A method and system for generating human-computer interactive visual effects based on physiological parameters

Abstract

This application relates to the field of critical care rehabilitation technology, specifically providing a method and system for generating human-computer interactive visual effects based on physiological parameters. The method includes the following steps: S1, acquiring multi-dimensional respiratory physiological parameters of the critically ill patient's current breathing and acquiring the critically ill patient's current vital sign parameters; S2, acquiring the respiratory contribution value of the critically ill patient's current breathing based on the multi-dimensional respiratory physiological parameters; S3, adjusting the overall visual model and local morphological details of virtual entities in the interactive display interface based on the cumulative value of the respiratory contribution value and the multi-dimensional respiratory physiological parameters; S4, adjusting the environmental background visual effects in the interactive display screen based on the comparison results of the current vital sign parameters with a first preset safety threshold and a second preset safety threshold. This method can effectively improve patients' training motivation and compliance, and enable medical staff to more comprehensively assess the patient's respiratory quality and overall physiological state.

Description

Technical Field

[0001] This application relates to the field of critical care rehabilitation technology, and more specifically, to a method and system for generating human-computer interactive visual effects based on physiological parameters. Background Technology

[0002] In the field of medical rehabilitation, especially for critically ill patients, restoring spontaneous breathing is a crucial part of their recovery process. Therefore, breathing training systems are often used to assist patients in their training. These systems collect patients' physiological data and transform it into visual feedback to guide breathing exercises. Initially, these systems often used relatively direct and simple visual presentation methods, such as directly linking the amount of air inhaled each time (tidal volume) to the height change of a bar graph on the screen. The basic design concept was to provide easy-to-understand feedback on respiratory depth, helping patients to take deep breaths and achieve preset inspiratory goals. This initial design was technically simple to implement, allowing for rapid deployment and providing immediate feedback.

[0003] However, as the course of illness progresses and the recovery period lengthens in critically ill patients, a gradually emerging problem arises: some patients experience significant fluctuations in cognitive abilities and attention span. These fluctuations may stem from underlying medical conditions, cognitive decline due to prolonged bed rest, or side effects of medication. In such cases, patients gradually find the aforementioned monotonous, linear, and unchanging forms of visual feedback tedious and boring. For example, facing a bar chart that merely shows alternating ups and downs for an extended period significantly weakens a patient's intrinsic motivation, making it difficult to maintain focus and training enthusiasm. This change in psychological state is not simply a lack of cooperation, but an objective manifestation of impaired physiological and psychological functions, directly affecting their level of engagement and adherence during training. When patients' attention is scattered and training becomes a mechanical repetition, their willingness to actively participate decreases, leading to a significant reduction in training effectiveness. This phenomenon reveals the limitations of basic visual feedback, initially considered sufficient, in maintaining long-term patient adherence when faced with the complex psychological and physiological states of critically ill patients.

[0004] Furthermore, during routine rounds and patient assessments, healthcare professionals observed that even when some patients, guided by visual feedback, achieved the system's target tidal volume, their breathing patterns might exhibit inefficiencies. For example, patients might adopt an excessively rapid breathing rate or inspiratory time to quickly reach the tidal volume target, resulting in inefficient gas exchange in the lungs and failure to fully utilize vital capacity. This phenomenon occurs because the current physiological data-to-visual element mapping function (i.e., focusing only on the linear relationship between tidal volume and bar chart height) fails to incorporate other equally important respiratory physiological data such as respiratory rate and inspiratory time. This means that although the system can collect this multi-dimensional data through respiratory flow sensors and chest and abdominal motion sensing bands, its visual feedback mechanism fails to effectively integrate and intuitively present this information. Therefore, healthcare professionals cannot comprehensively judge the overall quality of a patient's breathing based solely on the visual effects on the screen; for example, they cannot distinguish whether a successful deep breath is slow and effective or rapid and inefficient, making the assessment of the patient's breathing training effect incomplete and misleading.

[0005] There is currently no effective technical solution to the above problems. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for generating human-computer interactive visual effects based on physiological parameters, which can effectively improve patients' training enthusiasm and compliance, and enable medical staff to more comprehensively assess patients' breathing quality and overall physiological status.

[0007] In a first aspect, this application provides a method for generating human-computer interactive visual effects based on physiological parameters, which includes the following steps: S1. Obtain multi-dimensional respiratory physiological parameters of critically ill patients and obtain their current vital signs parameters; S2. Obtain the respiratory contribution value of critically ill patients' current breathing based on multi-dimensional respiratory physiological parameters; S3. Adjust the overall visual model and local morphological details of the virtual entities in the interactive display interface based on the cumulative value of respiratory contribution and multi-dimensional respiratory physiological parameters. S4. Adjust the environmental background visual effects in the interactive display screen based on the comparison results between the current vital signs parameters and the first preset safety threshold and the second preset safety threshold.

[0008] Secondly, this application also provides a human-computer interactive visual effects generation system based on physiological parameters, which includes: The parameter acquisition module is used to acquire multi-dimensional respiratory physiological parameters of critically ill patients and their current vital signs parameters. The contribution acquisition module is used to acquire the respiratory contribution value of critically ill patients' current breathing based on multi-dimensional respiratory physiological parameters. The entity adjustment module is used to adjust the overall visual model and local morphological details of virtual entities in the interactive display interface based on the cumulative value of respiratory contribution and multi-dimensional respiratory physiological parameters. The background adjustment module is used to adjust the environmental background visual effects in the interactive display screen based on the comparison results between the current vital signs parameters and the first preset safety threshold and the second preset safety threshold.

[0009] As can be seen from the above, the human-computer interactive visual effect generation method and system based on physiological parameters provided in this application combine the multi-dimensional respiratory physiological parameters and vital sign parameters of critically ill patients with interactive visual effects. By dynamically adjusting the visual model of virtual entities and the environmental background, it provides critically ill patients with richer and more attractive visual feedback, thereby effectively improving patients' training enthusiasm and compliance, and enabling medical staff to more comprehensively assess patients' respiratory quality and overall physiological state. This solves the problems of single visual feedback and incomplete assessment in the prior art. Attached Figure Description

[0010] Figure 1 A flowchart illustrating a method for generating human-computer interactive visual effects based on physiological parameters, provided in an embodiment of this application.

[0011] Figure 2 This is a schematic diagram of the structure of a human-computer interactive visual effect generation system based on physiological parameters, provided in an embodiment of this application.

[0012] Attached reference numerals: 1. Parameter acquisition module; 2. Contribution acquisition module; 3. Entity adjustment module; 4. Background adjustment module. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0014] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0015] Firstly, such as Figure 1 As shown, this application provides a method for generating human-computer interactive visual effects based on physiological parameters, which includes the following steps: S1. Obtain multi-dimensional respiratory physiological parameters of critically ill patients and obtain their current vital signs parameters; S2. Obtain the respiratory contribution value of critically ill patients' current breathing based on multi-dimensional respiratory physiological parameters; S3. Adjust the overall visual model and local morphological details of the virtual entities in the interactive display interface based on the cumulative value of respiratory contribution and multi-dimensional respiratory physiological parameters. S4. Adjust the environmental background visual effects in the interactive display screen based on the comparison results between the current vital signs parameters and the first preset safety threshold and the second preset safety threshold.

[0016] To facilitate understanding, some key terms in this embodiment are explained below. The multidimensional respiratory physiological parameters in this embodiment refer to multiple physiological indicators (such as tidal volume, inspiratory duration, and inspiratory flow smoothness) used to comprehensively assess the respiratory status of critically ill patients. These parameters collectively reflect the depth, rhythm, and efficiency of the patient's breathing, providing a data basis for subsequent visual effect adjustments. The vital sign parameters in this embodiment refer to physiological indicators reflecting the basic vital activity status of critically ill patients (such as blood oxygen saturation, heart rate, and respiratory rate). These parameters are important bases for assessing the patient's overall health status and training safety. The respiratory contribution value in this embodiment refers to a numerical value calculated based on the multidimensional respiratory physiological parameters, quantifying the quality of each breath and its contribution to rehabilitation training. Since this embodiment obtains the respiratory contribution value based on multidimensional respiratory physiological parameters, it effectively considers multiple dimensions of respiratory parameters in the process of obtaining the respiratory contribution value. Therefore, this embodiment avoids the one-sidedness of relying on a single indicator for assessment. Specifically, the larger the respiratory contribution value, the better the quality of the critically ill patient's current breathing, and the greater the contribution of the critically ill patient's current breathing to rehabilitation. The virtual entity in this embodiment refers to a graphical object (e.g., a virtual tree) presented in the interactive display interface for interaction with the patient. This virtual entity provides intuitive feedback on the patient's respiratory training effectiveness through changes in its overall visual model and local morphological details. The overall visual model in this embodiment refers to the macroscopic morphological manifestation of the virtual entity at different growth stages or states, such as the overall structural changes of a virtual tree from a seedling to a mature tree. This overall visual model can reflect the overall respiratory training effectiveness of critically ill patients; for example, the better the overall respiratory training effectiveness of a critically ill patient (the greater the cumulative respiratory contribution value), the thicker the virtual tree. The local morphological details in this embodiment refer to the microscopic visual manifestation of the components of the virtual entity based on the overall visual model (e.g., the color and shape of the virtual tree's leaves). These detailed changes can more precisely reflect the patient's real-time respiratory quality. The interactive display interface in this embodiment refers to a display interface used to present visual feedback information to critically ill patients and medical staff. The environmental background visual effect in this embodiment refers to the background image surrounding the virtual entity in the interactive display interface (e.g., sunny, cloudy, thunderstorms, etc.). Changes in this background effect are used to reflect the patient's vital signs and safety status in real time. In this embodiment, the first preset safety threshold and the second preset safety threshold refer to two preset values ​​used to assess the safety of the patient's vital signs parameters. When the vital signs parameters are lower than these thresholds, the system will trigger corresponding environmental background visual effects to provide visual warnings.

[0017] This application proposes a method for generating human-computer interactive visual effects based on physiological parameters. This method aims to improve the compliance and safety of respiratory training for critically ill patients and provide them with a personalized and immersive rehabilitation training experience by dynamically adjusting the visual feedback in the interactive display interface through real-time monitoring and analysis of respiratory parameters and vital signs parameters of critically ill patients.

[0018] In step S1, multidimensional respiratory physiological parameters of the critically ill patient's current respiration and current vital signs parameters are acquired. The acquisition of multidimensional respiratory physiological parameters can be achieved in various ways, such as using a respiratory flow sensor to monitor the patient's inspiratory and expiratory flow rates in real time, and obtaining tidal volume by integrating the inspiratory and expiratory flow rates; acquiring inspiratory duration using a chest and abdominal motion sensor; and obtaining inspiratory flow rate smoothness by performing smoothness analysis on the inspiratory flow waveform (e.g., calculating the difference between the maximum and minimum values ​​of the inspiratory flow waveform, and then dividing this difference by the average value of the inspiratory flow waveform). The acquisition of current vital signs parameters can be achieved using conventional medical monitoring equipment, such as using a pulse oximeter to monitor the patient's blood oxygen saturation in real time, using an electrocardiogram monitor to monitor heart rate, and using a blood pressure monitor to monitor blood pressure.

[0019] In step S2, the respiratory contribution value of the critically ill patient's current breathing is obtained based on multi-dimensional respiratory physiological parameters. This respiratory contribution value can be obtained in the following way: a positive baseline contribution value is preset. When the patient's tidal volume, inspiratory duration, and other parameters meet the preset ideal range, this baseline contribution value is assigned. If the patient's inspiratory flow rate is smooth, the baseline contribution value can be weighted, because a smooth inspiratory flow rate usually means more efficient gas exchange. If the respiratory rate is too fast or too slow, the corresponding respiratory contribution value is deducted. This calculation method can encourage patients to adopt a more efficient and healthier breathing pattern.

[0020] In step S3, the overall visual model and local morphological details of the virtual entity in the interactive display interface are adjusted based on the cumulative value of the respiratory contribution and multi-dimensional respiratory physiological parameters. For example, when the cumulative value of the respiratory contribution reaches a certain threshold, the overall visual model of the virtual entity (such as a tree) can grow from a seedling stage to a sapling stage, characterized by a thicker trunk and more branches and leaves. In the sapling stage, if the cumulative value continues to increase, it can further grow into a mature tree stage, characterized by a thicker trunk and more branches and leaves. This macroscopic visual change provides patients with long-term rehabilitation goals and a sense of accomplishment. Simultaneously, the system adjusts the local morphological details of the virtual entity based on multi-dimensional respiratory physiological parameters. For example, if the multi-dimensional respiratory physiological parameters indicate that the critically ill patient's breathing quality is good, the leaves can appear bright green and spread out; if the breathing quality is poor, the leaves may appear withered or curled. This detailed visual feedback can immediately guide critically ill patients to adjust their breathing patterns, thereby improving the immediate effectiveness of breathing training.

[0021] In step S4, the environmental background visual effects in the interactive display screen are adjusted based on the comparison results between the current vital sign parameters and the first and second preset safety thresholds. The adjustment of the environmental background visual effects is mainly used to reflect the patient's overall physiological safety status. For example, when the patient's blood oxygen saturation is higher than the first preset safety threshold, the environmental background can be displayed as a clear sky, symbolizing that the patient is in good condition; if the blood oxygen saturation drops to between the first and second preset safety thresholds, the environmental background becomes cloudy to alert medical staff; if the blood oxygen saturation further drops below the second preset safety threshold, the environmental background becomes thunderstorm, possibly accompanied by alarm information, to urgently prompt medical staff to intervene.

[0022] The human-computer interactive visual effect generation method based on physiological parameters proposed in this application solves the problems of low patient training enthusiasm and incomplete assessment by medical staff in the prior art by dynamically associating the multi-dimensional respiratory physiological parameters and vital sign parameters of critically ill patients with virtual entities and environmental background visual effects in the interactive display interface.

[0023] Specifically, traditional respiratory training systems focus only on a single tidal volume indicator, resulting in monotonous visual feedback that can easily bore patients and lead to poor training compliance. This application obtains multi-dimensional respiratory physiological parameters in step S1 and calculates respiratory contribution values ​​based on these parameters in step S2, making the assessment of patient respiratory quality more comprehensive. Furthermore, in step S3, based on the accumulated respiratory contribution value and multi-dimensional respiratory physiological parameters, the overall visual model and local morphological details of the virtual entity are dynamically adjusted. For example, the growth, leaf color, and shape of the virtual plant are changed according to the accumulated respiratory contribution value and multi-dimensional respiratory physiological parameters, providing rich and dynamic visual feedback for critically ill patients. This greatly enhances the fun and immersion of respiratory training, effectively solving the problem of low patient motivation. It should be understood that because critically ill patients can intuitively see how their efforts promote the growth and changes of the virtual life, this sense of accomplishment and participation is unparalleled by traditional bar chart feedback.

[0024] Furthermore, existing technologies fail to incorporate other crucial respiratory physiological data, such as respiratory rate and inspiratory time, making it difficult for healthcare professionals to comprehensively assess the overall quality of a patient's breathing. This application introduces multi-dimensional respiratory physiological parameters and combines them with adjustments to the local morphological details of virtual entities, enabling both healthcare professionals and patients to gain a more nuanced understanding of the quality of their breathing patterns. For example, when a patient's inspiratory flow smoothness is poor, the leaves of a virtual plant may appear withered or curled. This not only provides an immediate reminder for the patient to adjust their breathing but also offers healthcare professionals a more intuitive assessment basis, helping them distinguish between effective and inefficient breathing.

[0025] More importantly, this application introduces the monitoring of current vital sign parameters in step S4 and adjusts the environmental background visual effects based on the comparison results with preset safety thresholds. This innovation allows the system to not only focus on breathing training itself but also consider the patient's overall physiological safety. When the patient's vital signs become abnormal, the environmental background will change from "sunny" to "cloudy" or even "thunderstorm," accompanied by alarm information, thereby promptly alerting medical staff to intervene and ensuring that the patient can undergo rehabilitation training safely. This represents a significant improvement over existing technologies that only focus on the effectiveness of breathing training while neglecting the patient's overall physiological condition.

[0026] In summary, this application constructs a more attractive, comprehensive, and safer method for generating human-computer interactive visual effects for critically ill patients by comprehensively utilizing multi-dimensional physiological parameters, a dynamic and rich visual feedback mechanism, and real-time monitoring of the patient's overall physiological safety. This effectively improves the quality and efficiency of rehabilitation training and provides medical staff with more accurate assessment and intervention basis.

[0027] In some preferred embodiments, multidimensional respiratory physiological parameters include actual tidal volume, actual inspiratory duration, and actual inspiratory flow rate smoothness. Multidimensional respiratory physiological parameters refer to a set of multiple physiological indicators used to comprehensively assess the respiratory status of critically ill patients. These parameters can be obtained through various sensors and data processing technologies. It should be understood that, in addition to the parameters specified in this application, multidimensional respiratory physiological parameters may also include the inspiratory-to-expiratory ratio (IPR) and vital capacity, to provide a more comprehensive view of the respiratory status. Actual tidal volume refers to the volume of gas inhaled into the lungs during each inspiratory phase or the volume of gas exhaled during each expiratory phase. Its function is to serve as a basic indicator for measuring respiratory depth, reflecting the ventilation capacity of the lungs of critically ill patients. Actual tidal volume can be directly measured using a respiratory flow sensor, for example, by real-time monitoring of airflow using a differential pressure flow sensor or a hot-wire flow sensor, and the flow rate is calculated by integration. Actual inspiratory duration refers to the length of time a critically ill patient completes a single inspiratory phase. Its purpose is to assess respiratory efficiency and rhythm, distinguishing the patient's current breathing pattern (e.g., rapid breathing or deep breathing). Actual inspiratory duration can be precisely calculated by analyzing the waveform output from the respiratory flow sensor to identify the start and end points of the inspiratory phase. Actual inspiratory flow rate smoothness refers to the smoothness of airflow velocity changes during inspiration in a critically ill patient. Its purpose is to identify whether the breathing process is smooth and unobstructed. Actual inspiratory flow rate smoothness can be quantified through mathematical analysis of the inspiratory flow rate curve acquired by the respiratory flow sensor, for example, by calculating the standard deviation of the inspiratory flow rate curve over a specific time period.

[0028] In some preferred embodiments, step S2 includes: S21. Analyze whether the actual tidal volume is within the preset tidal volume range and the actual inhalation duration is within the preset inhalation duration range at the same time. If yes, the preset contribution values ​​corresponding to the preset tidal volume range and the preset inhalation duration range are used as the basic contribution values. If no, the basic contribution value is set to 0. S22. Obtain the actual respiratory rate of critically ill patients within the first preset time window; S23. Analyze whether the actual intake velocity smoothness is greater than or equal to the first preset velocity smoothness threshold. If so, extract the corresponding weighting coefficient from the pre-constructed mapping table of velocity smoothness and weighting coefficients based on the actual intake velocity smoothness as the target weighting coefficient, and use the product of the basic contribution value and the target weighting coefficient as the initial contribution value. If not, use the basic contribution value as the initial contribution value. The target weighting coefficient is greater than 1. The greater the actual intake velocity smoothness, the greater the target weighting coefficient. S24. Analyze whether the actual respiratory rate is within the preset respiratory rate range. If so, the sum of the preset contribution correction amount and the initial contribution value corresponding to the preset respiratory rate range is taken as the respiratory contribution value of the critically ill patient's current breathing. If not, based on the deviation between the actual respiratory rate and the preset respiratory rate range, the corresponding contribution correction amount is extracted from the pre-constructed mapping relationship table of respiratory rate deviation and contribution correction amount as the target contribution correction amount. Then, the sum of the target contribution correction amount and the initial contribution value is taken as the respiratory contribution value of the critically ill patient's current breathing. The target contribution correction amount is less than 0. The greater the deviation between the actual respiratory rate and the preset respiratory rate range, the smaller the target contribution correction amount.

[0029] In step S21, actual tidal volume and actual inspiratory duration are important physiological parameters reflecting a patient's respiratory volume and rhythm. The preset tidal volume range and preset inspiratory duration range represent healthy or target respiratory parameter ranges. It should be understood that the preset tidal volume range and preset inspiratory duration range can be set by clinicians based on the patient's individual condition, rehabilitation stage, or standard medical guidelines. The preset contribution value can be a fixed value (e.g., 10), or a value dynamically confirmed by medical staff based on the specific range of the preset tidal volume range and preset inspiratory duration range, as well as the current rehabilitation goals of the critically ill patient. Step S21 aims to establish a basic assessment of the respiratory quality of critically ill patients. By comparing the actual tidal volume and actual inspiratory duration with preset ideal ranges, the system can preliminarily determine whether the breathing of critically ill patients meets the basic requirements. Specifically, if both are within the preset range, the breathing is considered to meet the basic requirements, and a positive baseline contribution value is assigned to the breathing. If either parameter is outside the range, it indicates that the breathing has failed to meet the basic requirements, and the baseline contribution value of the breathing is set to zero, thereby avoiding the positive impact of ineffective or inefficient breathing on subsequent assessments.

[0030] Step S22 is used to obtain the respiratory rate of critically ill patients within a specific time period, providing crucial data for subsequent assessment and correction of respiratory rhythm. Respiratory rate is an important indicator of respiratory efficiency and the patient's physiological state; excessively fast or slow respiratory rates may indicate abnormal breathing patterns. The actual respiratory rate can be obtained as follows: the system can continuously monitor respiratory flow or chest and abdominal movement signals, identify the start or end of each breath using a peak detection algorithm, and then calculate the number of breaths occurring within a first preset time window (e.g., 30 seconds or 60 seconds) to derive the average respiratory rate.

[0031] Step S23 aims to assess the smoothness of the patient's inspiratory process using a first preset flow smoothness threshold and accordingly weight the baseline contribution value. Since a smooth inspiratory flow rate generally implies more efficient pulmonary gas exchange and lower respiratory effort, this embodiment encourages critically ill patients to adopt a smooth inspiratory mode by introducing a target weighting coefficient determined based on the actual inspiratory flow smoothness when the actual inspiratory flow smoothness is greater than or equal to the first preset flow smoothness threshold, thus weighting the baseline growth contribution. It should be understood that the first preset flow smoothness threshold can be a value pre-set by medical staff based on clinical experience or the patient's rehabilitation goals. A pre-constructed mapping table of flow smoothness and weighting coefficients stores different flow smoothness intervals and their corresponding weighting coefficients. The construction of this table is prior art and will not be discussed in detail here. Since the target weighting coefficient in this embodiment is greater than 1 and increases with the increase of the actual inspiratory flow smoothness, this embodiment is equivalent to rewarding the adoption of a smooth breathing mode.

[0032] Step S24 performs a final correction on the initial contribution value to penalize unhealthy respiratory rates. Specifically, even if the patient's tidal volume, inspiratory duration, and inspiratory stability are all within the target range, an excessively fast or slow respiratory rate may still affect respiratory efficiency and patient comfort. The preset respiratory rate range represents the normal or target respiratory rate range. The preset contribution correction is a correction value given when the respiratory rate is within the normal range; this preset contribution correction is greater than 0. If the actual respiratory rate is within the preset respiratory rate range, the preset contribution correction is summed with the initial contribution value to obtain the final respiratory contribution value. If the actual respiratory rate deviates from the preset range, a target contribution correction less than 0 is extracted from a pre-constructed mapping table of respiratory rate deviation and contribution correction based on the degree of deviation. This correction decreases as the deviation increases, penalizing abnormal respiratory rates, and is summed with the initial contribution value to obtain the final respiratory contribution value. The mapping table of respiratory rate deviation and contribution correction amount in this embodiment stores different respiratory rate deviation ranges and their corresponding contribution correction amounts. The construction of this table belongs to the prior art and will not be discussed in detail here.

[0033] This embodiment, through a series of logically rigorous steps, achieves a comprehensive and dynamic assessment of the respiratory quality of critically ill patients, thereby generating a respiratory contribution value that accurately reflects respiratory efficiency and health status. First, the system performs preliminary screening based on the actual tidal volume and actual inspiratory duration of each breath, ensuring that only breaths meeting basic requirements receive a positive baseline contribution value. This mechanism effectively avoids interference from ineffective or inefficient breathing in subsequent assessments, laying the foundation for identifying high-quality breathing. Based on this, the system acquires the patient's actual respiratory rate within a first preset time window, providing crucial data for subsequent respiratory rhythm assessment. Subsequently, this scheme introduces consideration of the smoothness of actual inspiratory flow rate. By multiplying the baseline contribution value by a target weighting coefficient determined based on the smoothness of actual inspiratory flow rate, the system provides additional rewards for smooth and efficient inspiratory patterns. This weighting mechanism effectively guides patients to perform smooth inspiratory breathing, rather than irregular breathing, thereby improving pulmonary gas exchange efficiency. Finally, this solution corrects the initial contribution value based on the actual respiratory rate. Specifically, the system compares the actual respiratory rate with a preset respiratory rate range. For breaths that deviate from the ideal rate, a negative target contribution correction amount is applied according to the magnitude of the deviation. This correction mechanism ensures that patients can maintain a healthy respiratory rhythm while pursuing tidal volume and inspiratory stability, avoiding potential physiological burdens caused by abnormal respiratory rate. Through the organic combination of the above steps, this solution integrates respiratory parameters from several dimensions, including actual tidal volume, actual inspiratory duration, actual inspiratory flow smoothness, and actual respiratory rate, to form a comprehensive evaluation model. This multi-dimensional, phased evaluation method overcomes the problems of incomplete and inaccurate evaluation caused by traditional methods that only focus on a single parameter. Therefore, this embodiment can not only identify breaths that meet basic standards, but also further distinguish high-quality, stable breaths and correct unhealthy respiratory rhythms, so that the generated respiratory contribution value can more comprehensively and accurately reflect the respiratory quality and efficiency of critically ill patients, thus providing a precise basis for adjusting the visual effects of virtual entities in the subsequent interactive display interface.

[0034] As a specific implementation method, the process of obtaining the respiratory contribution value of a critically ill patient's current breathing can be performed as follows: Assume the system sets a preset tidal volume range of [400ml, 600ml] and a preset inspiratory duration range of [2s, 4s] for the current rehabilitation stage, with a preset contribution value of 10. After a critically ill patient completes a breath, the system first analyzes their actual tidal volume and actual inspiratory duration. For example, if the critically ill patient's actual tidal volume is 500ml and the actual inspiratory duration is 3s, both are within the preset range, and the baseline contribution value is 10. If the critically ill patient's actual tidal volume is 350ml (outside the preset tidal volume range), the baseline contribution value is set to 0. Next, the system obtains the critically ill patient's actual respiratory rate within a first preset time window (e.g., the past 30 seconds), for example, an actual respiratory rate of 15 breaths / minute. Subsequently, the system evaluates the smoothness of the actual inspiratory flow rate during the breath. Assuming a first preset flow rate smoothness threshold of 0.7, and that the system has pre-built a mapping table between flow rate smoothness and weighting coefficients, if the actual inspiratory flow rate smoothness is 0.85 (greater than the first preset flow rate smoothness threshold), the system will extract the corresponding weighting coefficient from this mapping table as the target weighting coefficient based on the actual inspiratory flow rate smoothness. For example, if the mapping table indicates a smoothness of (0.7-0.8], the weighting coefficient is 1.1; if the smoothness is (…), the weighting coefficient is 1.1. When the smoothness is between 0.8 and 0.9, the weighting coefficient is 1.2; when the smoothness is between 0.9 and 1, the weighting coefficient is 1.3. Since the current actual inspiratory flow smoothness is 0.85, the target weighting coefficient is 1.2, and the initial contribution value is 12 (the product of the base contribution value and the target weighting coefficient). If the actual inspiratory flow smoothness is 0.6 (less than the first preset flow smoothness threshold), the initial contribution value is 10 (the base contribution value). Finally, the system analyzes the actual respiratory rate, assuming the preset respiratory rate range is [12 breaths / minute, ... [18 breaths / minute], the preset contribution correction for the preset respiratory rate range is 5. If the actual respiratory rate is 15 breaths / minute (within the preset respiratory rate range), the respiratory contribution value is 17 (the sum of the initial contribution and the preset contribution correction). If the actual respiratory rate is 20 breaths / minute (outside the preset respiratory rate range), the system will extract the target contribution correction from the mapping table of respiratory rate deviation and contribution correction based on the deviation between the actual respiratory rate and the preset respiratory rate range (2 breaths / minute). For example, if the mapping table indicates that the absolute value of the deviation is (0.5], the contribution correction is -3; the absolute value of the deviation is (5-10], the contribution correction is -5; and the absolute value of the deviation is greater than 10, the contribution correction is -10. Since the deviation between the actual respiratory rate and the preset respiratory rate range is 2 breaths / minute, the target contribution correction is -3, and the respiratory contribution value is 9.

[0035] In some preferred embodiments, step S2 further includes steps performed before step S21: A1. The rehabilitation stage corresponding to the preset mapping interval to which the accumulated respiratory contribution value belongs is taken as the current rehabilitation stage; A2. Based on the current rehabilitation stage, extract the corresponding preset parameter combination from the pre-constructed mapping table of rehabilitation stage and preset parameter combination; the preset parameter combination includes preset tidal volume range, preset inspiratory duration range, preset contribution value, first preset flow smoothness threshold, preset respiratory rate range and preset contribution correction amount; A3. Obtain the trends of tidal volume and inspiratory flow rate smoothness in critically ill patients within the second preset time window; A4. Calculate the standard deviation of blood oxygen saturation in critically ill patients within the second preset time window, and calculate the respiratory rate deviation based on the respiratory rate of critically ill patients within the second preset time window and the historical respiratory rate. A5. If the trend of tidal volume change indicates an increase in actual tidal volume, the trend of inspiratory flow rate smoothness change indicates an increase in actual inspiratory flow rate smoothness, the standard deviation of blood oxygen saturation is less than or equal to the first preset threshold, and the respiratory rate deviation is less than or equal to the second preset threshold, then increase the upper and lower limits of the preset tidal volume range, the preset inspiratory duration range, and the preset respiratory rate, and increase the first preset flow rate smoothness threshold. A6. If the trend of tidal volume change indicates that the actual tidal volume is decreasing, the trend of inspiratory flow rate smoothness change indicates that the actual inspiratory flow rate smoothness is decreasing, the standard deviation of blood oxygen saturation is greater than the first preset threshold, or the deviation of respiratory rate is greater than the second preset threshold, then reduce the upper and lower limits of the preset tidal volume range, the preset inspiratory duration range, and the preset respiratory rate, and reduce the first preset flow rate smoothness threshold.

[0036] Step A1 aims to dynamically identify the patient's rehabilitation level based on their long-term training performance. The preset mapping interval refers to multiple numerical ranges pre-set for the cumulative value of respiratory contribution. Each numerical range is associated with a specific rehabilitation stage. These intervals can be divided according to clinical experience or the rehabilitation patterns of patient groups. For example, the system can set the initial rehabilitation stage when the cumulative value is less than 1000, the intermediate rehabilitation stage when the cumulative value reaches 1000, and the late rehabilitation stage when the cumulative value reaches 5000.

[0037] Step A2 aims to ensure that the criteria used to assess a patient's respiratory performance are aligned with the patient's current stage of rehabilitation. The pre-built mapping table between rehabilitation stages and preset parameter combinations is a data structure storing different rehabilitation stages and their corresponding assessment parameters; the construction of this table is prior art. The preset parameter combination is a set of key parameters used to assess the patient's respiratory quality and calculate respiratory contribution values. It includes a preset tidal volume range, a preset inspiratory duration range, a preset contribution value, a first preset flow smoothness threshold, a preset respiratory rate range, and a preset contribution correction amount. For example, for patients in the early stages of rehabilitation, the preset tidal volume range may be set more leniently, while for patients in later stages, the range may be set more strictly.

[0038] Step A3 aims to capture short-term dynamic changes in the patient's breathing pattern. The second preset time window refers to a period of time used to analyze the patient's recent physiological data, such as the last 5 minutes, the last 10 breaths, or the last 30 minutes. The tidal volume change trend indicates whether the patient's actual tidal volume shows an overall trend of increasing, decreasing, or remaining stable within the second preset time window. This trend can be determined by performing linear regression analysis on multiple tidal volume data points within the window, or by comparing the average values ​​of the first and second halves of the window. The inspiratory flow smoothness change trend indicates whether the patient's actual inspiratory flow smoothness shows an overall trend of increasing, decreasing, or remaining stable within the second preset time window. This trend is obtained in a similar manner to the tidal volume change trend. It should be understood that obtaining the tidal volume change trend based on the actual tidal volume within the second preset time window and the inspiratory flow smoothness change trend based on the actual inspiratory flow smoothness within the second preset time window are existing technologies, and their working principles will not be discussed in detail here.

[0039] Step A4 aims to assess the patient's physiological stability during recent training. The standard deviation of blood oxygen saturation is a statistic that measures the degree of fluctuation in blood oxygen saturation within a second preset time window. The smaller the standard deviation, the more stable the blood oxygen saturation. The respiratory rate deviation refers to the difference between the patient's average respiratory rate within the second preset time window and a reference historical respiratory rate, which can be the patient's baseline respiratory rate, the average respiratory rate of the previous hour, or the clinically set target respiratory rate.

[0040] Step A5 is the mechanism by which the system proactively increases the training challenge based on the patient's positive performance and physiological stability. The first preset threshold is a pre-set value used to determine whether the standard deviation of blood oxygen saturation is within an acceptable stable range; for example, it can be set to 2%. The second preset threshold is a pre-set value used to determine whether the respiratory rate deviation is within an acceptable stable range; for example, it can be set to 3 breaths / minute. For example, the system can increase the upper and lower limits of the current preset tidal volume range by 5% each, while simultaneously increasing the first preset flow smoothness threshold by 10%.

[0041] Step A6 is a mechanism by which the system proactively reduces training challenge or increases tolerance based on the patient's negative performance or signs of physiological instability. For example, the system can reduce the upper and lower limits of the current preset tidal volume range by 5% each, while simultaneously reducing the first preset flow smoothness threshold by 10%.

[0042] This embodiment introduces a mechanism for dynamically adjusting preset parameter combinations, enabling the assessment criteria for respiratory training to adaptively adjust as the rehabilitation process of critically ill patients progresses and their real-time physiological states fluctuate. Specifically, the system first determines the patient's current rehabilitation stage based on the accumulated value of respiratory contribution and extracts a preset parameter combination matching that stage from a pre-built mapping table. This ensures that the baseline parameters used to assess respiratory performance are always adapted to the patient's overall rehabilitation level, avoiding the problem of fixed parameters failing to reflect the patient's long-term progress. Furthermore, the system monitors the patient's tidal volume and inspiratory flow smoothness trends in real time within a second preset time window, and calculates the standard deviation of blood oxygen saturation and respiratory rate deviation to comprehensively assess the patient's short-term physiological stability and training performance. When the patient exhibits positive physiological trends and good stability, the system appropriately increases the upper and lower limits of the preset tidal volume range, preset inspiratory duration range, and preset respiratory rate, and increases the first preset flow smoothness threshold, thereby increasing the training challenge to encourage continuous patient progress. Conversely, when a patient exhibits signs of declining or unstable physiological condition, the system promptly reduces these parameters, lowering the assessment threshold to alleviate the patient's burden, prevent overtraining, and ensure the safety of the training process. This dynamic adjustment mechanism directly impacts the calculation of the respiratory contribution value, enabling it to more accurately and sensitively reflect the quality and effort of each breath. Therefore, this implementation provides a more precise and personalized basis for adjusting the overall visual model and local morphological details of virtual entities in the subsequent interactive display interface, thereby significantly improving the adaptability, effectiveness, and safety of respiratory training.

[0043] The following is a concrete example to illustrate this. Suppose a critically ill patient is undergoing respiratory rehabilitation training. The system continuously tracks the cumulative value of their respiratory contribution. When the cumulative value reaches 1200 points, the system determines the current rehabilitation stage as the intermediate rehabilitation stage based on a preset mapping interval (e.g., 0-1000 points for the initial rehabilitation stage, 1001-5000 points for the intermediate rehabilitation stage). Subsequently, the system extracts the preset parameter combination corresponding to the intermediate rehabilitation stage from a pre-built mapping table. For example, the preset tidal volume interval is [400ml, 600ml], the preset inspiratory duration interval is [1.5s, 2.5s], and the first preset flow smoothness threshold is 70. In the next 5 minutes (the second preset time window), the system monitors that the patient's average tidal volume increases from 450ml to 480ml, and the inspiratory flow smoothness increases from 72 to 78, indicating that both the tidal volume and inspiratory flow smoothness trends indicate an increase. Simultaneously, the standard deviation of blood oxygen saturation is calculated to be 1.2% (less than the first preset threshold of 2%), and the respiratory rate deviation is 1 breath / min (less than the second preset threshold of 3 breaths / min). Since all conditions meet the requirements for active adjustment, the system will increase the preset parameters. For example, the preset tidal volume range will be adjusted to [420ml, 620ml], the preset inspiratory duration range will be adjusted to [1.6s, 2.6s], and the first preset flow smoothness threshold will be adjusted to 75. These adjusted parameters will be used to calculate the subsequent respiratory contribution value, making the training target more challenging to promote further patient recovery. Conversely, if, in another time period, the system detects that the patient's tidal volume and inspiratory flow smoothness both show a decreasing trend, and the standard deviation of blood oxygen saturation is greater than the first preset threshold, the system will decrease these preset parameters. For example, the preset tidal volume range will be adjusted to [380ml, 580ml] to reduce the training difficulty and ensure patient safety.

[0044] In some preferred embodiments, step S3 includes: S31. Take the growth stage corresponding to the preset mapping interval to which the cumulative value of the respiratory contribution value belongs as the current growth stage; S32. Based on the current growth stage, extract the corresponding overall visual model from the pre-built mapping relationship table between the growth stage and the overall visual model as the target visual model, and then adjust the overall visual model of the virtual entity in the interactive display interface to the target visual model; the target visual model has several visual elements. S33. Adjust the size of the overall visual model of the virtual entity in the interactive display interface according to the cumulative value of the breathing contribution value; the larger the cumulative value of the breathing contribution value, the larger the overall visual model of the virtual entity in the interactive display interface. S34. Adjust the visual effects and / or colors of visual elements based on multi-dimensional respiratory physiological parameters to adjust the local morphological details of virtual entities in the interactive display interface.

[0045] Step S31 is used to visualize the abstract cumulative respiratory contribution value into easily understood and perceived growth stages, providing patients with a sense of accomplishment and training goals at each stage. Specifically, the system can preset a series of cumulative respiratory contribution value intervals, each interval corresponding to a specific growth stage. For example, 0-500 points corresponds to the "seedling stage," 501-1500 points corresponds to the "small tree stage," 1501-3000 points corresponds to the "mature tree stage," and above 3001 points corresponds to the "flowering and fruiting stage." When the cumulative respiratory contribution value falls into a certain interval, the growth stage corresponding to that interval is determined as the current growth stage.

[0046] Step S32 is used to dynamically present virtual entities of different forms based on the cumulative respiratory contribution value of critically ill patients, thereby enhancing the richness and attractiveness of visual feedback. Specifically, the system can maintain a mapping table that stores different growth stages (such as seedling stage, sapling stage, large tree stage, flowering and fruiting stage) and their corresponding 3D model files or visual resource packages. When the current growth stage is determined, the system queries and loads the corresponding overall visual model (e.g., a seedling model, a small tree model, a large tree model, or a tree model with flowers and fruits) from the mapping table and renders it onto the interactive display interface.

[0047] Step S33 aims to intuitively reflect the patient's training progress and effort level in the current growth stage to provide continuous motivation. Specifically, the system can predefine an amplification function that takes the cumulative respiratory contribution value as input and outputs an amplification ratio. This amplification ratio is then used to amplify the overall visual model of the virtual entity in the interactive display interface. Alternatively, the system can pre-build a mapping table between the cumulative respiratory contribution value and the amplification ratio. This table stores the amplification ratios corresponding to different cumulative respiratory contribution value ranges. In actual use, the corresponding amplification ratio is extracted from the mapping table based on the current cumulative respiratory contribution value, and then this amplification ratio is used to amplify the overall visual model of the virtual entity in the interactive display interface.

[0048] Step S34 is used to map real-time, multi-dimensional respiratory physiological parameters onto local details of the virtual entity to provide refined, real-time feedback on respiratory quality. The system can dynamically adjust the color, shape, transparency, or animation effects of specific visual elements (such as leaves and flowers) on the virtual entity based on parameters such as actual tidal volume, actual inspiratory duration, and smoothness of actual inspiratory flow rate. For example, when respiratory parameters are good, leaves appear bright green and unfurled; when respiratory parameters are poor, leaves may appear yellowish and curled.

[0049] This embodiment links the cumulative respiratory contribution value of critically ill patients to the growth stage of a virtual entity and finely adjusts the local morphological details of the virtual entity based on multi-dimensional respiratory physiological parameters, thereby providing a dynamic, personalized, and attractive human-computer interactive visual effect. Specifically, the system first continuously monitors the cumulative respiratory contribution value of the patient. When the cumulative value increases from the interval corresponding to the small tree stage to the interval corresponding to the small tree stage, the system adjusts the overall visual model of the virtual entity from the overall visual model corresponding to the seedling stage to the overall visual model corresponding to the small tree stage. This staged growth not only provides patients with clear training goals and a sense of accomplishment but also makes the visual feedback no longer a simple linear change but presents a rich life cycle evolution. On this basis, within each growth stage, the overall size of the virtual entity is continuously and progressively adjusted according to the cumulative respiratory contribution value. Specifically, the larger the cumulative respiratory contribution value, the larger the overall visual model of the virtual entity, which intuitively reflects the patient's continuous efforts and progress in training, further enhancing the patient's training enthusiasm. More ingeniously, this solution also introduces adjustments to the local morphological details of the virtual entity. The system analyzes multi-dimensional respiratory physiological parameters in real time, such as actual tidal volume, actual inspiratory duration, and smoothness of actual inspiratory flow rate, and maps the real-time status of these parameters to the visual effects and / or colors of visual elements (such as leaves and flowers) on virtual entities. For example, when a patient's breathing pattern conforms to the ideal state, the leaves may appear lush and vibrant; while when the breathing pattern deviates, the leaves may appear withered, yellowed, or curled. This refined local adjustment allows the visual feedback to comprehensively reflect the quality of the patient's breathing, rather than just whether a single indicator is met, thus helping medical staff to more accurately assess the patient's respiratory efficiency and guide the patient to conduct more effective breathing training. Through the above mechanism, this solution transforms abstract physiological data into vivid and dynamic visual feedback, effectively solving the problems of traditional visual feedback being monotonous, dull, and difficult to maintain long-term patient compliance. At the same time, by integrating multi-dimensional respiratory physiological parameters and mapping them to local details, it overcomes the limitation of traditional solutions in comprehensively reflecting respiratory quality, enabling medical staff to more intuitively judge the overall quality of the patient's breathing, thereby improving the compliance and actual effectiveness of breathing training for critically ill patients.

[0050] In some preferred embodiments, the growth stages include the seedling stage, sapling stage, mature tree stage, and flowering and fruiting stage. During the seedling stage, the target visual model consists of a trunk and several leaves. During the sapling stage, the target visual model consists of a trunk, several first branches, and multiple leaves, with each first branch having several leaves. The diameter of the trunk in the sapling stage is larger than that in the seedling stage. During the mature tree stage, the target visual model consists of a trunk, multiple second branches, multiple leaves, and multiple flower buds. The number of second branches is greater than the number of first branches, and each second branch has multiple leaves. There are multiple leaves and several flower buds. The diameter of the trunk in the mature tree stage is larger than that in the sapling stage. The diameter of all second branches is larger than that of all first branches. When the growth stage is flowering and fruiting, the target visual model consists of the trunk, multiple second branches, multiple leaves, several flowers, and several fruits. The diameter of the trunk in the flowering and fruiting stage is greater than or equal to that of the trunk in the mature tree stage. The diameter of the second branches in the flowering and fruiting stage is greater than or equal to that of the second branches in the mature tree stage. Each flower bud corresponds to a flower or a fruit. Leaves, flower buds, flowers, and fruits are all visual elements.

[0051] The growth stages (i.e., seedling stage, sapling stage, mature tree stage, and flowering and fruiting stage) are the different developmental stages presented by the virtual entity in the interactive display interface, representing the gradual process of the critically ill patient's recovery progress. In the seedling stage, the target visual model consists of a trunk and several leaves. This is the visual representation of the virtual entity in the early stages of recovery (seedling stage). Specifically, the system can pre-store various visual model resources for the seedling stage, such as combinations of trunks and leaves of different shapes or colors, which can be selected based on the patient's preferences or system default settings. When the growth stage is the sapling stage, the target visual model consists of a trunk, several first branches, and multiple leaves. Each first branch has several leaves, and the diameter of the trunk in the sapling stage is larger than that in the seedling stage. This is the visual representation of the virtual entity in the mid-rehabilitation stage (sapling stage). The virtual entity in the sapling stage has grown and become more complex compared to the seedling stage. Specifically, when the current growth stage changes from the seedling stage to the sapling stage, the system can load a preset sapling stage model resource. This model includes a thicker trunk than in the seedling stage, newly added first branches, and more leaves distributed on each first branch. By adding new visual elements (first branches) and size changes (increased trunk diameter), the system can intuitively display the patient's rehabilitation progress, provide a visual sense of growth, and motivate the patient to continue training. During the tree-growing stage, the target visual model consists of a trunk, multiple second branches, multiple leaves, and multiple flower buds. The number of second branches is greater than the number of first branches. Each second branch has multiple leaves and several flower buds. The diameter of the trunk in the tree-growing stage is greater than that in the sapling stage, and the diameter of all second branches is greater than the diameter of all first branches. This is the visual representation of the virtual entity in the later stage of rehabilitation (tree-growing stage). When the current growth stage changes from the sapling stage to the tree-growing stage, the system can load a preset tree-growing stage model resource. This model has a thicker trunk, more and thicker second branches, and introduces flower bud elements. Through a more complex structure (more and thicker second branches) and predictive elements (flower buds), the richness of visual feedback is further enhanced, maintaining the patient's training interest and predicting the upcoming results.When the growth stage is flowering and fruiting, the target visual model consists of a trunk, multiple second branches, multiple leaves, several flowers, and several fruits. The diameter of the trunk in the flowering and fruiting stage is greater than or equal to the diameter of the trunk in the mature tree stage, and the diameter of the second branch in the flowering and fruiting stage is greater than or equal to the diameter of the second branch in the mature tree stage. Each flower bud corresponds to a flower or a fruit. This is the visual representation of the virtual entity at the end of the rehabilitation period (flowering and fruiting stage). When the current growth stage changes from the mature tree stage to the flowering and fruiting stage, the system can load the preset flowering and fruiting stage model resources. Based on the mature tree stage, this model replaces the flower buds with flowers or fruits. By intuitively displaying flowers and fruits, it provides patients with a clear sense of accomplishment and visual reward, significantly improving training compliance and motivation.

[0052] This embodiment clearly defines four growth stages: seedling stage, sapling stage, mature tree stage, and flowering and fruiting stage. Each stage has a specific virtual entity visual model, allowing the virtual entities in the interactive display interface to gradually evolve from simple trunks and leaves into complex forms with branches, buds, flowers, and fruits, based on the accumulated respiratory contribution value of the critically ill patient. This phased, progressive visual evolution, combined with the aforementioned technique of adjusting the overall visual model of the virtual entity based on the accumulated respiratory contribution value, allows the patient's recovery progress to be presented intuitively and dynamically. For example, when the patient's accumulated respiratory contribution value reaches a preset threshold, the virtual entity will transform from a seedling stage to a sapling stage, with an increased trunk diameter and the growth of the first branch and more leaves. This visual sense of growth can continuously motivate the patient. As the respiratory contribution value further accumulates, the virtual entity will continue to evolve into a mature tree stage, with more second branches and buds appearing, until finally entering the flowering and fruiting stage, where the buds transform into flowers and fruits. This design cleverly transforms abstract physiological data into vivid and dynamic visual feedback, enabling patients to clearly perceive their own efforts and rehabilitation progress. This effectively solves the problems of traditional visual feedback being singular, abstract, and unable to intuitively reflect rehabilitation progress, significantly enhancing patients' training interest and sustained participation.

[0053] In some preferred embodiments, the multidimensional respiratory physiological parameters include actual tidal volume, actual inspiratory duration, and actual inspiratory flow rate smoothness. Step S34 includes: S341. Analyze whether the following conditions are met simultaneously: the actual tidal volume is within the preset tidal volume range, the actual inhalation duration is within the preset inhalation duration range, and the actual inhalation flow rate smoothness is greater than or equal to the second preset flow rate smoothness threshold. If yes, set the color of the leaves to the preset emerald green and the shape of the leaves to the preset extended shape. If no, proceed to step S342. S342. Based on the deviation between the actual tidal volume and the preset tidal volume range, the deviation between the actual inhalation duration and the preset inhalation duration range, and / or the deviation between the actual inhalation flow rate smoothness and the second preset flow rate smoothness threshold, determine the yellowing color adjustment amount and the curling color adjustment amount; the greater the deviation between the actual tidal volume and the preset tidal volume range, the greater the deviation between the actual inhalation duration and the preset inhalation duration range, and the greater the deviation between the actual inhalation flow rate smoothness and the second preset flow rate smoothness threshold, the greater the yellowing color adjustment amount and the curling color adjustment amount. S343. Adjust the color of the leaves according to the yellowing adjustment amount so that the color of the leaves changes from the preset bright green to yellowing, and adjust the shape of the leaves according to the curling adjustment amount so that the shape of the leaves changes from the preset open shape to the curled shape. S344. If the current growth stage is the flowering and fruiting period, then proceed to step S345; otherwise, proceed to step S4. S345. Analyze whether the number of continuous breaths that simultaneously satisfy the following conditions have reached the preset number: the actual tidal volume is within the preset tidal volume range, the actual inspiratory duration is within the preset inspiratory duration range, and the actual inspiratory flow rate smoothness is greater than or equal to the second preset flow rate smoothness threshold. If yes, proceed to step S346; otherwise, proceed to step S4. S346. Transform a flower into a fruit or change the color of a fruit from a preset immature color to a preset ripe color, and execute step S4; the color of the fruit obtained by transforming the flower is the preset immature color.

[0054] Step S341 aims to determine whether the critically ill patient's current respiratory performance meets preset standards. The second preset flow smoothness threshold is used to define the stability of inspiratory flow rate. If all multidimensional respiratory physiological parameters meet the preset standards, it indicates that the patient's respiratory quality is good, and the system sets the color of the leaves to a preset emerald green and their shape to a preset extended shape to provide positive visual feedback. Otherwise, the system will execute the subsequent step S342 for further adjustments.

[0055] Step S342 is used to quantify the deviation between the patient's respiratory parameters and ideal values. The deviation between the actual tidal volume and the preset tidal volume range can be calculated as the distance between the actual tidal volume and the range boundary. For example, if the actual tidal volume is lower than the lower limit, the deviation is the lower limit minus the actual tidal volume; if it is higher than the upper limit, the deviation is the actual tidal volume minus the upper limit. The deviation between the actual inspiratory duration and the preset inspiratory duration range, and the deviation between the actual inspiratory flow rate smoothness and the second preset flow rate smoothness threshold are also calculated in a similar manner. The yellowing and curling adjustment amounts are parameters determined based on these deviation values ​​and are used to subsequently adjust the visual effect of the leaves. For example, a preset mapping table of combinations of tidal volume deviation, inhalation duration deviation, and smoothness deviation, along with their corresponding yellowing and curling adjustment amounts, can be used to map the deviation of the preset tidal volume range, the deviation of the actual inhalation duration from the preset inhalation duration range, and the deviation of the actual inhalation flow rate smoothness from the second preset flow rate smoothness threshold into the corresponding yellowing and curling adjustment amounts. Specifically, the larger the deviation, the larger the adjustment amount, to ensure that the intensity of the visual feedback is proportional to the degree of deviation in breathing quality.

[0056] Step S343 transforms the quantified breathing deviation into an intuitive visual change. The leaf color adjustment can be achieved by altering its RGB red, green, and blue color components. For example, the yellowing adjustment consists of a positive red adjustment, a positive blue adjustment, and a negative green adjustment. The red component of the leaf color is adjusted based on the red adjustment, the blue component based on the blue adjustment, and the green component based on the green adjustment, causing the leaf to change from bright green to yellowish-brown. Specifically, the greater the deviation, the larger the red and blue adjustment, and the smaller the green adjustment; that is, the greater the deviation, the more yellowish the leaf. Leaf shape adjustment can be achieved by using existing techniques such as vertex displacement and normal adjustment to adjust the leaves based on the curling adjustment, gradually changing them from an extended state to a curled state, or by replacing the leaf model with one of different degrees of curl (the degree of curl in this model corresponds to the curling adjustment).

[0057] Step S344 aims to determine whether to trigger a higher-level reward mechanism based on the growth stage of the virtual entity. If the current growth stage is the flowering and fruiting period, the system will execute step S345 to evaluate whether further reward conditions are met. If it is not the flowering and fruiting period, the system will skip the subsequent reward evaluation and directly execute step S4, which is to adjust the environmental background visual effects.

[0058] Step S345 is used to assess whether critically ill patients can consistently maintain high-quality respiratory performance. Sustained respiratory rate refers to the number of consecutive breaths that meet the ideal standard. The preset number is a threshold set by the system; for example, it can be set to 5 or 10 consecutive high-quality breaths. Only when the patient's respiratory quality not only meets the standard on a single breath but also consistently meets the preset number of breaths will it be considered to have met the criteria for a higher level of reward. If the preset number of breaths is reached, the system will execute step S346; if the preset number of breaths is not reached, the system will execute step S4.

[0059] Step S346 is the highest level of positive reinforcement for critically ill patients in the late stages of rehabilitation. When the patient achieves a preset number of sustained high-quality breaths, the system triggers changes in visual elements on the virtual entity. For example, a flower can gradually expand and transform into a fruit through a preset animation, or when a fruit exists on the virtual entity, its color can gradually change from a preset immature color (such as cyan) to a preset mature color (such as red or yellow) to symbolize the abundant recovery results. The color of the fruit transformed from the flower is the preset immature color, which provides further visual progression space for the subsequent ripening of the fruit. After completing this step, the system will execute step S4.

[0060] This embodiment provides more dynamic, accurate, and motivating visual feedback by finely adjusting the local morphological details of virtual entities. The method first acquires multi-dimensional respiratory physiological parameters of critically ill patients, including actual tidal volume, actual inspiratory duration, and actual inspiratory flow rate smoothness. These parameters are key indicators for assessing respiratory quality. In step S341, the system comprehensively judges these parameters, analyzing whether they simultaneously meet preset ideal ranges or thresholds. If the patient's respiratory performance meets these standards, indicating good respiratory quality, the system sets the color of the leaves on the virtual entity (e.g., a tree) in the interactive display interface to a preset emerald green and adjusts their shape to a preset extended form. This immediate and positive visual feedback intuitively informs the patient that their breathing behavior is correct, thereby reinforcing their good breathing pattern. Conversely, if the patient's respiratory parameters fail to simultaneously meet the preset conditions, the system proceeds to step S342, calculating the deviation between the actual respiratory parameters and the preset ideal values. These deviation values ​​are used to determine the adjustment amount for yellowing and curling, where the larger the deviation, the larger the adjustment amount, ensuring that the intensity of the visual feedback is proportional to the degree of decline in respiratory quality. Subsequently, in step S343, the system dynamically adjusts the color of the leaves according to these adjustment values, gradually changing them from a preset bright green to a withered yellow, while simultaneously adjusting the shape of the leaves, gradually changing them from a preset open shape to a curled shape. This gradual negative visual feedback can accurately reflect the degree of decline in the patient's breathing quality, alerting the patient to problems with their breathing pattern and prompting them to actively adjust their breathing behavior to avoid the visual effect of withering. Furthermore, to provide a higher level of incentive, the method determines the current growth stage of the virtual entity in step S344. If the virtual entity has entered the flowering and fruiting stage, the system will further evaluate the patient's continued performance. In step S345, the system analyzes whether the patient can continuously perform high-quality breathing, i.e., continuously meeting preset tidal volume, inspiratory duration, and inspiratory flow smoothness standards multiple times, and reaching a preset number of sustained breaths. This consideration of sustained good breathing avoids the one-sidedness of rewarding based solely on a single good breath, ensuring the rigor and effectiveness of the reward mechanism. If the patient successfully reaches the preset number of sustained breaths, the system will trigger a more symbolic positive visual change in step S346. For example, it may transform a flower on a virtual entity into a fruit, or change the color of a fruit from a preset unripe color to a preset ripe color. This visual reward of the result and the ripening fruit not only provides the patient with strong positive motivation, symbolizing the fruitful results of their recovery, but also further enhances the patient's sense of accomplishment and training compliance. If the number of sustained breaths is not reached, this reward will not be triggered, maintaining the rigor of the feedback. Through the above mechanism, this method transforms the real-time changes of multi-dimensional respiratory physiological parameters and the patient's continuous performance into dynamic and refined adjustments to the local morphological details of virtual entities (such as the color and shape of leaves, and the evolution of flowers and fruits).This, combined with the aforementioned scheme that adjusts the overall visual model and local morphological details of the virtual entity based on the cumulative value of respiratory contribution, and defines visual elements at different growth stages, makes visual feedback no longer singular and linear, but multi-dimensional, dynamic, and vibrant. This refined feedback mechanism can more accurately reflect the patient's respiratory quality and provide differentiated incentives based on their recovery stage and ongoing performance, thus effectively solving the problems of inaccurate feedback and insufficient incentives in traditional methods, and significantly improving the compliance and actual effectiveness of respiratory training for critically ill patients.

[0061] The following is a concrete example. Assume a critically ill patient is undergoing respiratory training, and the interactive display shows a virtual Tree of Life. Currently, this Tree of Life is in its flowering and fruiting stage, with multiple flowers and unripe fruits. The system's preset ideal tidal volume range is 500 ml to 700 ml, the ideal inspiratory duration range is 2 seconds to 3 seconds, and the second preset flow smoothness threshold is 0.8. Simultaneously, the system is set to trigger the transformation of flowers into fruits after 5 consecutive high-quality breaths. During a respiratory training session, the system acquires the patient's actual tidal volume, actual inspiratory duration, and actual inspiratory flow smoothness in real time. Scenario 1: The patient performs a high-quality breath. The system detects an actual tidal volume of 620 ml (within the 500-700 ml range), an actual inspiratory duration of 2.5 seconds (within the 2-3 second range), and an actual inspiratory flow smoothness of 0.9 (greater than or equal to 0.8). Since all conditions are met, the system sets the leaf color of the Tree of Life to a preset emerald green and its shape to a preset extended shape. At this point, the system records a high-quality breath. Scenario 2: In subsequent breaths, the patient's breathing quality declines due to fatigue. The system detects an actual tidal volume of 450 ml (below the preset range), an actual inspiratory duration of 1.5 seconds (below the preset range), and an actual inspiratory flow smoothness of 0.6 (below the second preset flow smoothness threshold). Based on these deviations, the system calculates a yellowish-brown adjustment amount and a curling adjustment amount. For example, the yellowish-brown adjustment amount corresponding to the tidal volume deviation, inspiratory duration deviation, and flow smoothness deviation is 20%. The system adjusts the color of the leaves on the Tree of Life from bright green to a yellowish-brown corresponding to the yellowish-brown adjustment amount, and changes their shape from an extended state to a curled state corresponding to the curling adjustment amount, to visually reflect the decline in breathing quality. Scenario 3: After adjustments, the patient takes 5 consecutive high-quality breaths (the actual tidal volume, actual inspiratory duration, and smoothness of the inspiratory flow rate all meet the preset conditions). Since the current growth stage is flowering and fruiting, and the number of consecutive high-quality breaths has reached the preset 5, the system will trigger a reward mechanism. For example, a flower in the interactive display interface will gradually swell and transform into a fruit of a preset unripe color, or an existing unripe fruit will gradually turn into a preset ripe color. After this visual change is completed, the system will continue to execute step S4, adjusting the visual effects of the environmental background.

[0062] In some preferred embodiments, the current vital signs parameter includes the current blood oxygen saturation, and step S4 includes: S41. If the current blood oxygen saturation is greater than or equal to the first preset safety threshold, then set the environmental background visual effect in the interactive display page to sunny. S42. If the current blood oxygen saturation is less than the first preset safety threshold and greater than or equal to the second preset safety threshold, then set the environmental background visual effect in the interactive display page to cloudy. S43. If the current blood oxygen saturation is less than the second preset safety threshold, the environmental background visual effect in the interactive display page will be set to a thunderstorm, and an alarm message will be generated.

[0063] Current blood oxygen saturation refers to the percentage of oxyhemoglobin in a patient's blood, a key physiological indicator for measuring the patient's oxygenation status. It can be obtained through non-invasive monitoring using a finger-clip pulse oximeter or invasive measurement using an arterial blood gas analyzer. The first and second preset safety thresholds are preset values ​​used to assess the safety level of a patient's blood oxygen saturation. For example, the first preset safety threshold can be set to 95%, indicating a safe state of blood oxygen saturation above this level, while the second preset safety threshold can be set to 90%, indicating a dangerous state of blood oxygen saturation below this level. These thresholds can be personalized by medical staff based on the patient's specific condition, age, underlying diseases, etc., or preset according to clinical guidelines or standards. These threshold settings are existing technology. Setting the environmental background visual effect of the interactive display page to a sunny day means presenting a bright and sunny scene with a clear blue sky in the interactive display interface. This can be achieved using a pre-designed sunny day background image. Its purpose is to provide patients with positive and comfortable visual feedback, suggesting that their physiological state is good and encouraging them to continue training. Setting the environmental background visual effect of the interactive display page to cloudy means presenting a visual scene with a dark sky and weak light in the interactive display interface. This can be achieved by loading a pre-designed cloudy background image. Its function is to serve as a mild warning, prompting medical staff to pay attention to the patient's oxygenation status, but not to the point of emergency intervention. Setting the environmental background visual effect of the interactive display page to thunderstorm means presenting a visual scene with dark clouds, lightning, thunder, and heavy rain in the interactive display interface. This can be achieved by loading a pre-designed thunderstorm background image. Its function is to issue an emergency safety warning, indicating that the patient's blood oxygen saturation has reached a dangerous level and that immediate intervention is required. The alarm information in this embodiment may include displaying text prompts on the screen (e.g., "Blood oxygen is too low, please pay attention immediately!"), playing alarm sound effects, sending notifications to medical staff's mobile devices, or triggering a linkage alarm with the central monitoring system.

[0064] This embodiment achieves intuitive and tiered feedback on the patient's safety status by specifically defining the current vital signs parameter as the current blood oxygen saturation and dynamically adjusting the environmental background visual effects on the interactive display page based on the comparison result with preset safety thresholds. Specifically, when the system obtains the current blood oxygen saturation of a critically ill patient, it compares it with a first preset safety threshold and a second preset safety threshold. If the current blood oxygen saturation is higher than or equal to the first preset safety threshold, it indicates that the patient's physiological state is safe and stable, and the system will set the environmental background visual effect to sunny, providing positive visual feedback. If the current blood oxygen saturation decreases but remains between the first and second preset safety thresholds, the system will set the environmental background visual effect to cloudy, as a mild warning, prompting medical staff to pay attention to the patient's oxygenation status. If the current blood oxygen saturation further decreases and falls below the second preset safety threshold, it indicates that the patient is in a dangerous state, and the system will quickly switch the environmental background visual effect to thunderstorm and generate an alarm message to force medical staff to take emergency intervention. This mechanism, combined with the overall framework of adjusting the environmental background visual effects based on the current vital signs parameters in step S4, allows the patient's safety status to be presented in real time through intuitive changes in the environmental background during breathing training. This eliminates the need for medical staff to frequently check independent monitoring equipment, thereby organically integrating key safety indicators with training feedback and improving the efficiency and accuracy of information acquisition.

[0065] In some preferred embodiments, step S1 includes: S11. Obtain multi-dimensional respiratory physiological parameters of critically ill patients and obtain their current vital signs parameters. S12. Perform low-pass filtering on multi-dimensional respiratory physiological parameters and smoothing filtering on current vital sign parameters.

[0066] Low-pass filtering of multidimensional respiratory physiological parameters refers to using signal processing techniques to filter out high-frequency noise components in respiratory physiological parameters, retaining low-frequency effective signals that reflect the true physiological trend. This can be achieved through digital filters (such as Butterworth filters, Chebyshev filters, etc.) or analog filters, effectively removing high-frequency interference by setting an appropriate cutoff frequency. Smoothing filtering of current vital signs parameters refers to using signal processing techniques to reduce short-term fluctuations and instantaneous outliers in vital signs parameters, making the data trend more stable and reliable. This can be achieved through moving average filters (calculating the average value of data within a certain time window) or median filters (eliminating spike noise). It should be understood that the low-pass filtering and smoothing filtering processes in this embodiment are existing technologies, and their working principles and processes will not be discussed in detail here.

[0067] This embodiment ensures the accuracy and reliability of subsequent data processing by introducing filtering in the initial stage of acquiring physiological parameters. Specifically, in step S11, the system first acquires the multi-dimensional respiratory physiological parameters and current vital sign parameters of the critically ill patient. These raw data are the basic inputs for the entire visual effect generation method. However, considering that these raw data may be affected by various noises and instantaneous fluctuations, direct use may lead to deviations in subsequent calculations and visual feedback. Therefore, in step S12, the acquired multi-dimensional respiratory physiological parameters are subjected to low-pass filtering, which can effectively filter out high-frequency interference such as sensor noise and muscle tremors, making the respiratory waveform purer and thus more accurately reflecting the patient's true breathing pattern. At the same time, the current vital sign parameters are subjected to smoothing filtering, which can reduce instantaneous jumps caused by slight patient movements or poor equipment contact, ensuring the stability of vital sign data. In this way, the filtered physiological parameter data is more reliable and consistent, laying a solid data foundation for the accurate calculation of respiratory contribution values ​​in subsequent step S2 and the accurate adjustment of environmental background visual effects based on safety thresholds in step S4. This data preprocessing mechanism enables the entire physiological parameter-based human-computer interactive visual effect generation method to provide more realistic and safer training feedback, thereby significantly improving the accuracy of human-computer interaction and the safety of training.

[0068] Secondly, such as Figure 2 As shown, this application also provides a human-computer interactive visual effects generation system based on physiological parameters, which includes: Parameter acquisition module 1 is used to acquire multi-dimensional respiratory physiological parameters of critically ill patients and acquire their current vital signs parameters. Contribution acquisition module 2 is used to acquire the respiratory contribution value of the current breathing of critically ill patients based on multi-dimensional respiratory physiological parameters; Entity adjustment module 3 is used to adjust the overall visual model and local morphological details of virtual entities in the interactive display interface based on the cumulative value of respiratory contribution and multi-dimensional respiratory physiological parameters. Background adjustment module 4 is used to adjust the environmental background visual effects in the interactive display screen based on the comparison results between the current vital signs parameters and the first preset safety threshold and the second preset safety threshold.

[0069] This application provides a human-computer interactive visual effects generation system based on physiological parameters, comprising a parameter acquisition module 1, a contribution acquisition module 2, an entity adjustment module 3, and a background adjustment module 4. This embodiment of the human-computer interactive visual effects generation system based on physiological parameters is preferably used to execute the steps in the human-computer interactive visual effects generation method based on physiological parameters provided in the first aspect above. The principle of this embodiment of the human-computer interactive visual effects generation system based on physiological parameters is the same as the principle of the human-computer interactive visual effects generation method based on physiological parameters provided in the first aspect above, and will not be repeated here.

[0070] As can be seen from the above, the human-computer interactive visual effect generation method and system based on physiological parameters provided in this application combine the multi-dimensional respiratory physiological parameters and vital sign parameters of critically ill patients with interactive visual effects. By dynamically adjusting the visual model of virtual entities and the environmental background, it provides critically ill patients with richer and more attractive visual feedback, thereby effectively improving patients' training enthusiasm and compliance, and enabling medical staff to more comprehensively assess patients' respiratory quality and overall physiological state. This solves the problems of single visual feedback and incomplete assessment in the prior art.

[0071] In the embodiments provided in this application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of the above units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another robot, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0072] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0073] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0074] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for generating human-computer interactive visual effects based on physiological parameters, characterized in that, The method for generating human-computer interactive visual effects based on physiological parameters includes the following steps: S1. Obtain multi-dimensional respiratory physiological parameters of the critically ill patient's current breathing, and obtain the current vital signs parameters of the critically ill patient; S2. Obtain the respiratory contribution value of the critically ill patient's current breathing based on the multi-dimensional respiratory physiological parameters; S3. Adjust the overall visual model and local morphological details of the virtual entities in the interactive display interface according to the cumulative value of the respiratory contribution value and the multi-dimensional respiratory physiological parameters. S4. Adjust the environmental background visual effects in the interactive display screen based on the comparison results between the current vital signs parameters and the first preset safety threshold and the second preset safety threshold.

2. The method for generating human-computer interactive visual effects based on physiological parameters according to claim 1, characterized in that, The multidimensional respiratory physiological parameters include actual tidal volume, actual inspiratory duration, and actual inspiratory flow rate smoothness.

3. The method for generating human-computer interactive visual effects based on physiological parameters according to claim 2, characterized in that, Step S2 includes: S21. Analyze whether the actual tidal volume is within a preset tidal volume range and the actual inhalation duration is within a preset inhalation duration range at the same time. If yes, then the preset contribution values ​​corresponding to the preset tidal volume range and the preset inhalation duration range are used as the basic contribution values. If no, then the basic contribution value is set to 0. S22. Obtain the actual respiratory rate of the critically ill patient within the first preset time window; S23. Analyze whether the actual intake flow velocity smoothness is greater than or equal to the first preset flow velocity smoothness threshold. If so, extract the corresponding weighting coefficient from the pre-constructed mapping table of flow velocity smoothness and weighting coefficients based on the actual intake flow velocity smoothness as the target weighting coefficient, and use the product of the basic contribution value and the target weighting coefficient as the preliminary contribution value. If not, use the basic contribution value as the preliminary contribution value. The target weighting coefficient is greater than 1. The greater the actual intake flow velocity smoothness, the greater the target weighting coefficient. S24. Analyze whether the actual respiratory rate is within a preset respiratory rate range. If so, the sum of the preset contribution correction amount corresponding to the preset respiratory rate range and the preliminary contribution value is taken as the respiratory contribution value of the critically ill patient's current breathing. If not, based on the deviation between the actual respiratory rate and the preset respiratory rate range, the corresponding contribution correction amount is extracted from a pre-constructed mapping table of respiratory rate deviation and contribution correction amount as the target contribution correction amount. Then, the sum of the target contribution correction amount and the preliminary contribution value is taken as the respiratory contribution value of the critically ill patient's current breathing. The target contribution correction amount is less than 0. The greater the deviation between the actual respiratory rate and the preset respiratory rate range, the smaller the target contribution correction amount.

4. The method for generating human-computer interactive visual effects based on physiological parameters according to claim 3, characterized in that, Step S2 also includes steps performed before step S21: A1. The rehabilitation stage corresponding to the preset mapping interval to which the accumulated respiratory contribution value belongs is taken as the current rehabilitation stage; A2. Based on the current rehabilitation stage, extract the corresponding preset parameter combination from the pre-constructed mapping table of rehabilitation stage and preset parameter combination; the preset parameter combination includes preset tidal volume range, preset inspiratory duration range, preset contribution value, first preset flow smoothness threshold, preset respiratory rate range and preset contribution correction amount. A3. Obtain the tidal volume change trend and inspiratory flow rate smoothness change trend of the critically ill patient within the second preset time window; A4. Calculate the standard deviation of blood oxygen saturation of the critically ill patient within the second preset time window, and calculate the respiratory rate deviation based on the respiratory rate of the critically ill patient within the second preset time window and the historical respiratory rate. A5. If the tidal volume change trend indicates an increase in actual tidal volume, the inspiratory flow rate smoothness change trend indicates an increase in actual inspiratory flow rate smoothness, the blood oxygen saturation standard deviation is less than or equal to a first preset threshold, and the respiratory rate deviation is less than or equal to a second preset threshold, then increase the upper and lower limits of the preset tidal volume range, the preset inspiratory duration range, and the preset respiratory rate, and increase the first preset flow rate smoothness threshold. A6. If the tidal volume change trend indicates a decrease in actual tidal volume, the inspiratory flow rate smoothness change trend indicates a decrease in actual inspiratory flow rate smoothness, the blood oxygen saturation standard deviation is greater than the first preset threshold, or the respiratory rate deviation is greater than the second preset threshold, then reduce the upper and lower limits of the preset tidal volume range, the preset inspiratory duration range, and the preset respiratory rate, and reduce the first preset flow rate smoothness threshold.

5. The method for generating human-computer interactive visual effects based on physiological parameters according to claim 1, characterized in that, Step S3 includes: S31. The growth stage corresponding to the preset mapping interval to which the cumulative value of the respiratory contribution value belongs is taken as the current growth stage; S32. Based on the current growth stage, extract the corresponding overall visual model from the pre-built mapping relationship table between growth stage and overall visual model as the target visual model, and then adjust the overall visual model of the virtual entity in the interactive display interface to the target visual model; the target visual model has several kinds of visual elements. S33. Adjust the size of the overall visual model of the virtual entity in the interactive display interface according to the cumulative value of the breathing contribution value; the larger the cumulative value of the breathing contribution value, the larger the overall visual model of the virtual entity in the interactive display interface. S34. Adjust the visual effects and / or colors of the visual elements according to the multi-dimensional respiratory physiological parameters to adjust the local morphological details of the virtual entities in the interactive display interface.

6. The method for generating human-computer interactive visual effects based on physiological parameters according to claim 5, characterized in that, The growth stages include the seedling stage, sapling stage, mature tree stage, and flowering and fruiting stage. During the seedling stage, the target visual model consists of a trunk and several leaves. During the sapling stage, the target visual model consists of a trunk, several first branches, and multiple leaves, with each first branch having multiple leaves. The diameter of the trunk in the sapling stage is larger than that in the seedling stage. During the mature tree stage, the target visual model consists of a trunk, multiple second branches, multiple leaves, and multiple flower buds. The number of second branches is greater than the number of first branches, and each second branch has multiple leaves and... The tree has several flower buds. The diameter of the trunk during the mature tree stage is larger than that during the sapling stage. The diameter of all second branches is larger than that of all first branches. When the growth stage is the flowering and fruiting stage, the target visual model consists of a trunk, multiple second branches, multiple leaves, several flowers, and several fruits. The diameter of the trunk during the flowering and fruiting stage is greater than or equal to the diameter of the trunk during the mature tree stage. The diameter of the second branches during the flowering and fruiting stage is greater than or equal to the diameter of the second branches during the mature tree stage. Each flower bud corresponds to one flower or one fruit. The leaves, flower buds, flowers, and fruits are all visual elements.

7. The method for generating human-computer interactive visual effects based on physiological parameters according to claim 6, characterized in that, The multidimensional respiratory physiological parameters include actual tidal volume, actual inspiratory duration, and actual inspiratory flow rate smoothness. Step S34 includes: S341. Analyze whether the following conditions are met simultaneously: the actual tidal volume is within a preset tidal volume range, the actual inhalation duration is within a preset inhalation duration range, and the actual inhalation flow rate smoothness is greater than or equal to a second preset flow rate smoothness threshold. If yes, set the color of the leaves to a preset emerald green and the shape of the leaves to a preset extended shape. If no, proceed to step S342. S342. Based on the deviation between the actual tidal volume and the preset tidal volume range, the deviation between the actual inhalation duration and the preset inhalation duration range, and / or the deviation between the actual inhalation flow rate smoothness and the second preset flow rate smoothness threshold, determine the yellowing color adjustment amount and the curling color adjustment amount; the greater the deviation between the actual tidal volume and the preset tidal volume range, the greater the deviation between the actual inhalation duration and the preset inhalation duration range, and the greater the deviation between the actual inhalation flow rate smoothness and the second preset flow rate smoothness threshold, the greater the yellowing color adjustment amount and the curling color adjustment amount. S343. Adjust the color of the leaves according to the yellowing adjustment amount so that the color of the leaves changes from a preset bright green to yellowing, and adjust the shape of the leaves according to the curling adjustment amount so that the shape of the leaves changes from a preset extended shape to a curled shape. S344. If the current growth stage is the flowering and fruiting period, if yes, proceed to step S345; otherwise, proceed to step S4. S345. Analyze whether the number of continuous breaths that simultaneously satisfy the following conditions have reached a preset number: the actual tidal volume is within a preset tidal volume range, the actual inspiratory duration is within a preset inspiratory duration range, and the actual inspiratory flow rate smoothness is greater than or equal to a second preset flow rate smoothness threshold. If yes, proceed to step S346; otherwise, proceed to step S4. S346. Transform one of the flowers into one of the fruits or change the color of one of the fruits from a preset unripe color to a preset ripe color, and execute step S4; the color of the fruit obtained by transforming the flower is the preset unripe color.

8. The method for generating human-computer interactive visual effects based on physiological parameters according to claim 1, characterized in that, The current vital signs parameters include the current blood oxygen saturation. Step S4 includes: S41. If the current blood oxygen saturation is greater than or equal to the first preset safety threshold, then set the environmental background visual effect in the interactive display page to sunny. S42. If the current blood oxygen saturation is less than the first preset safety threshold and greater than or equal to the second preset safety threshold, then the environmental background visual effect in the interactive display page is set to cloudy. S43. If the current blood oxygen saturation is less than the second preset safety threshold, the environmental background visual effect in the interactive display page is set to a thunderstorm, and an alarm message is generated.

9. The method for generating human-computer interactive visual effects based on physiological parameters according to claim 1, characterized in that, Step S1 includes: S11. Obtain multi-dimensional respiratory physiological parameters of the critically ill patient's current breathing, and obtain the current vital signs parameters of the critically ill patient; S12. Perform low-pass filtering on the multi-dimensional respiratory physiological parameters and smoothing filtering on the current vital signs parameters.

10. A human-computer interactive visual effects generation system based on physiological parameters, characterized in that, The human-computer interactive visual effects generation system based on physiological parameters includes: The parameter acquisition module is used to acquire multi-dimensional respiratory physiological parameters of the critically ill patient's current breathing and to acquire the critically ill patient's current vital sign parameters. The contribution acquisition module is used to acquire the respiratory contribution value of the critically ill patient's current breathing based on the multi-dimensional respiratory physiological parameters. The entity adjustment module is used to adjust the overall visual model and local morphological details of the virtual entities in the interactive display interface according to the cumulative value of the respiratory contribution value and the multi-dimensional respiratory physiological parameters. The background adjustment module is used to adjust the environmental background visual effects in the interactive display screen based on the comparison results between the current vital sign parameters and the first preset safety threshold and the second preset safety threshold.

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