaly values in all respiratory processes within the set period and the gas data in all respiratory processes within the set period into the calculation formula for the respiratory motion anomaly evaluation value within the set period. The calculation formula for the respiratory motion anomaly evaluation value within the set period is as follows: , where exp() is the exponential power of e, ut is the respiratory rate within the set period, um is the median of the safe range of the respiratory rate, N is the number of breaths within the set period, M is the type of gas during the respiratory process, qij is the exhalation volume of the j-th type of gas during the i-th respiratory process, qjm is the median of the safe range of the exhalation volume of the j-th type of gas, pij is the inhalation volume of the j-th type of gas during the i-th respiratory process, and qjm is the median of the safe range of the inhalation volume of the j-th type of gas;

[0025] S33. Obtain the calculated respiratory motion anomaly evaluation value within the set period.

[0026] Specifically, the specific content of the respiratory effect anomaly analysis strategy in S4 is as follows:

[0027] S41. Obtain the physiological parameter data of the patient within the set period. The physiological parameter data of the patient includes blood oxygen, blood pressure, and body temperature data;

[0028] S42. Import the physiological parameter data of the patient within the set period into the calculation formula for the respiratory effect anomaly evaluation value to calculate the respiratory effect anomaly evaluation value. The calculation formula for the respiratory effect anomaly evaluation value is as follows: , where R is the type of physiological parameter data, dc is the proportion coefficient of the c-th type of physiological parameter data, fc is the average value of the c-th type of physiological parameter data of the patient within the set period, and fcm is the median of the safe range of the c-th type of physiological parameter data within the set period;

[0029] S43. Obtain the calculated respiratory effect anomaly evaluation value.

[0030] Specifically, the respiratory anomaly judgment strategy in S5 includes the following specific content:

[0031] S51. Obtain the calculated respiratory motion anomaly evaluation value and respiratory effect anomaly evaluation value within the set period;

[0032] S52. Import the calculated respiratory motion anomaly evaluation value and respiratory effect anomaly evaluation value within the set period into the calculation formula for the respiratory anomaly judgment value to calculate the respiratory anomaly judgment value. The calculation formula for the respiratory anomaly judgment value is as follows: , where b is the proportion coefficient of the respiratory motion anomaly evaluation.

[0033] Specifically, the specific steps of S6 are as follows: Obtain the calculated respiratory abnormality judgment value, and divide the calculated respiratory abnormality judgment value by the set respiratory abnormality judgment threshold to obtain a warning value;

[0034] If the value of the warning value is less than or equal to 0, no warning is issued;

[0035] If the value of the warning value is greater than 0 and less than or equal to 0.3, a level-three warning is issued;

[0036] If the value of the warning value is greater than 0.3 and less than or equal to 0.6, a level-two warning is issued;

[0037] If the value of the warning value is greater than 0.6, a level-one warning is issued;

[0038] Release the warning level of respiratory monitoring and display it on the virtual reality model to remind medical staff.

[0039] A respiratory monitoring simulation analysis system based on virtual reality is implemented based on the above-mentioned respiratory monitoring simulation analysis method based on virtual reality. It includes a data acquisition module, a respiratory movement abnormality analysis module, a respiratory movement abnormality evaluation module, a respiratory effect abnormality analysis module, a respiratory abnormality judgment module, a warning module, and a control module. Among them, the data acquisition module is used to collect the patient's physical sign data through a data acquisition terminal and construct a virtual reality model representing the patient's physical sign data;

[0040] The respiratory movement abnormality analysis module is used to obtain the patient's respiratory movement data, import it into the respiratory movement judgment strategy for respiratory movement abnormality analysis, and demonstrate the patient's respiratory movement data on the virtual reality model; The respiratory movement abnormality evaluation module is used to obtain the gas data and respiratory movement abnormality analysis results during the patient's breathing process and import them into the respiratory movement abnormality evaluation strategy for respiratory movement abnormality evaluation. The respiratory effect abnormality analysis module is used to obtain the patient's physiological parameter data and import the patient's physiological parameter data into the respiratory effect abnormality analysis strategy for respiratory effect abnormality analysis. The respiratory abnormality judgment module is used to import the obtained respiratory movement abnormality evaluation result and respiratory effect abnormality analysis result into the respiratory abnormality judgment strategy for respiratory abnormality judgment. The warning module is used to issue the warning level of respiratory monitoring according to the obtained respiratory abnormality judgment result and display it on the virtual reality model to remind medical staff.

[0041] Specifically, the control module is used to control the operation of the data acquisition module, the respiratory movement abnormality analysis module, the respiratory movement abnormality evaluation module, the respiratory effect abnormality analysis module, the respiratory abnormality judgment module, and the warning module.

[0042] An electronic device, comprising: a processor and a memory, wherein a computer program that can be called by the processor is stored in the memory;

[0043] The processor executes the above-mentioned virtual reality-based respiratory monitoring simulation analysis method by calling the computer program stored in the memory.

[0044] A computer-readable storage medium stores instructions, which when run on a computer cause the computer to execute the virtual reality-based respiratory monitoring simulation analysis method as described above.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] The present invention collects the physical sign data of the patient through a data collection terminal, constructs a virtual reality model representing the physical sign data of the patient, obtains the respiratory motion data of the patient, imports it into a respiratory motion judgment strategy for analyzing abnormal respiratory motions, and demonstrates the respiratory motion data of the patient on the virtual reality model. The gas data and the respiratory motion abnormal analysis results during the patient's breathing process are imported into a respiratory motion abnormal evaluation strategy for evaluating abnormal respiratory motions, obtains the physiological parameter data of the patient, imports the physiological parameter data of the patient into a respiratory effect abnormal analysis strategy for analyzing abnormal respiratory effects, and imports the obtained respiratory motion abnormal evaluation results and respiratory effect abnormal analysis results into a respiratory abnormality judgment strategy for judging respiratory abnormalities. According to the obtained respiratory abnormality judgment results, a respiratory monitoring warning level is issued, and a reminder is displayed on the virtual reality model for medical staff. The comprehensive physical sign data of the patient during the breathing process is collected, and the abnormal respiratory motions and physiological parameters of the patient are analyzed based on the comprehensive physical signs of the patient during the breathing process, thereby comprehensively analyzing and accurately monitoring the abnormalities during the patient's breathing process, improving the accuracy and safety of respiratory monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic flow chart of the virtual reality-based respiratory monitoring simulation analysis method of the present invention;

[0048] Figure 2 It is a schematic framework diagram of the virtual reality-based respiratory monitoring simulation analysis system of the present invention;

[0049] Figure 3 It is a schematic diagram of data transmission of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0051] Example 1

[0052] Please refer to Figure 1 , an embodiment provided by the present invention: a breathing monitoring simulation analysis method based on virtual reality, which includes the following specific steps:

[0053] S1. Collect the patient's physical sign data through a data collection terminal, and construct a virtual reality model representing the patient's physical sign data;

[0054] It should be specifically noted in this embodiment that S1 includes the following specific steps:

[0055] S11. Collect the breathing action data during the breathing process through a breathing action sensor carried at a specified position of the patient. The breathing action data includes the thoracic contraction amplitude and thoracic expansion amplitude data, and is stored in the first storage component;

[0056] S12. Obtain the gas data during the patient's breathing process through an airflow sensor arranged at the nasal cavity position of the patient. The gas data includes the inhaled gas composition data and the exhaled gas composition data, and is stored in the second storage component;

[0057] S13. Obtain the patient's breathing frequency and breathing gas volume data through a breathing sensor, and store them in the third storage component;

[0058] S14. Construct a virtual reality model representing the patient's breathing action data, the gas data during the breathing process, the breathing frequency, and the breathing depth data;

[0059] To construct a virtual reality model to represent the patient's breathing action data, the gas data during the breathing process, the breathing frequency, and the breathing depth, multiple technologies need to be combined, such as sensor technology, data processing, and visualization technology. The specific data transmission process is as Figure 3 shown. The collected patient data is processed by the server and transmitted to the virtual reality display screen, which specifically includes the following steps:

[0060] S141. Data processing and analysis: Convert the collected raw data into a format that can be used for analysis through signal processing algorithms (such as filtering, feature extraction, etc.);

[0061] S142. Virtual reality model construction: Use 3D modeling software to create a virtual lung and respiratory tract model, enabling it to dynamically change according to real-time breathing data. Map the breathing action data to the virtual model, so that the chest and abdomen of the model move with the real breathing movement. Map the gas data to the gas elements (such as bubbles or gas clouds) in the virtual environment to visualize the gas changes during breathing. Display the breathing frequency and depth data in the virtual environment in the form of charts or text for users to view;

[0062] S143. Visualization and Interaction: Integrate the processed data and model into a virtual reality system, use devices such as VR headsets and controllers for interaction, and design a user interface so that users can view respiratory data and models in an intuitive way, and perform real-time feedback and adjustment;

[0063] S144. Feedback and Training: According to the user's breathing behavior in the virtual environment, the system can provide immediate feedback, such as indications of breathing depth, suggestions for breathing frequency, etc., and design a breathing training program to help users learn the correct breathing pattern through the guidance of a virtual coach;

[0064] Through the above steps, a virtual reality model integrating respiratory motion data, gas data, breathing frequency, and breathing depth can be constructed. This model can not only be used to monitor the respiratory condition of patients, but also help patients with breathing training and improve their breathing quality;

[0065] S2. Obtain the respiratory motion data of the patient, import it into the respiratory motion judgment strategy for respiratory motion abnormality analysis, and demonstrate the respiratory motion data of the patient on the virtual reality model;

[0066] S3. Obtain the gas data during the patient's breathing process and the results of respiratory motion abnormality analysis, and import them into the respiratory motion abnormality evaluation strategy for respiratory motion abnormality evaluation;

[0067] S4. Obtain the physiological parameter data of the patient, and import the physiological parameter data of the patient into the respiratory effect abnormality analysis strategy for respiratory effect abnormality analysis;

[0068] S5. Import the obtained results of respiratory motion abnormality evaluation and respiratory effect abnormality analysis into the respiratory abnormality judgment strategy for respiratory abnormality judgment;

[0069] S6. Release the respiratory monitoring warning level according to the obtained respiratory abnormality judgment results, and display it on the virtual reality model to remind medical staff;

[0070] In this embodiment, it should be specifically noted that the specific steps of the respiratory motion judgment strategy in S2 are as follows:

[0071] S21. Obtain the respiratory motion data during the patient's breathing process;

[0072] S22. Import the respiratory motion data during the i-th breathing process into the respiratory motion abnormality value calculation formula to calculate the respiratory motion abnormality value. Among them, the respiratory motion abnormality value calculation formula during the i-th breathing process is: , where a is the proportion coefficient of the exhalation action, ki is the thoracic contraction amplitude of the i-th breathing process, km is the median of the safe range of the thoracic contraction amplitude during the breathing process, kmax is the maximum value of the safe range of the thoracic contraction amplitude during the breathing process, kmin is the minimum value of the safe range of the thoracic contraction amplitude during the breathing process, si is the thoracic expansion amplitude data of the i-th breathing process, sm is the median of the safe range of the thoracic expansion amplitude during the breathing process, smax is the maximum value of the safe range of the thoracic expansion amplitude during the breathing process, and smin is the minimum value of the safe range of the thoracic expansion amplitude during the breathing process.

[0073] It should be specifically noted in this embodiment that the specific steps of the abnormal breathing action evaluation strategy in S3 are as follows:

[0074] S31. Obtain the abnormal breathing action values during all breathing processes within the set period obtained by calculation, and at the same time obtain the gas data during all breathing processes within the set period;

[0075] S32. Import the obtained abnormal breathing action values during all breathing processes within the set period and the gas data during all breathing processes within the set period into the calculation formula for the abnormal breathing action evaluation value within the set period to calculate the abnormal breathing action evaluation value within the set period. Among them, the calculation formula for the abnormal breathing action evaluation value within the set period is: , where exp() is the exponential power of e, ut is the breathing frequency within the set period, um is the median of the safe range of the breathing frequency, N is the number of breaths within the set period, M is the type of gas during the breathing process, qij is the exhaled volume of the j-th type of gas during the i-th breathing process, qjm is the median of the safe range of the exhaled volume of the j-th type of gas, pij is the inhaled volume of the j-th type of gas during the i-th breathing process, and qjm is the median of the safe range of the inhaled volume of the j-th type of gas;

[0076] S33. Obtain the calculated abnormal breathing action evaluation value within the set period.

[0077] It should be specifically noted in this embodiment that the specific content of the abnormal breathing effect analysis strategy in S4 is:

[0078] S41. Obtain the physiological parameter data of the patient within the set period. Among them, the physiological parameter data of the patient includes blood oxygen, blood pressure, and body temperature data;

[0079] S42. Import the physiological parameter data of the patient within the set period into the calculation formula for the abnormal breathing effect evaluation value to calculate the abnormal breathing effect evaluation value. Among them, the calculation formula for the abnormal breathing effect evaluation value is: , where R is the type of physiological parameter data, dc is the proportion coefficient of the c-th type of physiological parameter data, fc is the average value of the patient's c-th type of physiological parameter data within a set period, and fcm is the median of the safety range of the c-th type of physiological parameter data within a set period;

[0080] S43. Obtain the calculated abnormal evaluation value of the breathing effect.

[0081] In this embodiment, it should be specifically noted that the breathing abnormality judgment strategy in S5 includes the following specific contents:

[0082] S51. Obtain the calculated abnormal evaluation value of the breathing movement and the abnormal evaluation value of the breathing effect within a set period;

[0083] S52. Import the calculated abnormal evaluation value of the breathing movement and the abnormal evaluation value of the breathing effect within a set period into the breathing abnormality judgment value calculation formula to calculate the breathing abnormality judgment value. The breathing abnormality judgment value calculation formula is: , where b is the proportion coefficient of the abnormal evaluation of the breathing movement.

[0084] In this embodiment, it should be specifically noted that the specific steps of S6 are as follows: Obtain the calculated breathing abnormality judgment value, and divide the calculated breathing abnormality judgment value by the set breathing abnormality judgment threshold to obtain the warning value;

[0085] If the value of the warning value is less than or equal to 0, no warning is given;

[0086] If the value of the warning value is greater than 0 and less than or equal to 0.3, a level-3 warning is given;

[0087] If the value of the warning value is greater than 0.3 and less than or equal to 0.6, a level-2 warning is given;

[0088] If the value of the warning value is greater than 0.6, a level-1 warning is given;

[0089] Release the breathing monitoring warning level and display it on the virtual reality model to remind the medical staff;

[0090] Possible medical measures required:

[0091] Level-3 warning: May include observation, rest, maintaining a good body position (such as semi-sitting position), and symptomatic treatment (such as using drugs to relieve cough or relieve dyspnea).

[0092] Level-2 warning: May require increasing the monitoring frequency, performing physical therapy (such as chest physical therapy), adjusting the drug treatment plan, or taking further medical interventions.

[0093] Level 1 warning: It may include emergency medical measures such as immediately performing cardiopulmonary resuscitation, using a ventilator, administering oxygen therapy, and quickly transporting to the hospital emergency room.

[0094] In this embodiment, it should be further noted that the value-taking methods of the proportion coefficient of the c-th type of physiological parameter data, the proportion coefficient of the exhalation action, the proportion coefficient of the abnormal assessment of the breathing action, and the abnormal breathing judgment threshold here are as follows: Obtain the patient's physical sign data, substitute it into the breathing abnormality judgment value calculation formula to calculate the breathing abnormality judgment value, and then obtain the warning level. Hire 50 medical experts in this field to conduct a verification and analysis of the warning level, and substitute the verification and analysis results into the fitting software to obtain the values of the proportion coefficient of the c-th type of physiological parameter data, the proportion coefficient of the exhalation action, the proportion coefficient of the abnormal assessment of the breathing action, and the abnormal breathing judgment threshold that meet the highest accuracy rate of the analysis and verification;

[0095] The present invention collects the patient's physical sign data through a data acquisition terminal, constructs a virtual reality model representing the patient's physical sign data, obtains the patient's breathing action data, imports it into the breathing action judgment strategy for abnormal breathing action analysis, and demonstrates the patient's breathing action data on the virtual reality model. Obtain the gas data and the abnormal breathing action analysis result during the patient's breathing process and import them into the abnormal breathing action assessment strategy for abnormal breathing action assessment. Obtain the patient's physiological parameter data, import the patient's physiological parameter data into the abnormal breathing effect analysis strategy for abnormal breathing effect analysis. Import the obtained abnormal breathing action assessment result and abnormal breathing effect analysis result into the abnormal breathing judgment strategy for abnormal breathing judgment, issue the breathing monitoring warning level according to the obtained abnormal breathing judgment result, and display it on the virtual reality model to remind the medical staff. Collect the comprehensive physical sign data of the patient during the breathing process, and comprehensively analyze the abnormalities of the patient's breathing action and physiological parameters based on the patient's physical signs during the breathing process, so as to comprehensively analyze and accurately monitor the abnormalities during the patient's breathing process, improving the accuracy and safety of breathing monitoring.

[0096] Embodiment 2

[0097] As Figure 2 shown, the virtual reality-based breathing monitoring simulation analysis system is implemented based on the above virtual reality-based breathing monitoring simulation analysis method, and it includes a data acquisition module, a breathing action abnormal analysis module, a breathing action abnormal assessment module, a breathing effect abnormal analysis module, a breathing abnormal judgment module, a warning module, and a control module. Among them, the data acquisition module is used to collect the patient's physical sign data through a data acquisition terminal and construct a virtual reality model representing the patient's physical sign data;

[0098] The abnormal breathing motion analysis module is used to obtain the breathing motion data of the patient, import it into the breathing motion judgment strategy for abnormal breathing motion analysis, and demonstrate the breathing motion data of the patient on the virtual reality model; the abnormal breathing motion evaluation module is used to obtain the gas data during the patient's breathing process and the abnormal breathing motion analysis result, import them into the abnormal breathing motion evaluation strategy for abnormal breathing motion evaluation, the abnormal breathing effect analysis module is used to obtain the physiological parameter data of the patient, import the physiological parameter data of the patient into the abnormal breathing effect analysis strategy for abnormal breathing effect analysis, the abnormal breathing judgment module is used to import the obtained abnormal breathing motion evaluation result and abnormal breathing effect analysis result into the abnormal breathing judgment strategy for abnormal breathing judgment, and the warning module is used to issue the breathing monitoring warning level according to the obtained abnormal breathing judgment result, and display a reminder for medical staff on the virtual reality model; the control module is used to control the operation of the data acquisition module, the abnormal breathing motion analysis module, the abnormal breathing motion evaluation module, the abnormal breathing effect analysis module, the abnormal breathing judgment module and the warning module.

[0099] Embodiment 3

[0100] This embodiment provides an electronic device, including: a processor and a memory, wherein, a computer program that can be called by the processor is stored in the memory;

[0101] The processor executes the above-mentioned virtual reality-based breathing monitoring simulation analysis method by calling the computer program stored in the memory.

[0102] This electronic device may have relatively large differences due to configuration or performance, and can include one or more processors (Central Processing Units, CPU) and one or more memories. Among them, at least one computer program is stored in the memory, and this computer program is loaded and executed by the processor to implement the virtual reality-based breathing monitoring simulation analysis method provided by the above method embodiment. This electronic device can also include other components for realizing the functions of the device. For example, this electronic device can also have components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment will not be elaborated here.

[0103] Embodiment 4

[0104] This embodiment proposes a computer-readable storage medium, on which a rewritable computer program is stored;

[0105] When the computer program runs on a computer device, it enables the computer device to execute the above-mentioned virtual reality-based breathing monitoring simulation analysis method.

[0106] For example, a computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0107] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0108] It should be understood that determining B based on A does not mean determining B only based on A, and B can also be determined based on A and / or other information.

[0109] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0110] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0111] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

Claims

1. A respiratory monitoring simulation analysis method based on virtual reality, characterized in that: It includes the following specific steps: S1. Collecting the patient's physical sign data through a data collection terminal and constructing a virtual reality model representing the patient's physical sign data; The specific steps include: S11, collecting respiratory action data during breathing by carrying a respiratory action sensor at a designated position of the patient, wherein the respiratory action data includes chest cavity contraction amplitude and chest cavity expansion amplitude data, and storing the data in a first storage component; S12, obtaining gas data of the patient during breathing through an airflow sensor disposed at the nasal cavity of the patient, wherein the gas data includes inhaled gas component data and exhaled gas component data, and storing the data in a second storage component; S13, obtaining the patient's respiratory frequency and respiratory gas volume data through a respiratory sensor, and storing the data in a third storage component; S14, constructing a virtual reality model representing the patient's breathing action data, gas data during breathing, breathing frequency, and breathing depth data; S2. Acquire the patient's respiratory action data, import it into the respiratory action judgment strategy to perform respiratory action abnormality analysis, and demonstrate the patient's respiratory action data on the virtual reality model; S3, obtaining the gas data of the patient's breathing process and the abnormal breathing movement analysis results and importing them into the abnormal breathing movement assessment strategy to perform abnormal breathing movement assessment; S4, obtaining the patient's physiological parameter data, including the patient's physiological parameter data including blood oxygen, blood pressure and body temperature data, and importing the patient's physiological parameter data into the respiratory effect abnormality analysis strategy to perform respiratory effect abnormality analysis; S5, importing the obtained abnormal breathing action evaluation results and abnormal breathing effect analysis results into the abnormal breathing judgment strategy to perform abnormal breathing judgment; S6. Release the respiratory monitoring warning level based on the abnormal respiratory judgment result, and display it on the virtual reality model to remind medical staff.

2. The virtual reality-based respiratory monitoring simulation analysis method according to claim 1, characterized in that: The specific steps of the breathing action judgment strategy in S2 are as follows: S21, obtaining the patient's breathing action data during breathing; S22, importing the breathing action data in the i-th breathing process into the breathing action abnormal value calculation formula to calculate the breathing action abnormal value, wherein the breathing action abnormal value calculation formula in the i-th breathing process is: , where a is the coefficient of exhalation action proportion, ki is the chest contraction amplitude of the i-th breathing process, km is the median value of the chest contraction amplitude safety range of the breathing process, kmax is the maximum value of the chest contraction amplitude safety range of the breathing process, kmin is the minimum value of the chest contraction amplitude safety range of the breathing process, si is the chest expansion amplitude data of the i-th breathing process, sm is the median value of the chest expansion amplitude safety range of the breathing process, smax is the maximum value of the chest expansion amplitude safety range of the breathing process, and smin is the minimum value of the chest expansion amplitude safety range of the breathing process.

3. The virtual reality-based respiratory monitoring simulation analysis method according to claim 2, characterized in that: The specific steps of the abnormal breathing action assessment strategy in S3 are as follows: S31, obtaining the calculated abnormal value of the breathing action during the entire breathing process within the set period, and simultaneously obtaining the gas data during the entire breathing process within the set period; S32, importing the obtained abnormal breathing action values ​​in all breathing processes in the set period and the gas data in all breathing processes in the set period into the abnormal breathing action evaluation value calculation formula in the set period to calculate the abnormal breathing action evaluation value in the set period, wherein the abnormal breathing action evaluation value calculation formula in the set period is: , where exp() is the power of e, ut is the respiratory frequency within the set period, um is the median of the respiratory frequency safety range, N is the number of breaths within the set period, M is the type of gas during breathing, qij is the exhaled volume of the j-th gas type during the i-th breathing process, qjm is the median of the exhaled volume safety range of the j-th gas type, pij is the inhaled volume of the j-th gas type during the i-th breathing process, and qjm is the median of the inhaled volume safety range of the j-th gas type; S33, obtaining the calculated breathing movement abnormality evaluation value within the set period.

4. The virtual reality-based respiratory monitoring simulation analysis method according to claim 3, characterized in that: The specific content of the abnormal breathing effect analysis strategy in S4 is: S41, obtaining physiological parameter data of the patient within a set period; S42, importing the patient's physiological parameter data within the set period into the abnormal breathing effect evaluation value calculation formula to calculate the abnormal breathing effect evaluation value, wherein the abnormal breathing effect evaluation value calculation formula is: , where R is the type of physiological parameter data, dc is the proportion coefficient of the cth type of physiological parameter data, fc is the average value of the cth type of physiological parameter data of the patient within the set period, and fcm is the median value of the safe range of the cth type of physiological parameter data within the set period; S43. Obtain the calculated breathing effect abnormality assessment value.

5. The virtual reality-based respiratory monitoring simulation analysis method according to claim 4, characterized in that: The breathing abnormality judgment strategy in S5 includes the following specific contents: S51, obtaining the calculated breathing action abnormality evaluation value and breathing effect abnormality evaluation value within a set period; S52, importing the calculated breathing action abnormality evaluation value and breathing effect abnormality evaluation value within the set period into the breathing abnormality judgment value calculation formula to calculate the breathing abnormality judgment value, wherein the breathing abnormality judgment value calculation formula is: , where b is the coefficient of abnormal respiratory movement assessment.

6. The virtual reality-based respiratory monitoring simulation analysis method according to claim 5, characterized in that: The specific steps of S6 are as follows: obtaining the calculated abnormal breathing judgment value, dividing the calculated abnormal breathing judgment value by the set abnormal breathing judgment threshold to obtain a warning value; If the warning value is less than or equal to 0, no warning will be issued; If the warning value is greater than 0 and less than or equal to 0.3, a level 3 warning is issued; If the warning value is greater than 0.3 and less than or equal to 0.6, a second-level warning is issued; If the warning value is greater than 0.6, a first-level warning is issued; The respiratory monitoring warning level is issued and displayed on the virtual reality model to remind medical staff.

7. A respiratory monitoring simulation analysis system based on virtual reality, which is implemented based on the respiratory monitoring simulation analysis method based on virtual reality according to any one of claims 1 to 6, characterized in that: It includes a data acquisition module, a breathing action abnormality analysis module, a breathing action abnormality assessment module, a breathing effect abnormality analysis module, a breathing abnormality judgment module, an early warning module and a control module, wherein the data acquisition module is used to collect the patient's physical sign data through a data acquisition terminal and construct a virtual reality model representing the patient's physical sign data; The abnormal breathing movement analysis module is used to obtain the patient's breathing movement data, import it into the breathing movement judgment strategy to perform abnormal breathing movement analysis, and demonstrate the patient's breathing movement data on a virtual reality model; the abnormal breathing movement evaluation module is used to obtain the patient's gas data during breathing and import the abnormal breathing movement analysis results into the abnormal breathing movement evaluation strategy to perform abnormal breathing movement evaluation; the abnormal breathing effect analysis module is used to obtain the patient's physiological parameter data, import the patient's physiological parameter data into the abnormal breathing effect analysis strategy to perform abnormal breathing effect analysis; the abnormal breathing judgment module is used to import the obtained abnormal breathing movement evaluation results and abnormal breathing effect analysis results into the abnormal breathing judgment strategy to perform abnormal breathing judgment; the early warning module is used to issue a respiratory monitoring early warning level according to the obtained abnormal breathing judgment results, and display it on the virtual reality model to remind medical staff.

8. The virtual reality-based respiratory monitoring simulation analysis system according to claim 7, characterized in that: The control module is used for controlling the operation of the data acquisition module, the abnormal breathing action analysis module, the abnormal breathing action evaluation module, the abnormal breathing effect analysis module, the abnormal breathing judgment module and the early warning module.

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