Respiration monitoring simulation analysis system and method based on virtual reality

Abstract

The invention discloses a respiration monitoring simulation analysis system and method based on virtual reality, and the method comprises the steps: obtaining gas data and a respiration action abnormality analysis result in a respiration process of a patient, importing the gas data and the respiration action abnormality analysis result into a respiration action abnormality evaluation strategy for respiration action abnormality evaluation, and obtaining physiological parameter data of the patient; the physiological parameter data of the patient is imported into a breathing effect anomaly analysis strategy for breathing effect anomaly analysis, and the obtained breathing action anomaly evaluation result and the breathing effect anomaly analysis result are imported into a breathing anomaly judgment strategy for breathing anomaly judgment. According to an obtained breathing abnormity judgment result, issuing a breathing monitoring early warning level, displaying on a virtual reality model to remind medical staff, collecting comprehensive body sign data of a patient in the breathing process, analyzing breathing action and physiological parameter abnormity of the patient by combining the body signs of the patient in the breathing process, and displaying the abnormal breathing monitoring early warning level on the virtual reality model. And furthermore, the abnormity in the breathing process of the patient is comprehensively analyzed and accurately monitored.

Description

Technical Field

[0001] The invention belongs to the technical field of medical care, and in particular to a respiratory monitoring simulation analysis system and method based on virtual reality. 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 breathing patterns. Doctors can remotely access patients' VR respiratory monitoring data, monitor patients' respiratory conditions in real time, and provide remote guidance and suggestions based on the data. This is especially useful for patients with limited mobility or patients who need long-term monitoring. Virtual reality technology can not only provide patients with a more natural and interesting respiratory monitoring experience, but also help doctors more accurately assess patients' respiratory conditions and provide personalized respiratory training programs. With the advancement of technology, the application of virtual reality in respiratory monitoring and training will be more extensive and in-depth in the future.

[0003] The prior art cannot comprehensively analyze the patient's breathing movements and abnormal physiological parameters during the respiratory monitoring simulation analysis, and thus cannot effectively conduct comprehensive analysis and accurate monitoring of abnormalities in the patient's breathing process, which reduces the accuracy and safety of respiratory monitoring. The prior art has the above problems. In order to solve these problems, the present application designs a respiratory monitoring simulation analysis system and method based on virtual reality. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention proposes a respiratory monitoring simulation analysis system and method based on virtual reality. The present invention constructs a virtual reality model to characterize the patient's physical sign data, obtains the patient's respiratory movement data, imports it into a respiratory movement judgment strategy to perform respiratory movement abnormality analysis, and demonstrates the patient's respiratory movement data on the virtual reality model. The gas data and respiratory movement abnormality analysis results of the patient during the breathing process are obtained and imported into a respiratory movement abnormality evaluation strategy to perform respiratory movement abnormality evaluation. The patient's physiological parameter data is obtained, and the patient's physiological parameter data is imported into a respiratory effect abnormality analysis strategy to perform respiratory effect abnormality analysis. The obtained respiratory movement abnormality evaluation results and respiratory effect abnormality analysis results are imported into a respiratory abnormality judgment strategy to perform respiratory abnormality judgment. The respiratory monitoring warning level is issued according to the obtained respiratory abnormality judgment results, and is displayed on the virtual reality model to remind medical staff. The comprehensive physical sign data of the patient during the breathing process is collected, and the patient's respiratory movement and physiological parameter abnormalities are analyzed based on the comprehensive body signs of the patient during the breathing process, thereby comprehensively analyzing and accurately monitoring the abnormalities in the patient's breathing process, thereby improving the accuracy and safety of respiratory monitoring.

[0005] To achieve the above object, the present invention provides the following technical solutions: A respiratory monitoring simulation analysis method based on virtual reality 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; 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, 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.

[0006] Specifically, S1 includes the following specific steps: 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. Construct a virtual reality model that represents the patient's breathing action data, gas data during breathing, breathing frequency, and breathing depth data.

[0007] Specifically, 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.

[0008] Specifically, 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 jth gas type during the i-th breathing process, qjm is the median of the exhaled volume safety range of the jth gas type, pij is the inhaled volume of the jth gas type during the i-th breathing process, and qjm is the median of the inhaled volume safety range of the jth gas type; S33, obtaining the calculated breathing movement abnormality evaluation value within the set period.

[0009] Specifically, 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, wherein the physiological parameter data of the patient includes blood oxygen, blood pressure and body temperature data; 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.

[0010] Specifically, 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.

[0011] Specifically, 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.

[0012] A respiratory monitoring simulation analysis system based on virtual reality, which is implemented based on the above-mentioned respiratory monitoring simulation analysis method based on virtual reality, comprises a data acquisition module, a respiratory action abnormality analysis module, a respiratory action abnormality evaluation module, a respiratory effect abnormality analysis module, a respiratory 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.

[0013] Specifically, the control module is used for controlling and operating the data acquisition module, the abnormal breathing movement analysis module, the abnormal breathing movement evaluation module, the abnormal breathing effect analysis module, the abnormal breathing judgment module and the early warning module.

[0014] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned virtual reality-based respiratory monitoring simulation analysis method by calling the computer program stored in the memory.

[0015] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned virtual reality-based respiratory monitoring simulation analysis method.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention collects the patient's physical sign data through a data collection terminal, constructs a virtual reality model representing the patient's physical sign data, obtains the patient's respiratory movement data, imports it into a respiratory movement judgment strategy to perform respiratory movement abnormality analysis, and demonstrates the patient's respiratory movement data on the virtual reality model, obtains the patient's gas data during breathing and the respiratory movement abnormality analysis results, imports them into a respiratory movement abnormality evaluation strategy to perform respiratory movement abnormality evaluation, obtains the patient's physiological parameter data, imports the patient's physiological parameter data into a respiratory effect abnormality analysis strategy to perform respiratory effect abnormality analysis, imports the obtained respiratory movement abnormality evaluation results and respiratory effect abnormality analysis results into a respiratory abnormality judgment strategy to perform respiratory abnormality judgment, releases a respiratory monitoring warning level according to the obtained respiratory abnormality judgment results, and displays it on the virtual reality model to remind medical staff, collects the patient's comprehensive physical sign data during breathing, and comprehensively analyzes the patient's respiratory movement and physiological parameter abnormalities based on the patient's physical signs during breathing, thereby comprehensively analyzing and accurately monitoring the abnormalities in the patient's breathing process, thereby improving the accuracy and safety of respiratory monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a respiratory monitoring simulation analysis method based on virtual reality of the present invention; Figure 2 It is a schematic diagram of the framework of the respiratory monitoring simulation and analysis system based on virtual reality of the present invention; Figure 3 It is a data transmission schematic diagram of the present invention. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0019] Example 1

[0020] See also Figure 1 The present invention provides an embodiment: a respiratory monitoring simulation analysis method based on virtual reality, which 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; In this embodiment, it should be specifically explained that S1 includes the following specific steps: 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; Building a virtual reality model to represent the patient's breathing action data, gas data during breathing, breathing frequency and breathing depth requires the combination of multiple technologies, such as sensor technology, data processing and visualization technology. The specific data transmission process is as follows Figure 3 As shown, the collected patient data is processed by the server and transmitted to the virtual reality display screen, which specifically includes the following steps: 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.); S142. Virtual reality model construction: Use 3D modeling software to create a virtual lung and airway model that can change dynamically according to real-time breathing data, map the breathing action data onto the virtual model so that the chest and abdomen of the model move with real breathing movements, map gas data onto gas elements (such as bubbles or gas clouds) in the virtual environment to visualize the changes in gas during breathing, and display the breathing rate and depth data in the virtual environment in the form of charts or text for users to view; S143, Visualization and Interaction: Integrate the processed data and models into a virtual reality system, use VR helmets and controllers to interact, and design a user interface so that users can view respiratory data and models in an intuitive way and make real-time feedback and adjustments; S144, Feedback and Training: Based on the user's breathing behavior in the virtual environment, the system can provide immediate feedback, such as indication 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; Through the above steps, a virtual reality model integrating breathing action data, gas data, breathing frequency and breathing depth can be constructed. This model can not only be used to monitor the patient's breathing condition, but also help the patient to conduct breathing training and improve breathing quality; 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, 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 according to the abnormal respiratory judgment result, and display it on the virtual reality model to remind medical staff; In this embodiment, it should be specifically explained 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.

[0021] In this embodiment, it should be specifically explained 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 jth gas type during the i-th breathing process, qjm is the median of the exhaled volume safety range of the jth gas type, pij is the inhaled volume of the jth gas type during the i-th breathing process, and qjm is the median of the inhaled volume safety range of the jth gas type; S33, obtaining the calculated breathing movement abnormality evaluation value within the set period.

[0022] In this embodiment, it should be specifically explained 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, wherein the physiological parameter data of the patient includes blood oxygen, blood pressure and body temperature data; 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.

[0023] In this embodiment, it should be specifically explained 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.

[0024] In this embodiment, it should be specifically explained 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; Release respiratory monitoring warning levels and display them on the virtual reality model to remind medical staff; Medical treatment that may be needed: Level 3 alert: May include observation, rest, maintaining a good posture (such as semi-sitting), and symptomatic treatment (such as using medication to relieve cough or relieve breathing difficulties).

[0025] Level 2 alert: Increased monitoring frequency, physical therapy (such as chest physical therapy), adjustment of medication regimen, or further medical intervention may be required.

[0026] Level 1 alert: Emergency medical measures may include immediate cardiopulmonary resuscitation, use of a ventilator, oxygen therapy, and rapid transfer to a hospital emergency room.

[0027] It should be further explained in this embodiment that the proportion coefficient of the cth physiological parameter data, the exhalation action proportion coefficient, the respiratory action abnormality assessment proportion coefficient and the respiratory abnormality judgment threshold value are obtained by obtaining the patient's physical sign data, substituting it into the respiratory abnormality judgment value calculation formula to calculate the respiratory abnormality judgment value and then obtain the warning level, hiring 50 medical experts in this field to check and analyze the warning level, and substituting the check and analysis results into the fitting software to obtain the proportion coefficient of the cth physiological parameter data, the exhalation action proportion coefficient, the respiratory action abnormality assessment proportion coefficient and the respiratory abnormality judgment threshold value that meet the highest accuracy of the analysis and verification; The present invention collects the patient's physical sign data through a data collection terminal, constructs a virtual reality model representing the patient's physical sign data, obtains the patient's respiratory movement data, imports it into a respiratory movement judgment strategy to perform respiratory movement abnormality analysis, and demonstrates the patient's respiratory movement data on the virtual reality model, obtains the patient's gas data during breathing and the respiratory movement abnormality analysis results, imports them into a respiratory movement abnormality evaluation strategy to perform respiratory movement abnormality evaluation, obtains the patient's physiological parameter data, imports the patient's physiological parameter data into a respiratory effect abnormality analysis strategy to perform respiratory effect abnormality analysis, imports the obtained respiratory movement abnormality evaluation results and respiratory effect abnormality analysis results into a respiratory abnormality judgment strategy to perform respiratory abnormality judgment, releases a respiratory monitoring warning level according to the obtained respiratory abnormality judgment results, and displays it on the virtual reality model to remind medical staff, collects the patient's comprehensive physical sign data during breathing, and comprehensively analyzes the patient's respiratory movement and physiological parameter abnormalities based on the patient's physical signs during breathing, thereby comprehensively analyzing and accurately monitoring the abnormalities in the patient's breathing process, thereby improving the accuracy and safety of respiratory monitoring.

[0028] Example 2

[0029] like Figure 2 As shown, 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, and includes a data acquisition module, a respiratory action abnormality analysis module, a respiratory action abnormality evaluation module, a respiratory effect abnormality analysis module, a respiratory 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 for abnormal breathing movement analysis, and demonstrate the patient's breathing movement data on the virtual reality model; the abnormal breathing movement evaluation module is used to obtain the patient's gas data during the breathing process and import the abnormal breathing movement analysis results into the abnormal breathing movement evaluation strategy for 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 for 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 for abnormal breathing judgment; the early warning module is used to issue the 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; the control module is used to control the operation of the data acquisition module, the abnormal breathing movement analysis module, the abnormal breathing movement evaluation module, the abnormal breathing effect analysis module, the abnormal breathing judgment module and the early warning module.

[0030] Example 3

[0031] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned virtual reality-based respiratory monitoring simulation analysis method by calling the computer program stored in the memory.

[0032] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement the virtual reality-based respiratory monitoring simulation analysis method provided by the above method embodiment. The electronic device may also include other components for implementing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0033] Example 4

[0034] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon; When the computer program runs on a computer device, the computer device executes the above-mentioned virtual reality-based respiratory monitoring simulation analysis method.

[0035] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0036] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0037] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0038] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by 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 a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. 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, computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through 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 data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0039] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0040] 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 aforementioned method embodiments and will not be repeated here.

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; 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, 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 S1 includes the following specific steps: 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. Construct a virtual reality model that represents the patient's breathing action data, gas data during breathing, breathing frequency, and breathing depth data.

3. The virtual reality-based respiratory monitoring simulation analysis method according to claim 2, 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.

4. The virtual reality-based respiratory monitoring simulation analysis method according to claim 3, 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 jth gas type during the i-th breathing process, qjm is the median of the exhaled volume safety range of the jth gas type, pij is the inhaled volume of the jth gas type during the i-th breathing process, and qjm is the median of the inhaled volume safety range of the jth gas type; S33, obtaining the calculated breathing movement abnormality evaluation value within the set period.

5. The virtual reality-based respiratory monitoring simulation analysis method according to claim 4, 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, wherein the physiological parameter data of the patient includes blood oxygen, blood pressure and body temperature data; 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.

6. The virtual reality-based respiratory monitoring simulation analysis method according to claim 5, 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.

7. The virtual reality-based respiratory monitoring simulation analysis method according to claim 6, 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.

8. 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 7, 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.

9. The virtual reality-based respiratory monitoring simulation analysis system according to claim 8, 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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