A visual teaching method based on AI and MR technology virtual fusion

Abstract

The application discloses a kind of visual teaching methods based on AI and MR technology virtual fusion, it is related to virtual reality technical field.The visual teaching method based on AI and MR technology virtual fusion, including the following steps: artificial respiration qualified evaluation, artificial respiration action evaluation and artificial respiration rhythm evaluation.The artificial respiration qualified evaluation coefficient of artificial respiration data is judged whether to switch AI voice guidance, then the action standard evaluation index of artificial respiration action data is judged whether to carry out MR simulation guidance, finally the artificial respiration rhythm evaluation index of press time data and artificial respiration data is judged whether to carry out MR rhythm simulation teaching, reaches the effect of more accurately evaluating the visual teaching of artificial respiration, solves the problem that the visual teaching evaluation of artificial respiration is not comprehensive in prior art.

Description

Technical Field

[0001] This invention relates to the field of virtual reality technology, and in particular to a visual teaching method based on the virtual fusion of AI and MR technologies. Background Technology

[0002] With the rapid development of Artificial Intelligence (AI) and Mixed Reality (MR) technologies, the education sector has ushered in new opportunities for transformation. AI technology enables machines to process data and learn, providing innovative functions such as personalized recommendations and intelligent tutoring, thus promoting deeper student self-directed learning. MR, as a combination of Virtual Reality (VR) and Augmented Reality (AR), provides a highly immersive and interactive experience by overlaying virtual elements onto reality or immersing users in entirely new virtual environments. Visual teaching methods that integrate AI and MR can break through the spatial limitations of traditional classrooms, making abstract knowledge visible and providing an immersive learning experience. This approach not only helps students understand complex concepts more intuitively but also provides intelligent feedback based on student needs, becoming an important direction for exploration in modern educational technology.

[0003] Existing AI and MR-based visualization-based teaching methods have already seen some application in the education field. AI technology, through big data analysis, machine learning, and natural language processing, can provide personalized learning path recommendations, intelligent assessment, and adaptive learning systems, helping students adjust learning content according to their individual progress and comprehension abilities. MR technology, by integrating virtual reality (VR) and augmented reality (AR), provides an immersive learning experience, visualizing abstract knowledge and enabling students to learn interactively in simulated environments. For example, in biology courses, students can use MR technology to view 3D models of human anatomy or cell structures, deepening their understanding. Despite significant progress, these technologies still face challenges such as high hardware costs, insufficient technology adoption, and the complexity of developing some content. With continuous technological innovation and increasing educational demands, AI and MR-based visualization-based teaching methods are expected to achieve wider application.

[0004] For example, the invention patent with publication number CN114442328A discloses a virtual reality head-mounted device for teaching and its transmission system, which includes: a head-mounted device, a connector assembled at the front end of the head-mounted device, and a display assembled at the bottom end of the rear surface of the connector.

[0005] For example, the invention patent announcement with announcement number CN115877578B discloses a command transmitting device for facilitating VR virtual classroom teaching, which includes: a device body, two viewing tubes, a driven gear, and a foot wearing structure. Each viewing tube has a toothed groove on its outer wall. The two viewing tubes are movably connected above the two observation parts, and the bottom of the viewing tubes is set with an opening that extends to the inside of the observation part.

[0006] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: In existing technologies, because CPR (Cardio Pulmonary Resuscitation) is a skill that combines chest compressions and artificial respiration and has strict requirements for operational actions, existing CPR teaching methods are prone to one-sided assessment of students' learning outcomes, resulting in incomplete visual teaching and assessment of artificial respiration. Summary of the Invention

[0007] This application provides a visualization teaching method based on the virtual fusion of AI and MR technologies, which solves the problem of incomplete visualization teaching evaluation of artificial respiration in the prior art and realizes a more accurate visualization teaching evaluation of artificial respiration.

[0008] This application provides a visualization teaching method based on the virtual fusion of AI and MR technologies, including the following steps: First, real-time acquisition of artificial respiration data of students using a virtual dummy within a preset time period via MR; obtaining an artificial respiration qualification assessment coefficient based on the artificial respiration data; comparing the artificial respiration qualification assessment coefficient with a breathing threshold to determine whether to switch to AI voice guidance; the artificial respiration qualification assessment coefficient is used to assess the qualification level of the student's artificial respiration volume. Second, real-time acquisition of artificial respiration action data of students within a preset time period via AI; obtaining an action standardization assessment index based on the artificial respiration action data; comparing the action standardization assessment index with an action threshold to determine whether to conduct MR simulation guidance; the action standardization assessment index is used to assess the standardization of the student's actions when performing artificial respiration. Third, acquisition of compression time data within a preset time period via MR and combination with artificial respiration data to obtain an artificial respiration rhythm assessment index; comparing the artificial respiration rhythm assessment index with a rhythm threshold to determine whether to conduct MR rhythm simulation teaching; the artificial respiration rhythm assessment index is used to assess the degree to which the student's artificial respiration rhythm conforms to the target.

[0009] Furthermore, the artificial respiration data includes ventilation volume, ventilation rate, chest rise height, chest descent height, initial chest height, airflow rate, and respiratory action time data; respiratory action time data includes respiratory rate and respiratory duration; ventilation volume represents the average ventilation volume within a preset time period; ventilation rate represents the average ventilation rate within a preset time period; chest rise height represents the average chest rise height within a preset time period; chest descent height represents the average chest descent height within a preset time period; and airflow rate represents the average airflow rate within a preset time period.

[0010] Furthermore, the specific steps for obtaining the qualification assessment coefficient for artificial respiration are as follows: Number the preset time period and population type respectively; obtain reference artificial respiration data and artificial respiration weighting factors from the preset database. The reference artificial respiration data includes reference intervals for ventilation volume, ventilation rate, chest rise and fall, and ventilation rate. The artificial respiration weighting factors include weighting factors for ventilation volume, ventilation rate, ventilation depth, and ventilation rate; obtain the ventilation volume index based on the ventilation volume and ventilation volume reference intervals. The ventilation volume index represents the degree of agreement between the ventilation volume and the ventilation volume reference intervals; obtain the ventilation rate index based on the ventilation rate and ventilation rate reference intervals; and obtain the ventilation volume index based on the ventilation rate and ventilation rate reference intervals. The ventilation frequency index is obtained from the interval, which indicates the degree of agreement between the ventilation frequency and the reference interval. The ventilation depth index is obtained based on the chest rise height, chest descent height, original chest height and the reference interval of chest rise and fall. The ventilation depth index is used to reflect whether the ventilation depth is qualified through the degree of chest rise and fall. The ventilation velocity index is obtained based on the ventilation velocity and the reference interval of ventilation velocity. The ventilation velocity index indicates the degree of agreement between the ventilation velocity and the reference interval of ventilation velocity. The artificial respiration qualification assessment coefficient is obtained by coupling the ventilation volume index, ventilation frequency index, ventilation depth index and ventilation velocity index with the corresponding artificial respiration weighting factors after weighting calculation.

[0011] Furthermore, the artificial respiration action data includes hand offset, hand pressure, head tilt angle, and respiratory fit; hand offset refers to the offset of the trainee's hand position area from the preset hand position area; respiratory fit refers to the matching ratio between the trainee's mouth and the virtual puppet's mouth.

[0012] Furthermore, the specific process for obtaining the action standardization assessment index is as follows: Reference artificial respiration action data is obtained from a preset database. This data includes the maximum hand deflection, the hand pressure reference range, and the maximum head tilt angle. The hand pressure reference range includes the minimum and maximum hand pressure values. Logical quantification is performed on the obtained artificial respiration action data and the corresponding reference artificial respiration action data to obtain the quantified value of each artificial respiration action. The results of coupling the quantified values ​​of each artificial respiration action are then inversely calculated to obtain the action standardization assessment index.

[0013] Furthermore, the specific steps for obtaining the artificial respiration rhythm assessment index are as follows: D1, monitor whether the trainee is performing artificial respiration and chest compressions using MR. If both are present, proceed to D2; otherwise, do not perform an artificial respiration rhythm assessment. D2, obtain the breathing action time data from the artificial respiration data and obtain the chest compression time data using MR. The chest compression time data includes compression frequency and compression time. D3, obtain reference rhythm data from a preset database. The reference rhythm data includes reference breathing frequency, reference chest compression frequency, and reference ratio. D4, obtain the artificial respiration rhythm assessment index based on the breathing action time data, chest compression time data, and reference rhythm data.

[0014] Furthermore, the specific process for determining whether to conduct MR rhythm simulation teaching is as follows: Compare the artificial respiration rhythm assessment index with the rhythm threshold obtained from the database: If the artificial respiration rhythm assessment index is not less than the rhythm threshold, proceed to the next preset time period for artificial respiration teaching of the current population type until the preset number of preset time periods for artificial respiration teaching of the current population type are completed, and record that the student has completed the artificial respiration teaching of the current population type; If the artificial respiration rhythm assessment index is less than the rhythm threshold, end the student's artificial respiration learning process within the current preset time period, and conduct MR rhythm practice and provide guidance teaching in conjunction with AI voice before the artificial respiration teaching of the next preset time period.

[0015] Further, the specific steps for conducting MR rhythm practice and combining AI voice guidance are as follows: Step 1, demonstrate the standard artificial respiration procedure through MR and prompt the student to imitate and practice, while detecting and reminding the student's simulation tracking process; Step 2, use AI voice to ask the student whether to proceed to the next preset time period for artificial respiration teaching. If yes, switch to the next preset time period for artificial respiration teaching; otherwise, automatically save the student's current artificial respiration learning progress until the student completes the artificial respiration teaching for all types of people.

[0016] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. The system uses a pass / fail assessment coefficient obtained from artificial respiration data to determine whether to switch to AI voice guidance. Then, it uses a motion standardization assessment index obtained from artificial respiration action data to determine whether to conduct MR simulation guidance. Finally, it uses an artificial respiration rhythm assessment index obtained from compression time data and artificial respiration data to determine whether to conduct MR rhythm simulation teaching. This achieves comprehensive teaching and testing of students' artificial respiration learning, and thus realizes more accurate evaluation of visual teaching of artificial respiration, effectively solving the problem of incomplete visual teaching evaluation of artificial respiration in existing technologies.

[0017] 2. By obtaining reference artificial respiration action data from a preset database and the acquired artificial respiration action data, an action standardization evaluation index is obtained, thereby realizing a quantitative assessment of the standardization of the trainee's artificial respiration action, and thus achieving a more accurate assessment of the quality of the trainee's artificial respiration learning outcomes.

[0018] 3. By monitoring whether the trainee is performing artificial respiration and chest compressions, if both are present, an artificial respiration rhythm assessment is performed; otherwise, the assessment is not performed. Then, the breathing time data and chest compression time data are obtained from the artificial respiration data. Next, reference rhythm data is obtained from a preset database. Finally, an artificial respiration rhythm assessment index is obtained based on the breathing time data, chest compression time data, and reference rhythm data. This achieves a quantitative assessment of the trainee's standardization of artificial respiration rhythm, thereby enabling a more accurate evaluation of the quality of the trainee's artificial respiration learning outcomes. Attached Figure Description

[0019] Figure 1 A flowchart illustrating a visual teaching method based on the virtual fusion of AI and MR technologies, provided for embodiments of this application; Figure 2 A flowchart illustrating the process of determining whether to provide AI voice guidance and MR simulation guidance, provided for embodiments of this application; Figure 3 This is a flowchart illustrating the process of determining whether to conduct MR rhythm simulation teaching, as provided in an embodiment of this application. Detailed Implementation

[0020] This application provides a visual teaching method based on the virtual fusion of AI and MR technologies, which solves the problem of incomplete visual teaching evaluation of artificial respiration in the prior art. It obtains an artificial respiration qualification evaluation coefficient from the acquired artificial respiration data, and uses this coefficient against a breathing threshold to determine whether to switch to AI voice guidance. Simultaneously, it obtains an action standardization evaluation index by combining reference artificial respiration action data obtained from a preset database with the acquired artificial respiration action data, and uses this index against an action threshold to determine whether to conduct MR simulation guidance. Then, it monitors whether the student has both artificial respiration and chest compression actions. If both are present, an artificial respiration rhythm evaluation is performed; otherwise, no artificial respiration rhythm evaluation is performed. It then obtains breathing action time data and chest compression time data from the artificial respiration data, and next, obtains reference rhythm data from the preset database. Finally, it obtains an artificial respiration rhythm evaluation index based on the breathing action time data, chest compression time data, and reference rhythm data, and uses this index against a rhythm threshold to determine whether to conduct MR rhythm simulation teaching, thus achieving a more accurate visual teaching evaluation of artificial respiration.

[0021] The technical solution in this application embodiment aims to address the problem of incomplete visualization and assessment in artificial respiration teaching. The overall approach is as follows: By acquiring artificial respiration data, an artificial respiration qualification assessment coefficient is obtained, and based on this, a judgment is made whether to switch to AI voice guidance in accordance with the breathing threshold. At the same time, by acquiring artificial respiration action data, an action standardization assessment index is obtained, and based on this, a judgment is made whether to conduct MR simulation guidance in accordance with the action threshold. Then, by combining the acquired compression time data with artificial respiration data, an artificial respiration rhythm assessment index is obtained, and based on this, a judgment is made whether to conduct MR rhythm simulation teaching in accordance with the rhythm threshold. This achieves a more accurate assessment of the visual teaching effect of artificial respiration.

[0022] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0023] like Figure 1 The flowchart shown is a visual teaching method based on the virtual fusion of AI and MR technologies provided in this application embodiment. The method includes the following steps: acquiring artificial respiration data of students using virtual dolls in real time within a preset time period through MR; obtaining an artificial respiration qualification assessment coefficient based on the artificial respiration data; comparing the artificial respiration qualification assessment coefficient with a breathing threshold to determine whether to switch to AI voice guidance; the virtual doll is used to simulate the physical conditions of different groups of people through MR technology; the artificial respiration qualification assessment coefficient is used to assess whether the student's artificial respiration volume is qualified; acquiring artificial respiration action data of students in real time within a preset time period through AI; obtaining an action standardization assessment index based on the artificial respiration action data; comparing the action standardization assessment index with an action threshold to determine whether to conduct MR simulation guidance; the action standardization assessment index is used to assess whether the student's actions are standardized when performing artificial respiration; acquiring compression time data within a preset time period through MR and combining it with artificial respiration data to obtain an artificial respiration rhythm assessment index; comparing the artificial respiration rhythm assessment index with a rhythm threshold to determine whether to conduct MR rhythm simulation teaching; the artificial respiration rhythm assessment index is used to assess whether the student's artificial respiration rhythm is correct.

[0024] In this embodiment, the preset time period is set by professional medical staff to ensure that the data related to artificial respiration within the preset time period can be reflected in real time. By using MR technology to simulate the physical conditions of different groups of people, trainees can be helped to interact with individuals of different ages, genders or body types in a virtual environment. This simulation training helps to provide trainees with more accurate, timely and effective learning support to improve their overall artificial respiration skills and enable them to master emergency rescue skills more comprehensively. It also helps to more accurately evaluate the visual teaching of artificial respiration.

[0025] Specifically, the artificial respiration qualification assessment coefficient mainly evaluates whether the amount of gas in artificial respiration is up to standard, while the action standardization assessment index mainly evaluates whether the artificial respiration action is standardized. In addition, improper artificial respiration action may lead to air leakage, which in turn leads to an unqualified amount of gas. Furthermore, the breathing rhythm and the compression rhythm of artificial respiration work together to affect the overall rhythm of artificial respiration, thus affecting the artificial respiration rhythm assessment index. Therefore, it can be seen that the artificial respiration qualification assessment coefficient, the action standardization assessment index, and the artificial respiration rhythm assessment index are interconnected and mutually influential.

[0026] The artificial respiration data includes ventilation volume, ventilation rate, chest rise height, chest descent height, initial chest height, airflow rate, and respiratory action time data; respiratory action time data includes respiratory rate and respiratory duration; ventilation volume represents the average ventilation volume within a preset time period; ventilation rate represents the average ventilation rate within a preset time period; chest rise height represents the average chest rise height within a preset time period; chest descent height represents the average chest descent height within a preset time period; and airflow rate represents the average airflow rate within a preset time period.

[0027] Specifically, in the MR environment, the virtual dummy integrates data sensors related to artificial respiration. These sensors capture real-time data on the artificial respiration applied to the dummy by the learner during the operation, including but not limited to pressure sensors, flow sensors, position sensors, and time sensors. Furthermore, in the MR environment, the virtual dummy's chest can dynamically change, reflecting each ventilation operation by the learner. Simultaneously, AI calculates the ventilation rate based on the gas input speed and displays fluctuations in the ventilation rate on the MR interface to help the learner control the ventilation rate. By collecting and analyzing artificial respiration data in real time, a smart, detailed, and personalized simulated artificial respiration learning environment is provided for learners, thereby improving the quality and practicality of artificial respiration skills. This data-driven guidance method not only enhances learners' first aid skills but also accelerates the learning process, cultivating more competent first aid personnel.

[0028] like Figure 2The diagram illustrates the process for determining whether to provide AI voice guidance and MR simulation guidance according to an embodiment of this application. The specific logic is as follows: Real-time artificial respiration data is acquired via MR. An artificial respiration qualification assessment coefficient is obtained based on this data. This coefficient is compared with a breathing threshold. If the coefficient is not less than the threshold, AI voice guidance is not switched. If the coefficient is less than the threshold, the student's artificial respiration learning process is paused, and AI voice guidance is switched to provide instruction. Real-time artificial respiration action data is acquired via AI. An action standardization assessment index is obtained based on this data. This index is compared with an action threshold. If the index is not less than the threshold, MR simulation guidance is not provided. If the index is less than the threshold, the student's artificial respiration learning process is paused, and the student is prompted to perform simulated practice. The simulated practice is then tested. Once the student passes the simulated practice test, the artificial respiration learning process resumes. This process helps provide students with a personalized simulated learning method for emergency first aid skills and also facilitates a more accurate assessment of the quality of visualized teaching of artificial respiration.

[0029] Furthermore, the specific steps for obtaining the artificial respiration qualification assessment coefficient are as follows: Number the preset time period and population type respectively; obtain reference artificial respiration data and artificial respiration weighting factors from the preset database. The reference artificial respiration data includes reference intervals for ventilation volume, ventilation rate, chest rise and fall, and ventilation rate. The artificial respiration weighting factors include weighting factors for ventilation volume, ventilation rate, ventilation depth, and ventilation rate; based on the ventilation volume and ventilation volume reference intervals, obtain the ventilation volume index (i.e., the specific limiting expression of the artificial respiration qualification assessment coefficient). The ventilation volume index represents the degree of agreement between the ventilation volume and the ventilation volume reference range, which includes the minimum and maximum ventilation volumes. The ventilation frequency index (i.e., the specific limiting expression of the artificial respiration qualification assessment coefficient) is obtained based on the ventilation frequency and the ventilation frequency reference range. The ventilation rate index represents the degree of agreement between the ventilation rate and the reference range, which includes the minimum and maximum ventilation rates. The ventilation depth index (i.e., the specific limiting expression of the artificial respiration qualification assessment coefficient) is obtained based on the chest rise height, chest descent height, original chest height, and the reference range of chest rise and fall. The ventilation depth index is used to indirectly reflect whether the ventilation depth is qualified through the degree of chest wall rise and fall. The reference range for chest wall rise and fall includes the maximum value of chest wall rise and the minimum value of chest wall fall. Based on the ventilation velocity and the ventilation velocity reference range, the ventilation velocity index (i.e., the specific limiting expression of the artificial respiration qualification assessment coefficient) is obtained. The ventilation rate index represents the degree of agreement between the ventilation rate and the ventilation rate reference range, which includes the minimum and maximum ventilation rates. The artificial respiration qualification assessment coefficient is obtained by coupling the ventilation volume index, ventilation frequency index, ventilation depth index, and ventilation rate index with the corresponding artificial respiration weighting factors. The specific constraint expression for the artificial respiration qualification assessment coefficient is as follows: ; ; ; ; ; In the formula, n represents the number of the preset time period. , This represents the total number of items in the preset time period, and j represents the category number of different groups of people. , This indicates the total number of population types. This represents the ventilation volume index of the trainees for the j-th group within the nth preset time period. This represents the ventilation frequency index of trainees for the j-th group within the nth preset time period. This represents the ventilation depth index of the trainees for the j-th group within the nth preset time period. This represents the airflow velocity index of trainees to the j-th group within the nth preset time period. This represents the ventilation volume of the trainee to the j-th group within the nth preset time period. This represents the minimum ventilation volume for the j-th group of people. This represents the maximum ventilation volume for the j-th group of people. This represents the ventilation frequency of the trainees to the j-th group within the nth preset time period. This represents the minimum ventilation frequency for the j-th group of people. This represents the maximum ventilation frequency for the j-th group of people. This represents the chest rise of the trainees in the j-th group within the nth preset time period. This represents the thoracic descent height of the trainees towards the j-th group within the nth preset time period. This represents the minimum thoracic descent height for the j-th group of people. This represents the maximum chest rise height for the j-th type of population. This represents the original chest wall height of the j-th group of people. This represents the airflow velocity of the trainees to the j-th group of people within the nth preset time period. This represents the minimum airflow velocity for the j-th population group. This represents the maximum airflow velocity for the j-th population group. This represents the qualification assessment coefficient of the trainee for artificial respiration in the j-th group within the nth preset time period.

[0030] In this embodiment, the algorithm combines ventilation volume, ventilation frequency, ventilation depth, and ventilation rate to comprehensively analyze and obtain the artificial respiration qualification assessment coefficient. In the formula, ventilation volume, ventilation frequency, ventilation depth, and ventilation rate are positively correlated with the artificial respiration qualification assessment coefficient, and their values ​​range from 0 to 1. As the ventilation volume, ventilation frequency, ventilation depth, and ventilation rate increase, the artificial respiration qualification assessment coefficient also increases. It can be seen that ventilation volume, ventilation frequency, ventilation depth, and ventilation rate are positively correlated with the artificial respiration qualification assessment coefficient. When all four indicators increase simultaneously, the breathing aspects of the trainees' artificial respiration instruction are more standardized. Evaluating breathing-related data helps to provide trainees with better feedback on their artificial respiration teaching outcomes and also facilitates a more comprehensive and visual assessment of artificial respiration instruction.

[0031] Specifically, ventilation volume directly affects the degree of chest expansion. The amount of gas introduced with each breath determines the height of chest rise, while the amount of gas expelled affects the height of chest descent. When ventilation volume increases, the height of chest rise increases accordingly, and after gas expulsion, the chest returns to near its original height. The coordination of ventilation volume and ventilation rate determines the total ventilation volume per minute. Ventilation volume exceeding the maximum ventilation volume or ventilation rate exceeding the maximum ventilation rate will increase airway pressure and affect the safety of lung ventilation. Ventilation volume and ventilation rate together affect the efficiency of gas input per unit time. The faster the ventilation rate, the shorter the corresponding ventilation volume needs to be delivered; conversely, the lower the ventilation rate, the longer it takes to achieve the same ventilation volume. At the same time, ventilation rate determines the speed at which gas enters the pleural cavity, directly affecting the rate of chest rise and fall. Higher ventilation rates usually lead to rapid chest rise, while lower ventilation rates result in slower chest rise.

[0032] Specifically, the artificial respiration weighting factor is obtained from a preset database, representing the degree of influence of the ventilation volume index on the artificial respiration qualification assessment coefficient. It is usually obtained through a preset data mapping table. The data mapping table is used to project the mapping relationship between the ventilation volume index and the artificial respiration qualification assessment coefficient. For example, by inputting the ventilation volume index, ventilation frequency index, ventilation depth index, and ventilation flow rate index from the ventilation volume index into the data mapping table, the corresponding ventilation volume weighting factor, ventilation frequency weighting factor, ventilation depth weighting factor, and ventilation flow rate weighting factor can be output. In addition, the sum of the artificial respiration weighting factors is 1.

[0033] It should be explained that the ventilation index is obtained by comprehensively analyzing the ventilation volume and the ventilation volume reference range. When the ventilation volume is within the ventilation volume reference range, the larger the value of the ventilation index, the more standardized the ventilation volume of the trainee's artificial respiration. The assessment of ventilation volume accurately quantifies the trainee's mastery of artificial respiration ventilation volume, which is conducive to a more comprehensive evaluation of the teaching results of visualized artificial respiration.

[0034] It should be explained that the ventilation frequency index is obtained by comprehensively analyzing the ventilation frequency and the ventilation frequency reference range. When the ventilation frequency is within the ventilation frequency reference range, the larger the value of the ventilation frequency index, the more standardized the ventilation frequency of the trainee's artificial respiration. The assessment of ventilation frequency accurately quantifies the trainee's mastery of artificial respiration ventilation frequency, which is conducive to a more comprehensive evaluation of the teaching results of visualized artificial respiration.

[0035] It should be explained that the ventilation depth index is obtained by comprehensively analyzing the chest rise height, chest descent height, original chest height, and chest rise reference range. The higher the degree of agreement between the chest rise range and the chest rise reference range, the larger the value of the ventilation depth index, indicating that the ventilation depth of the trainee is more standardized in performing artificial respiration. The assessment of ventilation depth accurately quantifies the trainee's mastery of artificial respiration ventilation depth, which is conducive to a more comprehensive evaluation of the teaching results of visualized artificial respiration.

[0036] It should be explained that the ventilation rate index is obtained by comprehensively analyzing the ventilation rate and the ventilation rate reference range. When the ventilation rate is within the ventilation rate reference range, the larger the value of the ventilation rate index, the more standardized the ventilation rate of the trainee's artificial respiration. The assessment of ventilation rate accurately quantifies the trainee's mastery of artificial respiration ventilation rate, which is conducive to a more comprehensive evaluation of the teaching results of visualized artificial respiration.

[0037] Specifically, reference artificial respiration data is obtained from a preset database. In one specific embodiment, reference artificial respiration data can be obtained by consulting medical literature. For example, by consulting medical literature, it can be found that the reference range for adult ventilation volume is generally [500, 6000] ml, the reference range for adult ventilation rate is generally [10, 12] minutes / blow, the reference range for adult ventilation flow rate is generally [10, 12] liters / minute, and the reference range for chest rise and fall is generally [0, 2.5] cm.

[0038] Furthermore, the specific process for determining whether to switch to AI voice guidance is as follows: Compare the artificial respiration qualification assessment coefficient with the breathing threshold obtained from the database: If the artificial respiration qualification assessment coefficient is not less than the breathing threshold, then the AI ​​voice guidance will not be switched; if the artificial respiration qualification assessment coefficient is less than the breathing threshold, then the student's artificial respiration learning process will be paused, and the AI ​​voice guidance will be used for teaching. The AI ​​voice guidance is used to inform the student of the breathing standard requirements through voice. The breathing threshold represents the minimum standard of compliance for breathing in the process of assessing the student's artificial respiration learning.

[0039] In this embodiment, timely AI voice feedback helps trainees understand their shortcomings and quickly adjust their operations. This rapid feedback mechanism helps improve trainees' learning efficiency, enabling them to acquire more accurate operational skills and reduce the pitfalls of repeated practice. Through this AI-based feedback adjustment mechanism, a more efficient, intelligent, and safe artificial respiration learning experience is provided, helping trainees to master first aid skills more efficiently.

[0040] Specifically, the breathing threshold is obtained from a preset database. This preset database is established before the construction of the intelligent building low-voltage system integrated control system to store various set data. The database includes, but is not limited to, historical average response data, low-voltage call assessment intervals, low-voltage assessment thresholds, and building assessment thresholds. These values ​​are directly set by technical personnel. For example, under normal circumstances, qualified artificial respiration data for the corresponding population type from historical data are substituted into the specific constraint expression of the artificial respiration qualification assessment coefficient to obtain the corresponding dataset. The result of averaging this dataset is recorded as the breathing threshold.

[0041] Furthermore, the artificial respiration action data includes hand offset, hand pressure, head tilt angle, and respiratory fit; hand offset refers to the offset of the trainee's hand position area from the preset hand position area; respiratory fit refers to the matching ratio between the trainee's mouth and the virtual puppet's mouth.

[0042] In this embodiment, by quantifying the detailed data of each artificial respiration operation, precise guidance, immediate correction, and safety assurance are achieved. This helps trainees master the standardized movements more quickly during artificial respiration learning, improves the effectiveness and scientific nature of artificial respiration training, and also provides a more refined data foundation for subsequent artificial respiration action evaluation.

[0043] Furthermore, the specific process for obtaining the action standardization evaluation index is as follows: Reference artificial respiration action data is obtained from a preset database. This data includes the maximum hand deflection, hand pressure reference range, and maximum head tilt angle. The hand pressure reference range includes the minimum and maximum hand pressure values. Logical quantification is performed on the obtained artificial respiration action data and the corresponding reference data to obtain quantified values ​​for each artificial respiration action. The results of coupling these quantified values ​​are then inversely calculated to obtain the action standardization evaluation index. The specific constraint expression for the action standardization evaluation index is as follows: ; ; In the formula, n represents the number of the preset time period. , This represents the total number of items in the preset time period, and j represents the category number of different groups of people. , This indicates the total number of population types. This represents the hand offset of the trainee relative to the j-th group of people within the nth preset time period. This represents the maximum hand offset of the j-th group of people. This represents the hand pressure index of trainees towards the j-th group within the nth preset time period. This represents the hand pressure exerted by the trainee on the j-th group of people during the nth preset time period. This represents the minimum hand pressure in the j-th group of people. This represents the maximum hand pressure in the j-th group of people. This represents the head tilt angle of the trainees towards the j-th group of people during the nth preset time period. This represents the maximum head tilt angle for the j-th group of people. This represents the degree of breathing compatibility between the trainees and the j-th group within the nth preset time period. This represents the trainee's evaluation index of the j-th group's proper conduct during the nth preset time period.

[0044] In this embodiment, the algorithm combines artificial respiration action data and corresponding reference artificial respiration action data to obtain an action standardization evaluation index. In the formula, the smaller the hand deviation is than the maximum hand deviation (i.e., ... When the hand pressure is within the reference range, the higher the value of the movement standardization assessment index, the more accurate the student's hand position. Similarly, when the hand pressure is within the reference range, the higher the value of the movement standardization assessment index, the more standardized the student's control over hand pressure. Likewise, when the head tilt angle is smaller than the maximum head tilt angle (i.e., ...), the higher the value of the movement standardization assessment index, the more accurate the student's hand position. The higher the action standardization assessment index, the more accurate the head tilt of the trainee. Furthermore, a higher breathing fit indicates a better match between the trainee's mouth and the virtual dummy's mouth, resulting in a higher action standardization assessment index and more standardized mouth movements. Additionally, during artificial respiration, the head tilt angle affects airway opening, and hand pressure affects chest compression effectiveness. These two actions need to be coordinated to ensure airway opening during compressions, allowing for smooth air delivery during ventilation. Furthermore, the trainee's hand movements during the transition between compressions and ventilations can easily affect breathing fit. A larger hand deviation may prevent the trainee from quickly and accurately locating the virtual dummy's mouth when moving to the mouth area, leading to reduced fit. Real-time assessment of trainee movements helps correct improper actions promptly, preventing the development of incorrect artificial respiration habits, thus achieving a more comprehensive assessment of artificial respiration instruction.

[0045] Specifically, reference artificial respiration action data is obtained from a preset database. In one specific embodiment, the reference artificial respiration action data is set according to the standards and requirements of the medical industry, which are set by medical experts in the medical field.

[0046] Furthermore, the specific process for determining whether to conduct MR simulation guidance is as follows: Compare the action standardization assessment index with the action threshold obtained from the database: If the action standardization assessment index is not less than the action threshold, then MR simulation guidance will not be conducted; if the action standardization assessment index is less than the action threshold, then the student's artificial respiration learning process will be paused, and the standard action will be demonstrated and the student will be prompted to conduct simulation practice. At the same time, the simulation practice will be tested, and the student's artificial respiration learning process will be resumed after the simulation practice test is passed.

[0047] In this embodiment, the system automatically determines whether MR guidance is needed based on the trainee's performance, thus enabling personalized teaching of artificial respiration. Trainees who meet the requirements can continue learning artificial respiration, while those who do not meet the requirements enter the MR simulation stage. MR simulation enhances the operation through multi-sensory guidance, helping trainees better master artificial respiration skills.

[0048] Specifically, the action threshold is obtained from a preset database. In one specific embodiment, qualified artificial respiration action data for the corresponding population type in historical data are substituted into the specific constraint expression of the action standardization assessment index to obtain the corresponding dataset, and the result of averaging the dataset is recorded as the action threshold.

[0049] like Figure 3The diagram shown is a flowchart illustrating the process for determining whether to conduct MR rhythm simulation teaching according to an embodiment of this application. The specific logic is as follows: An artificial respiration rhythm assessment index is obtained by combining the acquired chest compression time data with artificial respiration data. This index is then compared with a rhythm threshold. If the artificial respiration rhythm assessment index is not less than the rhythm threshold, artificial respiration teaching for the current population type proceeds to the next preset time period until a preset number of preset time periods of artificial respiration teaching for the current population type are completed, and the student is recorded as having completed artificial respiration teaching for the current population type. If the artificial respiration rhythm assessment index is less than the rhythm threshold, the student's artificial respiration learning process within the current preset time period ends, and the process is terminated before the next preset time period of artificial respiration teaching. The specific steps for conducting MR rhythm practice combined with AI voice guidance are as follows: First, demonstrate the standard artificial respiration procedure using MR and prompt the student to imitate and practice, while simultaneously monitoring and reminding the student's simulation and tracking process; next, use AI voice to ask the student if they want to proceed to the next preset time period for artificial respiration instruction. If yes, switch to the next preset time period for artificial respiration instruction; otherwise, automatically save the student's current artificial respiration learning progress until the student completes artificial respiration instruction for all types of people. Through the above process, not only can the quality of the visual teaching of artificial respiration be more comprehensively evaluated, but the quality of the student's artificial respiration learning outcomes can also be more accurately assessed.

[0050] Further, the specific steps for obtaining the artificial respiration rhythm assessment index are as follows: D1, monitor whether the trainee is performing both artificial respiration and chest compressions using MR. If both are present, proceed to D2; otherwise, do not perform an artificial respiration rhythm assessment. D2, obtain the breathing action time data from the artificial respiration data and obtain the chest compression time data using MR. The chest compression time data includes compression frequency and compression time. D3, obtain reference rhythm data from a preset database. The reference rhythm data includes reference breathing frequency, reference chest compression frequency, and reference ratio. The reference ratio represents the standard ratio when chest compressions and artificial respiration are combined for CPR. D4, obtain the artificial respiration rhythm assessment index based on the breathing action time data, chest compression time data, and reference rhythm data. The specific constraint expression for the artificial respiration rhythm assessment index is as follows: ; In the formula, n represents the number of the preset time period. , This represents the total number of items in the preset time period, and j represents the category number of different groups of people. , This indicates the total number of population types. This represents the breathing frequency of the trainees towards the j-th group within the nth preset time period. This represents the frequency at which trainees press on the j-th group of people within the nth preset time period. This represents the breathing time of the trainee towards the j-th group within the nth preset time period. This represents the time the student presses on the j-th group of people within the nth preset time period. This represents the reference proportion of the j-th group. This represents the reference respiratory rate for the j-th population group. This represents the reference compression frequency for the j-th group of people. This represents the assessment index of the artificial respiration rhythm of the trainees for the j-th group within the nth preset time period.

[0051] In this embodiment, the algorithm combines respiratory action time data, compression time data, and reference rhythm data to comprehensively analyze and obtain the artificial respiration rhythm assessment index. In the formula, as the respiratory rate approaches the reference respiratory rate (i.e., ...), the artificial respiration rhythm is evaluated. The closer the pressing frequency is to the reference pressing frequency (i.e.) The higher the artificial respiration rate assessment index, the more closely the respiratory rate and compression rate conform to the standard requirements for CPR; record the ratio of respiratory time to compression time (i.e., the ratio of breathing time to compression time). The time ratio is the ratio of time to compression time. The closer the time ratio is to the reference ratio, the higher the artificial respiration rhythm assessment index is, indicating that the alternation of breathing time and compression time is more in line with the standard requirements of CPR. By assessing the artificial respiration rhythm, it is beneficial to more comprehensively evaluate the visual teaching of artificial respiration, thereby improving the quality of the teaching results of artificial respiration for trainees.

[0052] Specifically, compression rate refers to the number of compressions performed per minute, and compression duration is the duration of each compression. These two metrics are interrelated; a higher compression rate requires shorter compression durations to complete the prescribed number of compressions within one minute. The coordination between compression rate and respiratory rate determines the overall rhythm of CPR. Typically, in CPR, the ratio of compressions to rescue breaths is 30:2, meaning two rescue breaths are given after 30 compressions. Therefore, compression rate directly affects the arrangement of respiratory rate. Respiratory rate refers to the number of breaths performed per minute, and respiratory duration is the duration of each breath. These two metrics are interrelated; a higher respiratory rate requires shorter breath durations to ensure the prescribed number of breaths is completed within one minute. Compression and respiratory times are alternated during CPR.

[0053] Furthermore, the specific process for determining whether to conduct MR rhythm simulation teaching is as follows: Compare the artificial respiration rhythm assessment index with the rhythm threshold obtained from the database: If the artificial respiration rhythm assessment index is not less than the rhythm threshold, then MR rhythm simulation teaching will not be conducted, and the artificial respiration teaching for the current population type will proceed to the next preset time period until the preset number of preset time periods of artificial respiration teaching for the current population type are completed, and the student will be recorded as having completed the artificial respiration teaching for the current population type; If the artificial respiration rhythm assessment index is less than the rhythm threshold, then the student's artificial respiration learning process within the current preset time period will end, and MR rhythm practice will be conducted before the artificial respiration teaching in the next preset time period, combined with AI voice guidance.

[0054] In this embodiment, it is possible to more accurately determine whether MR rhythm simulation teaching is needed, and selectively introduce MR guidance and AI voice prompts based on the trainee's rhythm performance, thereby achieving a more precise improvement in the trainee's rhythm control ability and ensuring the efficiency, safety and standardization of artificial respiration training.

[0055] Specifically, the rhythm threshold is obtained from a preset database. In one specific embodiment, qualified breathing action time data and compression time data for the corresponding population type in historical data are substituted into the specific constraint expression of the artificial respiration rhythm assessment index to obtain the corresponding dataset, and the result of averaging the dataset is recorded as the action threshold.

[0056] Further, the specific steps for conducting MR rhythm practice and combining AI voice guidance are as follows: Step 1, demonstrate the standard artificial respiration procedure through MR and prompt the student to imitate and practice, while detecting and reminding the student's simulation tracking process; Step 2, use AI voice to ask the student whether to proceed to the next preset time period for artificial respiration teaching. If yes, switch to the next preset time period for artificial respiration teaching; otherwise, automatically save the student's current artificial respiration learning progress until the student completes the artificial respiration teaching for all types of people.

[0057] In this embodiment, the MR demonstration combined with real-time detection provides trainees with more detailed guidance on the artificial respiration rhythm, which helps trainees to master the standard artificial respiration rhythm more accurately. Furthermore, through the MR rhythm demonstration, AI voice prompts, and real-time feedback mechanism, trainees can master the standard artificial respiration rhythm more quickly and accurately, ensuring the professionalism of the skills, thereby improving the overall teaching quality and learning experience.

[0058] In summary, this application embodiment determines whether to switch to AI voice guidance by using the artificial respiration qualification assessment coefficient obtained from artificial respiration data, then determines whether to conduct MR simulation guidance by using the action standard assessment index obtained from artificial respiration action data, and finally determines whether to conduct MR rhythm simulation teaching by using the artificial respiration rhythm assessment index obtained from the compression time data and artificial respiration data. This achieves comprehensive teaching and testing of students' artificial respiration learning, and thus realizes more accurate evaluation of artificial respiration through visual teaching, effectively solving the problem of incomplete visual teaching evaluation of artificial respiration in the prior art.

[0059] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0063] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0064] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A visualization teaching method based on the virtual fusion of AI and MR technologies, characterized in that, Includes the following steps: The system acquires the artificial respiration data of the trainee to the virtual dummy in real time within a preset time period through MR. Based on the artificial respiration data, the artificial respiration qualification assessment coefficient is obtained. The artificial respiration qualification assessment coefficient is compared with the breathing threshold to determine whether to switch to AI voice guidance. The artificial respiration qualification assessment coefficient is used to assess the qualification of the trainee's artificial respiration volume. The artificial respiration data includes ventilation volume, ventilation rate, chest rise height, chest descent height, initial chest height, ventilation flow rate, and respiratory action time data. The respiratory action time data includes respiratory rate and respiratory duration; The ventilation volume represents the average ventilation volume within a preset time period; The ventilation frequency represents the average ventilation frequency within a preset time period; The chest rise height represents the average chest rise height over a preset time period; The thoracic cavity descent height represents the average thoracic cavity descent height within a preset time period; The airflow velocity represents the average airflow velocity over a preset time period; The specific steps for obtaining the qualification assessment coefficient for artificial respiration are as follows: Each preset time period and population type is assigned a number; Reference artificial respiration data and artificial respiration weighting factors are obtained from a preset database. The reference artificial respiration data includes reference intervals for ventilation volume, ventilation rate, chest rise and fall, and ventilation flow rate. The artificial respiration weighting factors include a ventilation volume weighting factor, a ventilation rate weighting factor, a ventilation depth weighting factor, and a ventilation flow rate weighting factor. A ventilation index is obtained based on ventilation volume and a ventilation volume reference range, wherein the ventilation index represents the degree of agreement between ventilation volume and the ventilation volume reference range. A ventilation frequency index is obtained based on the ventilation frequency and the ventilation frequency reference range, wherein the ventilation frequency index represents the degree of agreement between the ventilation frequency and the ventilation frequency reference range. The ventilation depth index is obtained based on the chest rise height, chest descent height, original chest height and chest rise reference range. The ventilation depth index is used to reflect whether the ventilation depth is qualified by the degree of chest rise. A ventilation velocity index is obtained based on the ventilation velocity and the ventilation velocity reference range, wherein the ventilation velocity index represents the degree of agreement between the ventilation velocity and the ventilation velocity reference range. The artificial respiration qualification assessment coefficient is obtained by coupling the ventilation volume index, ventilation frequency index, ventilation depth index, and ventilation flow rate index with the corresponding artificial respiration weight factors after weighting calculation. AI acquires real-time data on trainees' artificial respiration actions within a preset time period. Based on the artificial respiration action data, an action standardization assessment index is obtained. The action standardization assessment index is compared with an action threshold to determine whether MR simulation guidance is needed. The action standardization assessment index is used to evaluate the degree of standardization of trainees' actions when performing artificial respiration. The artificial respiration data includes hand deflection, hand pressure, head tilt angle, and respiratory fit. The hand offset refers to the offset of the area of ​​the student's hand position from the area of ​​the preset hand position. The breathing fit refers to the proportion of the student's mouth that matches the mouth of the virtual doll; The specific process for obtaining the action standardization evaluation index is as follows: The reference artificial respiration action data is obtained from a preset database. The reference artificial respiration action data includes the maximum hand deflection, the hand pressure reference range, and the maximum head tilt angle. The hand pressure reference range includes the minimum hand pressure and the maximum hand pressure. By performing logical quantification on the acquired artificial respiration action data and the corresponding reference artificial respiration action data, the quantified value of each artificial respiration action is obtained. The result of coupling the quantitative values ​​of each artificial respiration action is inversely calculated to obtain the action standardization evaluation index. The specific process for determining whether to perform MR simulation guidance is as follows: Compare the action standardization assessment index with the action thresholds obtained from the database: If the motion standardization assessment index is not less than the motion threshold, then MR simulation guidance will not be provided; If the action standardization assessment index is less than the action threshold, the student's artificial respiration learning process will be suspended, and the student will be prompted to perform simulated practice. At the same time, the simulated practice will be tested. The student's artificial respiration learning process will be resumed after the simulated practice test is passed. The artificial respiration rhythm assessment index is obtained by acquiring compression time data within a preset time period through MR and combining it with artificial respiration data. The artificial respiration rhythm assessment index is compared with the rhythm threshold to determine whether to conduct MR rhythm simulation teaching. The artificial respiration rhythm assessment index is used to assess the degree to which the rhythm of the trainee's artificial respiration is in line with the target.

2. The visualization teaching method based on the virtual fusion of AI and MR technologies as described in claim 1, characterized in that: The specific process for determining whether to switch to AI voice guidance is as follows: The artificial respiration qualification assessment coefficient was compared with the breathing threshold obtained from the database: If the artificial respiration qualification assessment coefficient is not less than the breathing threshold, the AI ​​voice guidance will not be switched. If the artificial respiration qualification assessment coefficient is less than the breathing threshold, the student's artificial respiration learning process will be suspended and the teaching will be switched to AI voice guidance.

3. The visualization teaching method based on the virtual fusion of AI and MR technologies as described in claim 1, characterized in that: The specific steps for obtaining the artificial respiration rhythm assessment index are as follows: D1: Monitor the trainee's artificial respiration and chest compressions using MR. If both are present, proceed to D2; otherwise, do not assess the artificial respiration rhythm. D2, acquire respiratory action time data from artificial respiration data, and acquire compression time data through MR, the compression time data including compression frequency and compression time; D3. Obtain reference rhythm data from a preset database. The reference rhythm data includes reference breathing rate, reference compression rate, and reference ratio. D4 is an artificial respiration rhythm assessment index derived from respiratory action time data, compression time data, and reference rhythm data.

4. The visualization teaching method based on the virtual fusion of AI and MR technologies as described in claim 3, characterized in that: The specific procedure for determining whether to conduct MR rhythm simulation teaching is as follows: The artificial respiration rhythm assessment index was compared with rhythm thresholds obtained from the database: If the artificial respiration rhythm assessment index is not less than the rhythm threshold, then proceed to the next preset time period for artificial respiration teaching of the current population type until the preset number of preset time periods for artificial respiration teaching of the current population type are completed, and record that the student has completed artificial respiration teaching of the current population type. If the artificial respiration rhythm assessment index is less than the rhythm threshold, the artificial respiration learning process for the trainee in this preset time period will be terminated. At the same time, before the artificial respiration teaching in the next preset time period, MR rhythm practice will be carried out and guided by AI voice.

5. The visualization teaching method based on the virtual fusion of AI and MR technologies as described in claim 4, characterized in that: The specific steps for conducting MR rhythm practice and combining it with AI voice guidance are as follows: Step 1: Demonstrate the standard artificial respiration procedure using MR and prompt trainees to imitate and practice, while monitoring and reminding trainees during the simulation process; Step 2: Use AI voice to ask the student if they want to proceed with the next preset time period for artificial respiration instruction. If yes, switch to the next preset time period for artificial respiration instruction; otherwise, automatically save the student's current artificial respiration learning progress until the student completes artificial respiration instruction for all types of people.

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