Personalized debugging method and system for intelligent breathing training device

Abstract

According to the personalized debugging method and system of the intelligent breathing training device, the breathing function evaluation result of the patient, the breathing training control information and the breathing training similarity are obtained according to at least two of the breathing function evaluation result, the breathing training control information and the breathing training similarity; determining a breathing training personalized debugging result of immersive training information provided by the to-be-processed patient in the first virtual reality lung function data, the real-time monitoring element information is used as the occurrence number of the lung function indexes; according to the method, at least three of four different forms of respiratory training information of the number of breathing times of the patient state of each piece of real-time monitoring element information and different influence element information of immersive training information provided by a patient to be processed in virtual reality lung function data are used; and whether the immersive training information provided by the to-be-processed patient is reasonable or not is judged, so that the debugging accuracy and reliability of the respiratory training device can be improved.

Description

Technical Field

[0001] The present application relates to the field of data debugging technology, and in particular to a personalized debugging method and system for an intelligent breathing trainer. Background Art

[0002] During normal inhalation, the diaphragm and external intercostal muscles contract. However, when inhaling forcefully, the accessory inspiratory muscles, such as the trapezius and scalenes, are also required. These muscle contractions lift the chest, expanding the chest cavity to its maximum capacity, necessitating inspiratory muscle training. Most respiratory trainers on the market use the principle of resistance training. When inhaling through a respiratory trainer, the user exerts force against the set resistance to increase inspiratory muscle strength, thereby increasing respiratory muscle strength and tolerance.

[0003] At present, due to the differences in physical fitness and medical conditions of each person, the methods and details of breathing training are also different. Therefore, different breathing trainers need to be debugged differently for different patients so that targeted rehabilitation training can be achieved for patients. However, at present, there is no device that can provide personalized breathing training services according to the patient. Therefore, a technology is urgently needed to overcome the above technical problems. Summary of the Invention

[0004] In order to improve the technical problems existing in the related technologies, the present application provides a personalized debugging method and system for an intelligent breathing trainer.

[0005] In a first aspect, a personalized debugging method for an intelligent breathing trainer is provided, comprising: Based on the respiratory training scene description information of the patient to be processed that provides immersive training information parsed from the first virtual reality lung function data, obtaining the patient's respiratory state information in the area represented by the respiratory training scene description information; Determining, in combination with the patient's respiratory status information, a patient respiratory function assessment result of the patient to be processed providing immersive training information in the patient's respiratory status information; Determining the respiratory training control information corresponding to the immersive training information provided by the patient to be processed by combining the number of occurrences of each real-time monitoring element information as a lung function indicator in the area represented by the respiratory training scene description information and the number of patient state respirations through each real-time monitoring element information; Determining a respiratory training similarity corresponding to the immersive training information provided by the patient to be processed, combining respiratory state influencing factor information of the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data and influencing factor information of the area represented by the respiratory training scene description information in the second virtual reality pulmonary function data; Combining at least two of the patient's respiratory function assessment result, the respiratory training control information, and the respiratory training similarity, a respiratory training personalized debugging result of the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data is determined.

[0006] In the present application, the method of determining the respiratory training control information corresponding to the immersive training information provided by the patient to be processed by combining the number of occurrences of each real-time monitoring element information in the area represented by the respiratory training scene description information as a lung function indicator and the number of patient state respirations through each real-time monitoring element information includes: Determining a lung respiratory status assessment level for each of the real-time monitoring element information, based on the number of occurrences of each of the real-time monitoring element information as a lung function indicator in the area represented by the breathing training scenario description information and the number of patient state respirations through each of the real-time monitoring element information; Based on the pulmonary respiratory state assessment level of each of the real-time monitoring element information, generating a pulmonary respiratory state assessment level set corresponding to the immersive training information provided by the patient to be treated, the pulmonary respiratory state assessment level set including a target respiratory feature representing each of the real-time monitoring element information in the area represented by the respiratory training scene description information; the respiratory feature description element of the target respiratory feature is determined in combination with the pulmonary respiratory state assessment level of the real-time monitoring element information corresponding to the target respiratory feature; In combination with the lung respiratory state assessment level set corresponding to the immersive training information provided by the patient to be processed, the respiratory training control information corresponding to the immersive training information provided by the patient to be processed is determined.

[0007] In the present application, the step of determining the respiratory training control information corresponding to the immersive training information provided by the patient to be processed in combination with the lung respiratory state assessment level set corresponding to the immersive training information provided by the patient to be processed includes: extracting one or more first reference breathing features from the target breathing features of the lung breathing state assessment level set; determining a target respiratory feature region for each of the first reference respiratory features in the lung respiratory state assessment level set; determining a reference breathing feature description element for each first reference breathing feature according to the breathing feature description element of each breathing feature in the target breathing feature area of ​​each first reference breathing feature; The respiratory training control information corresponding to the immersive training information provided by the patient to be processed is determined by combining the reference respiratory feature description elements of each of the first reference respiratory features in the pulmonary respiratory state assessment level set.

[0008] In the present application, the lung respiratory state assessment level based on each of the real-time monitoring element information is used to generate a lung respiratory state assessment level set corresponding to the immersive training information provided by the patient to be treated, including: Loading the lung respiratory state assessment level of each of the real-time monitoring element information into the respiratory training debugging network to obtain the target lung respiratory state assessment level corresponding to each of the real-time monitoring element information; The target lung respiratory state assessment level corresponding to each of the real-time monitoring element information is used as the respiratory feature description element of the target respiratory feature corresponding to each of the real-time monitoring element information, and a lung respiratory state assessment level set corresponding to the immersive training information provided by the patient to be treated is generated. In the lung respiratory state assessment level set, each of the target respiratory features is distributed according to the relative position of the corresponding real-time monitoring element information in the area represented by the respiratory training scene description information.

[0009] In the present application, the lung respiratory state assessment level of each of the real-time monitoring element information is loaded into the respiratory training debugging network to obtain the target lung respiratory state assessment level corresponding to each of the real-time monitoring element information, including: performing difference quantification processing on the pulmonary respiratory state assessment level of each of the real-time monitoring element information to obtain a first transitional pulmonary respiratory state assessment level of each of the real-time monitoring element information; the difference quantification processing is used to quantify the difference between the pulmonary respiratory state assessment levels corresponding to different real-time monitoring element information; Loading the first transitional lung respiratory state assessment level of each of the real-time monitoring element information into the respiratory training debugging network to obtain the second transitional lung respiratory state assessment level of each of the real-time monitoring element information; The first transition pulmonary respiratory state assessment level of each of the real-time monitoring element information is rounded to obtain the target pulmonary respiratory state assessment level corresponding to each of the real-time monitoring element information.

[0010] In the present application, the step of determining the patient respiratory function assessment result of the patient to be processed in the patient respiratory state information by combining the patient respiratory state information with the patient's respiratory state information includes: performing clustering processing on the patient's respiratory state information to obtain a clustering result; wherein a respiratory feature description element representing a respiratory feature of respiratory training is different from a respiratory feature description element representing a respiratory feature of non-respiratory training in the clustering result; Extracting one or more second reference breathing features from a target breathing feature region in the clustering result; the target breathing feature region is a breathing feature region of the region represented by the breathing training scene description information in the clustering result; determining a target breathing feature region for each second reference breathing feature in the clustering result; Combined with the respiratory feature description elements of each respiratory feature in the target respiratory feature area of ​​each second reference respiratory feature in the clustering result, the patient respiratory function assessment result of the patient to be processed providing immersive training information in the patient respiratory state information is determined.

[0011] In the present application, the respiratory feature description element used to represent the respiratory feature of the respiratory training in the clustering result is the target respiratory feature description element; the determination of the patient respiratory function assessment result of the patient providing immersive training information in the patient respiratory state information by combining the respiratory feature description elements of each respiratory feature in the target respiratory feature area of ​​each second reference respiratory feature in the clustering result includes: Determining a breathing training description element corresponding to each second reference breathing feature, based on a weight of the breathing feature description element in the target breathing feature region of each second reference breathing feature in the clustering result, where the breathing training description element is a breathing training description segment matching element or a breathing training description segment non-matching element; The patient respiratory function assessment result of the immersive training information provided by the patient to be processed in the patient respiratory state information is determined based on the number of second reference respiratory features of the matching elements of the respiratory training description segment and the number of second reference respiratory features of the non-matching elements of the respiratory training description segment.

[0012] In the present application, the respiratory state influencing factor information of the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data includes respiratory training scene description information of each of the multiple first motion state segments, and the influencing factor information of the area represented by the respiratory training scene description information in the second virtual reality pulmonary function data includes respiratory training scene description information of each of the multiple second motion state segments; combining the similarity between the respiratory state influencing factor information of the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data and the influencing factor information of the area represented by the respiratory training scene description information in the second virtual reality pulmonary function data to determine the respiratory training similarity corresponding to the immersive training information provided by the patient to be processed, includes: determining, from the plurality of second motion state segments, a matching motion state segment that matches each of the first motion state segments; Determine a relative coefficient between the first motion state segment and the corresponding matching motion state segment based on the breathing training scene description information of the first motion state segment and the breathing training scene description information of the corresponding matching motion state segment, wherein the relative coefficient includes a relative distance and a relative angle; determining a similarity between the respiratory state influencing factor information and the influencing factor information based on a relative coefficient between each of the first motion state segments and the corresponding matching motion state segment; In combination with the similarities, the breathing training similarity corresponding to the immersive training information provided by the patient to be processed is determined.

[0013] In the present application, the step of combining at least two of the patient's respiratory function assessment result, the respiratory training control information, and the respiratory training similarity to determine a personalized debugging result of the respiratory training provided by the patient in the first virtual reality pulmonary function data includes: If the patient's respiratory function assessment result is less than the patient's respiratory function assessment result threshold, the respiratory training control information is less than the abnormal respiratory training probability threshold, and the respiratory training similarity is less than the respiratory training similarity threshold, it is obtained that the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data is a personalized debugging result of respiratory training.

[0014] In the present application, the step of combining at least two of the patient's respiratory function assessment result, the respiratory training control information, and the respiratory training similarity to determine a personalized debugging result of the respiratory training provided by the patient in the first virtual reality pulmonary function data includes: Inputting the patient's respiratory function assessment results, respiratory training control information, and respiratory training similarity corresponding to the immersive training information provided by the patient to be processed into the respiratory training parsing network to obtain an abnormal probability result that the immersive training information provided by the patient to be processed is predicted to be respiratory training; If the abnormal probability result of the breathing training predicted by the immersive training information provided by the patient to be processed is less than the breathing training probability threshold, obtaining a personalized debugging result of the first virtual reality pulmonary function data of the immersive training information provided by the patient to be processed not being breathing training; If the abnormal probability result of the immersive training information provided by the patient to be processed being predicted as breathing training is not less than the breathing training probability threshold, the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data is obtained as a personalized debugging result of breathing training.

[0015] In the present application, after determining the personalized debugging result of the breathing training of the patient to be processed in the first virtual reality pulmonary function data by combining the patient's respiratory function assessment result, the breathing training control information, and the breathing training similarity, the method further includes: If the personalized debugging result of the breathing training is that immersive training information is provided for the patient to be processed and the first virtual reality pulmonary function data is breathing training, the immersive training information provided for the patient to be processed in the first virtual reality pulmonary function data is warned.

[0016] In a second aspect, a personalized debugging system for an intelligent breathing trainer is provided, comprising a processor and a memory communicating with each other, wherein the processor is configured to read and execute a computer program from the memory to implement the above method.

[0017] The embodiment of the present application provides a personalized debugging method and system for an intelligent breathing trainer, which determines the patient's respiratory function evaluation result of the immersive training information provided by the patient to be processed in the patient's respiratory state information according to the patient's respiratory state information; and determines the respiratory training control information corresponding to the immersive training information provided by the patient to be processed according to the number of occurrences of each real-time monitoring element information as a lung function indicator in the area represented by the respiratory training scene description information and the number of patient state respirations through each real-time monitoring element information; and determines the respiratory state influencing element information of the immersive training information provided by the patient to be processed in the first virtual reality lung function data and the influencing element information of the area represented by the respiratory training scene description information in the second virtual reality lung function data. Processing the breathing training similarity corresponding to the immersive training information provided by the patient; then, determining the personalized debugging result of the breathing training of the patient to be processed in the first virtual reality pulmonary function data based on at least two of the patient's respiratory function assessment result, the breathing training control information, and the breathing training similarity, thereby combining at least three of four different forms of breathing training information, namely, the number of occurrences of real-time monitoring element information as a pulmonary function indicator, the number of patient state respirations through each real-time monitoring element information, and the influencing element information of different immersive training information provided by the patient to be processed in the virtual reality pulmonary function data, to determine whether the immersive training information provided by the patient to be processed is reasonable, thereby improving the accuracy and reliability of the debugging of the breathing trainer. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the region. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 This is a flowchart of a personalized debugging method for an intelligent breathing trainer provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to better understand the above technical solution, the technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0021] See also Figure 1 , shows a personalized debugging method for an intelligent breathing trainer, which may include the technical solutions described in the following steps S110-S150.

[0022] S110 , based on the respiratory training scene description information of the patient to be processed that provides immersive training information parsed from the first virtual reality pulmonary function data, obtain the patient's respiratory state information in the area represented by the respiratory training scene description information.

[0023] The immersive training information provided by the pending patient refers to the breathing training to be analyzed in the first virtual reality pulmonary function data. The immersive training information provided by the pending patient may not correspond to the current breathing training in the real scene. The breathing training scene description information provided by the immersive training information provided by the pending patient may refer to personalized breathing training data indicating the current area in the real scene corresponding to the immersive training information provided by the pending patient.

[0024] Generally speaking, the first virtual reality pulmonary function data includes many respiratory exercise description segments. Detecting all of the respiratory exercise description segments in the first virtual reality pulmonary function data requires a large amount of data processing and consumes a lot of resources. Therefore, immersive training information for the patient to be treated can be extracted from the first virtual reality pulmonary function data according to extraction rules. The extraction rules may include extracting nearby respiratory exercises as immersive training information for the patient to be treated, extracting respiratory exercises within a large area with a high incidence of respiratory symptoms as immersive training information for the patient to be treated, and extracting respiratory exercises with a high frequency of symptoms as immersive training information for the patient to be treated.

[0025] In one embodiment, the breathing training scene description information for providing immersive training information for the patient to be treated can be obtained from the first virtual reality pulmonary function data. For the first virtual reality pulmonary function data, the breathing training scene description information of the breathing training is matched for each breathing training analyzed thereon. The breathing training scene description information of the breathing training includes the breathing training scene description information of each real-time monitoring element information in the area where the breathing training is located. The breathing training scene description information can be longitude and latitude.

[0026] The area represented by the description information of the breathing training scene for the patient to be processed that provides immersive training information refers to the area covered by the description information of the breathing training scene for the patient to be processed that provides immersive training information in the current scene. The patient's respiratory status information in the area represented by the description information of the breathing training scene can be obtained through a respiratory monitoring device or a manual monitor. The patient's respiratory status information may at least include the area represented by the description information of the breathing training scene for the patient to be processed that provides immersive training information. The patient's respiratory status information may also include other areas outside the area represented by the description information of the breathing training scene for the patient to be processed that provides immersive training information.

[0027] It can be understood that there may be multiple patients to be treated who are provided with immersive training information. When the areas represented by the breathing training scene description information for multiple patients to be treated who are provided with immersive training information are close to each other, the immersive training information for multiple patients to be treated may correspond to one patient's respiratory status information, that is, the patient's respiratory status information includes the areas represented by the breathing training scene description information for multiple patients to be treated who are provided with immersive training information. Therefore, there is no need to separately obtain the patient's respiratory status information for each area represented by the breathing training scene description information for each patient to be treated who is provided with immersive training information, thereby saving the cost and accuracy of obtaining the patient's respiratory status information.

[0028] S120: Determine, based on the patient's respiratory status information, a patient respiratory function assessment result of the immersive training information provided by the patient to be processed in the patient's respiratory status information.

[0029] The patient respiratory function assessment result of the patient providing immersive training information in the patient respiratory status information is used to indicate the probability of the patient providing immersive training information being monitored in the patient respiratory status information. The higher the patient respiratory function assessment result of the patient providing immersive training information in the patient respiratory status information, the higher the probability of the patient providing immersive training information being monitored in the patient respiratory status information. The lower the patient respiratory function assessment result of the patient providing immersive training information in the patient respiratory status information, the lower the probability of the patient providing immersive training information being monitored in the patient respiratory status information.

[0030] In some possible implementation embodiments, S120 may include: clustering the patient's respiratory state information to obtain a clustering result; the respiratory feature description element representing the respiratory feature of respiratory training in the clustering result is different from the respiratory feature description element representing the respiratory feature of non-respiratory training; extracting one or more second reference respiratory features from the target respiratory feature region in the clustering result; the target respiratory feature region is the respiratory feature region of the region represented by the respiratory training scene description information in the clustering result; determining the target respiratory feature region of each second reference respiratory feature in the clustering result; and determining the patient's respiratory function assessment result of the immersive training information provided by the patient to be processed in the patient's respiratory state information based on the respiratory feature description elements of each respiratory feature in the target respiratory feature region of each second reference respiratory feature in the clustering result. The respiratory feature description element representing the respiratory feature of respiratory training in the clustering result may be a target respiratory feature description element, and the respiratory feature description element representing the respiratory feature of non-respiratory training in the clustering result may be a designated respiratory feature description element. The target respiratory feature description element is different from the designated respiratory feature description element. For example, the target respiratory feature description element may be z, and the designated respiratory feature description element may be v.

[0031] In some possible implementations, a respiratory training segmentation network may be used to perform respiratory training segmentation on the patient's respiratory state information to determine respiratory training regions and non-respiratory training regions from the patient's respiratory state information. The respiratory feature description elements of the respiratory characteristics in the respiratory training regions are then adjusted to target respiratory feature description elements, and the respiratory feature description elements of the respiratory characteristics in the non-respiratory training regions are adjusted to designated respiratory feature description elements to obtain clustering results. The respiratory training segmentation network may be obtained by training a neural network.

[0032] After the clustering results are obtained, an area corresponding to the area represented by the breathing training scene description information providing immersive training information for the patient to be processed can be determined from the clustering results as the target breathing feature area.

[0033] In this embodiment, one or more second reference breathing features are extracted in the target breathing feature area, and one or more second reference breathing features can also be extracted in an area in the target breathing feature area that matches the area represented by the central point breathing training scene description information providing immersive training information for the patient to be treated.

[0034] After obtaining the target breathing feature region, one or more second reference breathing features may be extracted from the target breathing feature region.

[0035] For another example, in the target breathing feature area, a breathing feature is extracted from every first number of breathing features in an area that matches the area represented by the central point breathing training scene description information providing immersive training information for the patient to be processed as a second reference breathing feature.

[0036] After obtaining the second reference breathing feature, a target breathing feature area for each second reference breathing feature is determined in the clustering result. The target breathing feature area of ​​the second reference breathing feature refers to the breathing feature area surrounding the second reference breathing feature in the clustering result. The target breathing feature area of ​​the second reference breathing feature includes the second reference breathing feature and similar breathing features of the second reference breathing feature (i.e., breathing features directly or indirectly adjacent to the second reference breathing feature).

[0037] For example, a rectangular breathing feature region may be constructed based on each second reference breathing feature in the clustering result, and used as a target breathing feature region corresponding to each second reference breathing feature.

[0038] A target respiratory feature area corresponding to each second reference respiratory feature is obtained, and a patient respiratory function assessment result of the immersive training information provided by the patient to be processed in the patient respiratory state information is determined based on the respiratory feature description elements of each respiratory feature in the target respiratory feature area corresponding to each second reference respiratory feature.

[0039] Among them, under the premise that the respiratory feature description element used to represent the respiratory feature of respiratory training in the clustering result is the target respiratory feature description element, the patient respiratory function evaluation result of the patient to be processed providing immersive training information in the patient respiratory status information is determined according to the respiratory feature description elements of each respiratory feature in the target respiratory feature area of ​​each second reference respiratory feature in the clustering result, including: determining the respiratory training description element corresponding to each second reference respiratory feature according to the weight of the respiratory feature of the target respiratory feature description element in the target respiratory feature area of ​​each second reference respiratory feature in the clustering result, the respiratory training description element is a respiratory training description fragment matching element or a respiratory training description fragment non-matching element; determining the patient respiratory function evaluation result of the patient to be processed providing immersive training information in the patient respiratory status information according to the number of second reference respiratory features of the respiratory training description fragment matching element and the number of second reference respiratory features of the respiratory training description fragment non-matching element.

[0040] For each second reference breathing feature, the number of breathing features in which the breathing feature description elements in the target breathing feature area corresponding to the second reference breathing feature are target breathing feature description elements is counted, and then the weight of the breathing features in which the breathing feature description elements in the target breathing feature area corresponding to the second reference breathing feature are target breathing feature description elements is determined, and the breathing training description elements of the second reference breathing features whose proportion reaches the proportion threshold are used as breathing training description segment matching elements, and the breathing training description elements of the second reference breathing features whose proportion does not reach the proportion threshold are used as breathing training description segment non-matching elements.

[0041] The breathing training description element of the second reference breathing feature is used to indicate the abnormal probability of breathing training at the location of the second reference breathing feature in the current scene. That is, if the breathing training description element of the second reference breathing feature is a breathing training description fragment matching element, it indicates that the abnormal probability of breathing training at the location of the second reference breathing feature in the current scene is high; if the breathing training description element of the second reference breathing feature is a breathing training description fragment non-matching element, it indicates that the abnormal probability of breathing training at the location of the second reference breathing feature in the current scene is low. In the current scene, breathing training occupies a certain area. For a real-time monitoring element information on breathing training in the current scene, the area near the real-time monitoring element information has a higher probability of being an abnormality of breathing training. Based on this principle, in the present application, for each second reference breathing feature, if the number of breathing features representing breathing training in the target breathing feature area of ​​the second reference breathing feature is greater, then the probability that the position corresponding to the second reference breathing feature in the current scene is an abnormality of breathing training is higher. Conversely, if the number of breathing features representing non-breathing training in the target breathing feature area of ​​the second reference breathing feature is greater, then the probability that the position corresponding to the second reference breathing feature in the current scene is not an abnormality of breathing training is higher. Therefore, in the above embodiment, the breathing training description element of the second reference breathing feature is determined in combination with the number of breathing features whose breathing feature description elements are target breathing feature description elements in the target breathing feature area including the second reference breathing feature, that is, the breathing training description element is determined with reference to the breathing feature description elements of the second reference breathing feature itself and the breathing feature description elements of similar breathing features of the second reference breathing feature. Compared with determining the breathing training description element of the second reference breathing feature only based on the breathing feature description elements of the second reference breathing feature, the accuracy of the breathing training description element determined for the second reference breathing feature can be guaranteed.

[0042] After determining the respiratory training description elements of all the respective second reference respiratory features in the target respiratory feature area corresponding to the immersive training information provided by the patient to be processed, determine the patient's respiratory function assessment result in the patient's respiratory status information based on the number of second reference respiratory features of the respiratory training description segment matching elements and the number of second reference respiratory features of the respiratory training description segment non-matching elements. For example, the ratio of the number of second reference respiratory features of the respiratory training description segment matching elements and the number of second reference respiratory features of the respiratory training description segment non-matching elements can be used as the patient's respiratory function assessment result in the patient's respiratory status information based on the immersive training information provided by the patient to be processed. For another example, the ratio of the number of second reference respiratory features of the respiratory training description segment matching elements and the number of all second reference respiratory features in the target respiratory feature area corresponding to the immersive training information provided by the patient to be processed can be used as the patient's respiratory function assessment result in the patient's respiratory status information based on the immersive training information provided by the patient to be processed.

[0043] S130. Determine the respiratory training control information corresponding to the immersive training information provided by the patient to be processed based on the number of occurrences of each real-time monitoring element information as a lung function indicator in the area represented by the respiratory training scene description information and the number of patient state respirations through each real-time monitoring element information.

[0044] A piece of real-time monitoring element information in the area represented by the breathing training scene description information for the patient to be processed to provide immersive training information may refer to a current area in the area represented by the breathing training scene description information for the patient to be processed to provide immersive training information.

[0045] In this embodiment, a target time period can be set, and the number of occurrences of each real-time monitoring element information as a lung function indicator and the number of patient status respirations passing through each real-time monitoring element information in the area represented by the breathing training scene description information within the target time period can be counted as the number of occurrences of the real-time monitoring element information as a lung function indicator and the number of patient status respirations passing through each real-time monitoring element information required in S120.

[0046] In some possible implementation embodiments, after obtaining the number of occurrences of each real-time monitoring element information as a pulmonary function indicator and the number of patient state respirations through each real-time monitoring element information, the average value of the number of occurrences of each real-time monitoring element information as a pulmonary function indicator can be determined as the positioning density of the pulmonary function indicator, and the average value of the number of patient state respirations through each real-time monitoring element information can be determined as the trajectory density. Based on the positioning density and the trajectory density, the respiratory training control information corresponding to the immersive training information provided by the patient to be processed is determined. For example, the positioning density and trajectory density of each real-time monitoring element information are summed to obtain the density and value of the real-time monitoring element information. The density and value of the real-time monitoring element information in the area represented by the respiratory training scene description information for providing immersive training information to the patient to be processed are averaged or summed to obtain the respiratory training control information corresponding to the immersive training information provided by the patient to be processed.

[0047] In some possible embodiments, S130 may also include: determining the lung respiratory state assessment level of each real-time monitoring element information according to the number of occurrences of each real-time monitoring element information as a lung function indicator in the area represented by the respiratory training scene description information and the number of patient state respirations through each real-time monitoring element information; generating a lung respiratory state assessment level set corresponding to the immersive training information provided by the patient to be processed based on the lung respiratory state assessment level of each real-time monitoring element information, the lung respiratory state assessment level set including the target respiratory feature representing each real-time monitoring element information in the area represented by the respiratory training scene description information; the respiratory feature description element of the target respiratory feature is determined according to the lung respiratory state assessment level of the real-time monitoring element information corresponding to the target respiratory feature; and determining the respiratory training control information corresponding to the immersive training information provided by the patient to be processed according to the lung respiratory state assessment level set corresponding to the immersive training information provided by the patient to be processed.

[0048] In some embodiments, for each real-time monitoring element information in the area represented by the respiratory training scene description information, the number of occurrences of the real-time monitoring element information as a lung function indicator and the number of occurrences of the patient's state breathing times through the real-time monitoring element information can be calculated to obtain the lung respiratory state assessment level of the real-time monitoring element information; then, the lung respiratory state assessment level of the real-time monitoring element information is loaded into the respiratory training debugging network to obtain the target lung respiratory state assessment level corresponding to each real-time monitoring element information; the target lung respiratory state assessment level corresponding to each real-time monitoring element information is used as the respiratory feature description element of the target respiratory feature corresponding to each real-time monitoring element information, and a lung respiratory state assessment level set corresponding to the immersive training information provided by the patient to be processed is generated, and each target respiratory feature is distributed in the lung respiratory state assessment level set according to the relative position of the corresponding real-time monitoring element information in the area represented by the respiratory training scene description information. Wherein, the respiratory training debugging network can be a value range of the respiratory feature description element, for example, 1-10, and the respiratory training debugging network can be 1-10.

[0049] In some embodiments, the lung respiratory state assessment level of each real-time monitoring element information can be loaded into the respiratory training debugging network to obtain the third transition lung respiratory state assessment level of each real-time monitoring element information; the third transition lung respiratory state assessment level of each real-time monitoring element information is rounded to obtain the target lung respiratory state assessment level corresponding to each real-time monitoring element information.

[0050] In some other embodiments, the lung respiratory state assessment level of each real-time monitoring element information can be differentially quantified to obtain a first transition lung respiratory state assessment level of each real-time monitoring element information; the differential quantification processing is used to quantify the difference between the lung respiratory state assessment levels corresponding to different real-time monitoring element information; the first transition lung respiratory state assessment level of each real-time monitoring element information is loaded into the respiratory training debugging network to obtain a second transition lung respiratory state assessment level of each real-time monitoring element information; the first transition lung respiratory state assessment level of each real-time monitoring element information is rounded to obtain a target lung respiratory state assessment level corresponding to each real-time monitoring element information.

[0051] In this embodiment, for each real-time monitoring element information, a logarithmic operation can be performed on the lung respiratory state assessment level corresponding to each real-time monitoring element information to achieve differential quantification processing of the lung respiratory state assessment level corresponding to each real-time monitoring element information, and obtain the first transition lung respiratory state assessment level corresponding to each real-time monitoring element information.

[0052] After obtaining the target lung respiratory state assessment level of each real-time monitoring element information, a target respiratory feature can be determined for each real-time monitoring element information, and the target lung respiratory state assessment level of each real-time monitoring element information can be used as the respiratory feature description element of the corresponding target respiratory feature. According to the relative position of each real-time monitoring element information in the area represented by the respiratory training scene description information of the immersive training information provided to the patient to be processed, the target respiratory features corresponding to each real-time monitoring element information are distributed to build a lung respiratory state assessment level set corresponding to the immersive training information provided to the patient to be processed.

[0053] Generally speaking, the area formed by the target respiratory characteristics is usually not a regular area. Therefore, in order to make the lung respiratory state assessment level set more regular, initial data can also be obtained, and the baseline respiratory characteristics of the initial data are used as the target respiratory characteristics corresponding to the key real-time monitoring element information in the area represented by the respiratory training scene description information of the immersive training information provided by the patient to be treated (which can be the most baseline real-time monitoring element information in the area represented by the respiratory training scene description information of the immersive training information provided by the patient to be treated). Based on the target respiratory characteristics corresponding to the key real-time monitoring element information, according to the distribution between the real-time monitoring element information in the area represented by the respiratory training scene description information of the immersive training information provided by the patient to be treated, the target respiratory characteristics corresponding to the real-time monitoring element information in the area represented by the respiratory training scene description information of the immersive training information provided by the patient to be treated are determined from the initial data. Then, the respiratory characteristic description elements of the target respiratory characteristics in the initial data are adjusted to the target traffic flow of the corresponding real-time monitoring element information, and the respiratory characteristic description elements of other respiratory characteristics in the initial data except the target respiratory characteristics are adjusted to preset respiratory characteristic description elements, and the initial data after the respiratory characteristic description elements are adjusted are obtained as the lung respiratory state assessment level set.

[0054] The initial data may be obtained by capturing an image of the area represented by the description of the breathing training scene in which the patient is provided with immersive training information using a camera or an electronic device equipped with a camera. The captured image may include at least the area represented by the description of the breathing training scene in which the patient is provided with immersive training information, and the initial data may also include other areas outside the area represented by the description of the breathing training scene in which the patient is provided with immersive training information.

[0055] As mentioned above, the target breathing features correspond to the immersive training information provided by the patient to be processed in the lung respiratory state assessment level set. Therefore, one or more first reference breathing features can be extracted from each target breathing feature in the lung respiratory state assessment level set; the target breathing feature area of ​​each first reference breathing feature is determined in the lung respiratory state assessment level set; the reference breathing feature description element of each first reference breathing feature is determined based on the breathing feature description elements of each breathing feature in the target breathing feature area of ​​each first reference breathing feature; the breathing training control information corresponding to the immersive training information provided by the patient to be processed is determined based on the reference breathing feature description elements of each first reference breathing feature in the lung respiratory state assessment level set.

[0056] In other embodiments, determining the respiratory training control information corresponding to the immersive training information provided to the patient to be treated based on the lung respiratory state assessment level set may include: extracting one or more first reference respiratory features from each target respiratory feature in the lung respiratory state assessment level set; determining the target respiratory feature area of ​​each first reference respiratory feature in the lung respiratory state assessment level set; determining the reference respiratory feature description element of each first reference respiratory feature based on the respiratory feature description elements of each respiratory feature in the target respiratory feature area of ​​each first reference respiratory feature; determining the respiratory training control information corresponding to the immersive training information provided to the patient to be treated based on the reference respiratory feature description elements of each first reference respiratory feature in the lung respiratory state assessment level set.

[0057] Generally, there are a large number of target respiratory features in the lung respiratory state assessment level set. In order to improve data processing efficiency, one or more first reference respiratory features can be extracted from the target respiratory features in the lung respiratory state assessment level set, and then the respiratory training control information corresponding to the immersive training information provided to the patient to be processed is determined based on the respiratory feature description elements of each respiratory feature in the target respiratory feature area of ​​the first reference respiratory feature.

[0058] Exemplarily, a respiratory feature region formed by target respiratory features in a set of lung respiratory state assessment levels can be used as an effective flow region, and one or more first reference respiratory features can be extracted from the effective flow region. For example, the central point respiratory training scene description information of the immersive training information provided by the patient to be processed can be determined based on the respiratory training scene description information of the immersive training information provided by the patient to be processed, and then one or more first reference respiratory features can be extracted from an area in the effective flow region that matches the area represented by the central point respiratory training scene description information of the immersive training information provided by the patient to be processed; for another example, one or more first reference respiratory features can be directly extracted from the effective flow region.

[0059] After obtaining the first reference breathing feature, a target breathing feature region is determined for each first reference breathing feature in the lung breathing state assessment level set. The target breathing feature region for the first reference breathing feature refers to a breathing feature region in the lung breathing state assessment level set that surrounds the first reference breathing feature. The target breathing feature region for the first reference breathing feature includes the first reference breathing feature and similar breathing features to the first reference breathing feature (i.e., breathing features that are directly or indirectly adjacent to the first reference breathing feature).

[0060] For example, a rectangular breathing feature region can be constructed based on each first reference breathing feature in the lung breathing state assessment level set, serving as the target breathing feature region corresponding to each first reference breathing feature. Alternatively, a closed breathing feature region (which can be of any shape) can be constructed in the lung breathing state assessment level set to include each first reference breathing feature, serving as the target breathing feature region corresponding to each first reference breathing feature. The area of ​​the target breathing feature region corresponding to the first reference breathing feature is determined based on the current area represented by each breathing feature in the initial data (or in the lung breathing state assessment level set).

[0061] S140. Determine a respiratory training similarity corresponding to the immersive training information provided by the patient to be processed based on respiratory state influencing factor information in the first virtual reality pulmonary function data and influencing factor information of the area represented by the respiratory training scene description information in the second virtual reality pulmonary function data.

[0062] The second virtual reality pulmonary function data may refer to virtual reality pulmonary function data that is different from the first virtual reality pulmonary function data. The respiratory state influencing factor information may refer to the linear information of the immersive training information provided by the patient to be processed in the virtual reality pulmonary function data, which may be indicated by a motion state segment. A motion state segment refers to a point in the virtual reality pulmonary function data that is used to describe a current real-time monitoring factor information in the current scene. At this time, the influencing factor information of the respiratory training in the virtual reality pulmonary function data is described by a motion state segment string (the motion state segment string may be a point string composed of each motion state segment in the center point of the immersive training information provided by the patient to be processed), and the influencing factor information may include multiple motion state segments and the respiratory training scene description information of each motion state segment.

[0063] In some possible implementations, based on the respiratory training scene description information of the patient providing immersive training information parsed from the first virtual reality pulmonary function data, the area represented by the respiratory training scene description information of the patient providing immersive training information can be determined, and then the influencing factor information of the area represented by the respiratory training scene description information of the patient providing immersive training information in the second virtual reality pulmonary function data can be obtained. For example, a motion state segment string describing a target center point can be obtained as the influencing factor information of the area represented by the respiratory training scene description information of the patient providing immersive training information in the second virtual reality pulmonary function data; wherein the target center point can refer to the center point of the area represented by the respiratory training scene description information of the patient providing immersive training information in the extension direction of the immersive training information provided by the patient; for example, if the immersive training information provided by the patient is in the east-west direction, the center point of the area represented by the respiratory training scene description information of the patient providing immersive training information along the east-west direction is used as the target center point, and then the motion state segment string describing the target center point is obtained as the influencing factor information of the area represented by the respiratory training scene description information of the patient providing immersive training information in the second virtual reality pulmonary function data.

[0064] In other embodiments, a target area represented by the breathing training scene description information of the immersive training information provided by the patient to be processed can be obtained, and then local virtual reality pulmonary function data located in the target area in the second virtual reality pulmonary function data can be obtained. Based on the breathing training scene description information of each breathing training in the local virtual reality pulmonary function data, the breathing training shape of each breathing training is drawn to obtain a road network shape corresponding to the local virtual reality pulmonary function data, and then the influencing factor information of each breathing training shape in the road network shape is obtained as the influencing factor information of the area represented by the breathing training scene description information of the immersive training information provided by the patient to be processed in the second virtual reality pulmonary function data.

[0065] After obtaining the respiratory state influencing factor information of the patient to be processed providing immersive training information in the first virtual reality pulmonary function data and the influencing factor information of the area represented by the respiratory training scene description information in the second virtual reality pulmonary function data, the respiratory training similarity corresponding to the immersive training information provided by the patient to be processed can be determined based on the similarity between the respiratory state influencing factor information of the patient to be processed in the first virtual reality pulmonary function data and the influencing factor information of the area represented by the respiratory training scene description information of the patient to be processed providing immersive training information in the second virtual reality pulmonary function data.

[0066] For example, if there is one second virtual reality pulmonary function data and there is also one influencing factor information of the area represented by the breathing training scene description information of the immersive training information provided by the patient to be processed in the second virtual reality pulmonary function data, then the similarity between the breathing state influencing factor information of the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data and the influencing factor information of the area represented by the breathing training scene description information in the second virtual reality pulmonary function data is directly calculated as the breathing training similarity corresponding to the immersive training information provided by the patient to be processed.

[0067] For another example, if there is only one second virtual reality pulmonary function data, and there are multiple influencing factor information of the area represented by the breathing training scene description information of the immersive training information provided by the patient to be processed in the second virtual reality pulmonary function data, then the similarity between the breathing state influencing factor information of the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data and each corresponding influencing factor information of the area represented by the breathing training scene description information in the second virtual reality pulmonary function data is directly calculated, and then the maximum value of the calculated similarities is obtained as the breathing training similarity corresponding to the immersive training information provided by the patient to be processed.

[0068] For another example, there are multiple second virtual reality pulmonary function data. For each second virtual reality pulmonary function data, the similarity between the respiratory state influencing factor information of the patient to be processed in the first virtual reality pulmonary function data and the influencing factor information corresponding to the area represented by the respiratory training scene description information of the patient to be processed in the second virtual reality pulmonary function data is determined and calculated as the single similarity corresponding to the second virtual reality pulmonary function data (if there are multiple influencing factor information corresponding to the respiratory state influencing factor information of the patient to be processed in each second virtual reality pulmonary function data, the respiratory state influencing factor information of the patient to be processed in the first virtual reality pulmonary function data is determined and calculated). For each influencing factor information corresponding to the area represented by the scene description information in the second virtual reality pulmonary function data, similarity between the influencing factor information and the respiratory state influencing factor information in the first virtual reality pulmonary function data of the patient to be processed providing immersive training information is determined. Then, a maximum value is extracted from the similarities between multiple respiratory state influencing factor information corresponding to the second virtual reality pulmonary function data and the respiratory state influencing factor information in the first virtual reality pulmonary function data of the patient to be processed providing immersive training information, and the maximum value is used as the single similarity corresponding to the second virtual reality pulmonary function data. Then, the single similarities corresponding to the multiple second virtual reality pulmonary function data are averaged to obtain the respiratory training similarity corresponding to the immersive training information provided by the patient to be processed.

[0069] In some possible implementations, the patient to be treated provides immersive training information, and the respiratory state influencing factor information in the first virtual reality pulmonary function data includes respiratory training scene description information of each of the plurality of first motion state segments, and the influencing factor information of the area represented by the respiratory training scene description information in the second virtual reality pulmonary function data includes respiratory training scene description information of each of the plurality of second motion state segments; Determining the respiratory training similarity corresponding to the immersive training information provided by the patient based on similarity between respiratory state influencing factor information in the first virtual reality pulmonary function data of the immersive training information provided by the patient and influencing factor information in the second virtual reality pulmonary function data of the area represented by the respiratory training scene description information, including: determining a matching motion state segment that matches each first motion state segment from a plurality of second motion state segments; determining a relative coefficient between the first motion state segment and the corresponding matching motion state segment based on the respiratory training scene description information of the first motion state segment and the respiratory training scene description information of the corresponding matching motion state segment, the relative coefficient including a relative distance and a relative angle; determining the similarity between the respiratory state influencing factor information in the first virtual reality pulmonary function data of the immersive training information provided by the patient and influencing factor information in the second virtual reality pulmonary function data of the area represented by the respiratory training scene description information based on the relative coefficient between each first motion state segment and the corresponding matching motion state segment; and determining the respiratory training similarity corresponding to the immersive training information provided by the patient based on similarity between the respiratory state influencing factor information in the first virtual reality pulmonary function data of the immersive training information provided by the patient and influencing factor information in the second virtual reality pulmonary function data of the area represented by the respiratory training scene description information.

[0070] Finally, the similarity between the respiratory state influencing factor information of the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data and the influencing factor information of the area represented by the respiratory training scene description information in the second virtual reality pulmonary function data can be used as the respiratory training similarity corresponding to the immersive training information provided by the patient to be processed.

[0071] The similarity between the respiratory state influencing factor information in the first virtual reality pulmonary function data for the patient providing immersive training information and the influencing factor information in the second virtual reality pulmonary function data for the region represented by the respiratory training scene description information can be a number between [0-1]. The larger the number, the higher the similarity, indicating that the current region represented by the two influencing factor information from different virtual reality pulmonary function data may be the same respiratory training exercise in the real world, or vice versa. For the patient providing immersive training information, if other virtual reality pulmonary function data also contain this patient providing immersive training information, then the probability that this patient providing immersive training information is abnormal in the first virtual reality pulmonary function data is low. Conversely, if no other virtual reality pulmonary function data contains this patient providing immersive training information, then the probability that this patient providing immersive training information is abnormal in the first virtual reality pulmonary function data is high.

[0072] S150: Determine a personalized debugging result of the breathing training of the patient to be processed in the first virtual reality lung function data by providing immersive training information according to at least two of the patient's respiratory function evaluation result, the breathing training control information, and the breathing training similarity.

[0073] After obtaining the patient's respiratory function assessment result of the respiratory training to be classified in the patient's respiratory state information, the respiratory training control information corresponding to the immersive training information provided by the patient to be processed, and the corresponding respiratory training similarity, the personalized debugging result of the respiratory training provided by the patient to be processed in the first virtual reality pulmonary function data can be comprehensively determined based on at least two of the three information. The personalized debugging result of the respiratory training is used to indicate whether the immersive training information provided by the patient to be processed is respiratory training in the first virtual reality pulmonary function data. If the immersive training information provided by the patient to be processed is respiratory training in the first virtual reality pulmonary function data, it means that the immersive training information provided by the patient to be processed, as parsed in the first virtual reality pulmonary function data, does not exist in the current scene; if the immersive training information provided by the patient to be processed is not respiratory training in the first virtual reality pulmonary function data, it means that the immersive training information provided by the patient to be processed, as parsed in the first virtual reality pulmonary function data, also exists in the current scene.

[0074] When the patient's respiratory function assessment result is less than the patient's respiratory function assessment result threshold, it means that the immersive training information provided by the patient to be processed is difficult to be detected in the patient's respiratory status information, and the abnormal probability that the immersive training information provided by the patient to be processed is respiratory training is high; when the respiratory training control information is less than the abnormal respiratory training probability threshold, it means that the lung function indicators and the patient's state breathing number appearing in the immersive training information provided by the patient to be processed are small, and the abnormal probability that the immersive training information provided by the patient to be processed is respiratory training is high; when the respiratory training similarity is less than the respiratory training similarity threshold, it means that the probability of the immersive training information provided by the patient to be processed appearing in other virtual reality lung function data is low, and the abnormal probability that the immersive training information provided by the patient to be processed is respiratory training is high.

[0075] Therefore, in some embodiments, at least two of the patient's respiratory function assessment result, respiratory training control information, and respiratory training similarity can be obtained, and at least one of the at least two obtained is less than a corresponding threshold value, to obtain the result that the patient to be processed provides immersive training information in the first virtual reality pulmonary function data as a personalized debugging result of respiratory training. For example, if the at least two are the patient's respiratory function assessment result and the respiratory training control information, then if any one of the patient's respiratory function assessment result is less than the patient's respiratory function assessment result threshold or the respiratory training control information is less than the abnormal respiratory training probability threshold, then the result that the patient to be processed provides immersive training information in the first virtual reality pulmonary function data as a personalized debugging result of respiratory training is obtained. For another example, if the at least two are the patient's respiratory function assessment result and the respiratory training similarity, then if any one of the patient's respiratory function assessment result is less than the patient's respiratory function assessment result threshold or the respiratory training similarity is less than the respiratory training similarity threshold, then the result that the patient to be processed provides immersive training information in the first virtual reality pulmonary function data as a personalized debugging result of respiratory training is obtained.

[0076] In another embodiment, the patient's respiratory function assessment result, respiratory training control information, and respiratory training similarity may also be obtained. When at least one of the patient's respiratory function assessment result is less than a patient's respiratory function assessment result threshold, the respiratory training control information is less than an abnormal respiratory training probability threshold, and the respiratory training similarity is less than a respiratory training similarity threshold, the result of obtaining that the patient provided immersive training information in the first virtual reality pulmonary function data is a personalized debugging result of respiratory training is obtained. In another embodiment, the result of obtaining that the patient provided immersive training information in the first virtual reality pulmonary function data is a personalized debugging result of respiratory training is obtained when at least two of the patient's respiratory function assessment result is less than a patient's respiratory function assessment result threshold, the respiratory training control information is less than an abnormal respiratory training probability threshold, and the respiratory training similarity is less than a respiratory training similarity threshold occur. In yet another embodiment, the result of obtaining that the patient provided immersive training information in the first virtual reality pulmonary function data is a personalized debugging result of respiratory training is obtained when the patient's respiratory function assessment result is less than a patient's respiratory function assessment result threshold, the respiratory training control information is less than an abnormal respiratory training probability threshold, and the respiratory training similarity is less than a respiratory training similarity threshold. Among them, the patient respiratory function assessment result threshold, abnormal breathing training probability threshold and breathing training similarity threshold can be values ​​set based on needs, and this application does not limit them.

[0077] In some possible implementation embodiments, at least two of the patient respiratory function assessment results, respiratory training control information, and respiratory training similarity corresponding to the immersive training information provided by the patient to be processed can also be input into the respiratory training analysis network to obtain an abnormal probability result that the immersive training information provided by the patient to be processed is predicted to be respiratory training; if the abnormal probability result that the immersive training information provided by the patient to be processed is predicted to be respiratory training is less than the respiratory training probability threshold, a personalized debugging result is obtained that the immersive training information provided by the patient to be processed is not respiratory training in the first virtual reality pulmonary function data; if the abnormal probability result that the immersive training information provided by the patient to be processed is predicted to be respiratory training is not less than the respiratory training probability threshold, a personalized debugging result is obtained that the immersive training information provided by the patient to be processed is respiratory training in the first virtual reality pulmonary function data.

[0078] After obtaining the abnormal probability result of the patient providing immersive training information predicted as breathing training, if the abnormal probability result of the patient providing immersive training information predicted as breathing training is less than the breathing training probability threshold, it is obtained that the patient providing immersive training information in the first virtual reality pulmonary function data is not a personalized debugging result of breathing training; if the abnormal probability result of the patient providing immersive training information predicted as breathing training is not less than the breathing training probability threshold, it is obtained that the patient providing immersive training information in the first virtual reality pulmonary function data is a personalized debugging result of breathing training.

[0079] It is worth mentioning that when determining the personalized debugging result of the respiratory training provided by the patient in the first virtual reality pulmonary function data based on two of the patient's respiratory function assessment result, respiratory training control information, and respiratory training similarity corresponding to the immersive training information provided by the patient, the other one that is not used may not be obtained. For example, when the other one that is not used is the patient's respiratory function assessment result corresponding to the immersive training information provided by the patient, the patient's respiratory state information may not be parsed (i.e., S110-S120 are not executed); for another example, when the other one that is not used is the respiratory training control information corresponding to the immersive training information provided by the patient, the respiratory training control information may not be parsed (i.e., S130 is not executed); for another example, when the other one that is not used is the respiratory training similarity, the respiratory training similarity may not be parsed (i.e., S140 is not executed).

[0080] In this embodiment, if there are multiple patients to be treated who provide immersive training information, the immersive training information can be provided to each patient to be treated separately in accordance with the aforementioned S110-S150 method to determine their respective personalized breathing training debugging results, which will not be repeated here.

[0081] In the present application, the patient respiratory function assessment result of the patient's respiratory function information provided by the patient to be processed is determined in the patient's respiratory function information according to the patient's respiratory state information; and the respiratory training control information corresponding to the immersive training information provided by the patient to be processed is determined according to the number of occurrences of each real-time monitoring element information as a lung function indicator in the area represented by the respiratory training scene description information and the number of patient state respirations through each real-time monitoring element information; and the respiratory function information provided by the patient to be processed is determined according to the respiratory state influencing element information in the first virtual reality lung function data of the immersive training information provided by the patient to be processed and the influencing element information of the area represented by the respiratory training scene description information in the second virtual reality lung function data. Then, according to at least two of the patient's respiratory function assessment result, the respiratory training control information and the respiratory training similarity, the personalized debugging result of the respiratory training provided by the patient to be processed in the first virtual reality pulmonary function data is determined, thereby combining at least three of the four different forms of respiratory training information, namely, the number of occurrences of the real-time monitoring element information as a pulmonary function indicator, the number of patient state respirations through each real-time monitoring element information, and the influencing element information of different immersive training information provided by the patient to be processed in the virtual reality pulmonary function data, to determine whether it is reasonable for the patient to be processed to provide the immersive training information, thereby improving the accuracy and reliability of the debugging of the respiratory trainer.

[0082] In one embodiment, after S150, the method may further include: if the personalized debugging result of the breathing training is to provide immersive training information for the patient to be processed and the first virtual reality pulmonary function data is breathing training, providing the immersive training information for the patient to be processed in the first virtual reality pulmonary function data for early warning.

[0083] Based on the breathing training scene description information of the immersive training information provided by the patient to be processed, the patient's respiratory status information of the area represented by the breathing training scene description information is obtained, and the number of occurrences of each real-time monitoring element information as a lung function indicator and the number of patient state respirations through each real-time monitoring element information in the area represented by the breathing training scene description information are determined, and the information of the target area in the first virtual reality lung function data and the second virtual reality lung function data is determined.

[0084] Based on the patient's respiratory state information, a respiratory training feature of the patient's respiratory state information (i.e., a respiratory feature description element of the respiratory feature in the target respiratory feature region of each of the second reference respiratory features in the aforementioned embodiment) can be determined: respiratory feature description elements of the respiratory feature in the target respiratory feature region of each of the one or more second reference respiratory features are determined. Then, based on the respiratory training feature of the patient's respiratory state information, a respiratory training feature associated with the patient's respiratory state information is determined—the patient's respiratory function assessment result in the patient's respiratory state information based on the immersive training information provided by the patient to be processed.

[0085] Based on the number of occurrences of real-time monitoring element information as lung function indicators and the number of patient-state respirations obtained through each piece of real-time monitoring element information, a trajectory and lung function indicator breathing training characteristics (i.e., a lung breathing state assessment level set in the aforementioned embodiment) can be determined. The lung breathing state assessment level set is constructed based on the number of occurrences of real-time monitoring element information as lung function indicators and the number of patient-state respirations obtained through each piece of real-time monitoring element information. Then, based on the trajectory and lung function indicator breathing training characteristics, a breathing training feature associated with the trajectory and lung function indicator is determined—that is, breathing training control information corresponding to the immersive training information provided by the patient to be processed.

[0086] Based on the road network of the target area in the first virtual reality pulmonary function data and the second virtual reality pulmonary function data, the breathing training shape features (i.e., the influencing factors information of the respiratory state in the first virtual reality pulmonary function data and the influencing factors information of the area represented by the breathing training scene description information in the second virtual reality pulmonary function data) can be determined. Based on the road network of the target area in the first virtual reality pulmonary function data and the second virtual reality pulmonary function data, the influencing factors information of the respiratory state in the first virtual reality pulmonary function data and the influencing factors information of the area represented by the breathing training scene description information in the second virtual reality pulmonary function data are determined. Then, based on the breathing training shape features, a breathing training feature related to the breathing training shape, namely, the breathing training similarity, is determined.

[0087] Then, breathing training judgment is made by combining the breathing training features related to the patient's respiratory state information (the patient's respiratory function assessment results in the patient's respiratory state information provided by the patient to be processed), the breathing training features related to the trajectory and lung function indicators (the breathing training control information corresponding to the immersive training information provided by the patient to be processed), and the breathing training features related to the breathing training shape (the breathing training similarity corresponding to the immersive training information provided by the patient to be processed).

[0088] Afterwards, if it is determined that the immersive training information provided by the patient to be processed is breathing training in the first virtual reality pulmonary function data, the immersive training information provided by the patient to be processed is directly deleted from the first virtual reality pulmonary function data; if it is determined that the immersive training information provided by the patient to be processed is not breathing training in the first virtual reality pulmonary function data, the immersive training information provided by the patient to be processed is retained in the first virtual reality pulmonary function data.

[0089] Based on the above, a personalized debugging device for an intelligent breathing trainer is provided, the device comprising: a data acquisition module, configured to acquire, based on the respiratory training scene description information of the patient to be processed and providing immersive training information, the patient's respiratory state information in the area represented by the respiratory training scene description information; a proportion determination module, configured to determine, in combination with the patient's respiratory status information, a patient respiratory function assessment result of the immersive training information provided by the patient to be processed in the patient's respiratory status information; an information determination module for determining breathing training control information corresponding to the immersive training information provided by the patient to be processed, based on the number of occurrences of each real-time monitoring element information as a lung function indicator in the area represented by the breathing training scene description information and the number of patient state respirations through each real-time monitoring element information; a similarity determination module, configured to determine a similarity of the respiratory training corresponding to the immersive training information provided by the patient to be processed, by combining information on factors influencing the respiratory state of the patient to be processed in the first virtual reality pulmonary function data and information on factors influencing the area represented by the respiratory training scene description information in the second virtual reality pulmonary function data; The result debugging module is used to determine a personalized debugging result of the breathing training of the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data by combining at least two of the patient's respiratory function assessment result, the breathing training control information, and the breathing training similarity.

[0090] Based on the above, a personalized debugging system for an intelligent breathing trainer is shown, which includes a processor and a memory that communicate with each other. The processor is used to read a computer program from the memory and execute it to implement the above method.

[0091] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0092] In summary, based on the above scheme, the patient's respiratory function evaluation result of the immersive training information provided by the patient to be processed is determined in the patient's respiratory status information according to the patient's respiratory status information; and the respiratory training control information corresponding to the immersive training information provided by the patient to be processed is determined according to the number of occurrences of each real-time monitoring element information as a lung function indicator in the area represented by the respiratory training scene description information and the number of patient state respirations through each real-time monitoring element information; and the immersive training provided by the patient to be processed is determined according to the respiratory state influencing element information in the first virtual reality lung function data and the influencing element information of the area represented by the respiratory training scene description information in the second virtual reality lung function data. Then, according to at least two of the patient's respiratory function assessment result, the respiratory training control information, and the respiratory training similarity, the personalized debugging result of the respiratory training provided by the patient to be processed in the first virtual reality pulmonary function data is determined, thereby combining at least three of four different forms of respiratory training information, namely, the number of occurrences of the real-time monitoring element information as a pulmonary function indicator, the number of patient state respirations through each real-time monitoring element information, and the influencing element information of different immersive training information provided by the patient to be processed in the virtual reality pulmonary function data, to determine whether it is reasonable for the patient to be processed to provide the immersive training information, thereby improving the accuracy and reliability of the debugging of the respiratory trainer.

[0093] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-mentioned methods and systems can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the system and its modules of the present application. Not only can hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc. be implemented, they can also be implemented using software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0094] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other possible beneficial effects.

Claims

1. A personalized debugging method for an intelligent breathing trainer, characterized in that: The method comprises: Based on the respiratory training scene description information of the patient to be processed that provides immersive training information parsed from the first virtual reality lung function data, obtaining the patient's respiratory state information in the area represented by the respiratory training scene description information; Determining, in combination with the patient's respiratory status information, a patient respiratory function assessment result of the patient to be processed providing immersive training information in the patient's respiratory status information; Determining the respiratory training control information corresponding to the immersive training information provided by the patient to be processed by combining the number of occurrences of each real-time monitoring element information as a lung function indicator in the area represented by the respiratory training scene description information and the number of patient state respirations through each real-time monitoring element information; Determining a respiratory training similarity corresponding to the immersive training information provided by the patient to be processed, combining respiratory state influencing factor information of the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data and influencing factor information of the area represented by the respiratory training scene description information in the second virtual reality pulmonary function data; Combining at least two of the patient's respiratory function assessment result, the respiratory training control information, and the respiratory training similarity, a respiratory training personalized debugging result of the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data is determined.

2. The method according to claim 1, wherein The method of determining the respiratory training control information corresponding to the immersive training information provided by the patient to be processed by combining the number of occurrences of each real-time monitoring element information as a lung function indicator in the area represented by the respiratory training scene description information and the number of patient state respirations through each real-time monitoring element information includes: Determining a lung respiratory status assessment level for each of the real-time monitoring element information, based on the number of occurrences of each of the real-time monitoring element information as a lung function indicator in the area represented by the breathing training scenario description information and the number of patient state respirations through each of the real-time monitoring element information; Based on the pulmonary respiratory state assessment level of each of the real-time monitoring element information, generating a pulmonary respiratory state assessment level set corresponding to the immersive training information provided by the patient to be treated, the pulmonary respiratory state assessment level set including a target respiratory feature representing each of the real-time monitoring element information in the area represented by the respiratory training scene description information; the respiratory feature description element of the target respiratory feature is determined in combination with the pulmonary respiratory state assessment level of the real-time monitoring element information corresponding to the target respiratory feature; In combination with the lung respiratory state assessment level set corresponding to the immersive training information provided by the patient to be processed, the respiratory training control information corresponding to the immersive training information provided by the patient to be processed is determined.

3. The method according to claim 1, wherein The respiratory state influencing factor information of the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data includes respiratory training scene description information of each of a plurality of first motion state segments, and the influencing factor information of the region represented by the respiratory training scene description information in the second virtual reality pulmonary function data includes respiratory training scene description information of each of a plurality of second motion state segments; and determining the respiratory training similarity corresponding to the immersive training information provided by the patient to be processed by combining the similarity between the respiratory state influencing factor information of the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data and the influencing factor information of the region represented by the respiratory training scene description information in the second virtual reality pulmonary function data, includes: determining, from the plurality of second motion state segments, a matching motion state segment that matches each of the first motion state segments; Determine a relative coefficient between the first motion state segment and the corresponding matching motion state segment based on the breathing training scene description information of the first motion state segment and the breathing training scene description information of the corresponding matching motion state segment, wherein the relative coefficient includes a relative distance and a relative angle; determining a similarity between the respiratory state influencing factor information and the influencing factor information based on a relative coefficient between each of the first motion state segments and the corresponding matching motion state segment; Determining, based on the similarities, a breathing training similarity corresponding to the immersive training information provided by the patient to be processed; Among them, it also includes: determining the lung respiratory state evaluation level of each real-time monitoring element information according to the number of occurrences of each real-time monitoring element information as a lung function indicator in the area represented by the breathing training scene description information and the number of patient state respirations passing through each real-time monitoring element information; generating a lung respiratory state evaluation level set corresponding to the immersive training information provided by the patient to be processed based on the lung respiratory state evaluation level of each real-time monitoring element information, the lung respiratory state evaluation level set including the target breathing feature representing each real-time monitoring element information in the area represented by the breathing training scene description information; the breathing feature description element of the target breathing feature is determined according to the lung respiratory state evaluation level of the real-time monitoring element information corresponding to the target breathing feature; determining the breathing training control information corresponding to the immersive training information provided by the patient to be processed according to the lung respiratory state evaluation level set corresponding to the immersive training information provided by the patient to be processed; The immersive training information provided by the patient to be processed refers to the breathing training to be parsed in the first virtual reality pulmonary function data. The immersive training information provided by the patient to be processed may not correspond to the current breathing training in the real scene; the breathing training scene description information of the immersive training information provided by the patient to be processed is used to indicate the personalized breathing training data of the current area corresponding to the immersive training information provided by the patient to be processed in the real scene; Among them, the area represented by the breathing training scene description information of the patient to be processed providing immersive training information refers to the area covered by the breathing training scene description information of the patient to be processed providing immersive training information in the current scene, and the patient's respiratory status information in the area represented by the breathing training scene description information is obtained through a respiratory monitor and can also be obtained through a manual monitor; the patient's respiratory status information at least includes the area represented by the breathing training scene description information of the patient to be processed providing immersive training information, and the patient's respiratory status information also includes other areas outside the area represented by the breathing training scene description information of the patient to be processed providing immersive training information; Among them, the patient respiratory function assessment result of the patient providing immersive training information in the patient respiratory status information is used to indicate the probability of the patient providing immersive training information being monitored in the patient respiratory status information. The higher the patient respiratory function assessment result of the patient providing immersive training information in the patient respiratory status information, the higher the probability of the patient providing immersive training information being monitored in the patient respiratory status information. The lower the patient respiratory function assessment result of the patient providing immersive training information in the patient respiratory status information, the lower the probability of the patient providing immersive training information being monitored in the patient respiratory status information.

4. The method according to claim 2, wherein The determining of the respiratory training control information corresponding to the immersive training information provided by the patient to be processed in combination with the lung respiratory state assessment level set corresponding to the immersive training information provided by the patient to be processed includes: extracting one or more first reference breathing features from the target breathing features of the lung breathing state assessment level set; determining a target respiratory feature region for each of the first reference respiratory features in the lung respiratory state assessment level set; determining a reference breathing feature description element for each first reference breathing feature according to the breathing feature description element of each breathing feature in the target breathing feature area of ​​each first reference breathing feature; The respiratory training control information corresponding to the immersive training information provided by the patient to be processed is determined by combining the reference respiratory feature description elements of each of the first reference respiratory features in the pulmonary respiratory state assessment level set.

5. The method according to claim 2, wherein The step of generating a set of pulmonary respiratory status assessment levels corresponding to the immersive training information provided by the patient to be processed based on the pulmonary respiratory status assessment levels of each of the real-time monitoring element information comprises: Loading the lung respiratory state assessment level of each of the real-time monitoring element information into the respiratory training debugging network to obtain the target lung respiratory state assessment level corresponding to each of the real-time monitoring element information; The target lung respiratory state assessment level corresponding to each of the real-time monitoring element information is used as the respiratory feature description element of the target respiratory feature corresponding to each of the real-time monitoring element information, and a lung respiratory state assessment level set corresponding to the immersive training information provided by the patient to be treated is generated. In the lung respiratory state assessment level set, each of the target respiratory features is distributed according to the relative position of the corresponding real-time monitoring element information in the area represented by the respiratory training scene description information.

6. The method according to claim 5, wherein The step of loading the lung respiratory state assessment level of each of the real-time monitoring element information into the respiratory training debugging network to obtain the target lung respiratory state assessment level corresponding to each of the real-time monitoring element information comprises: performing difference quantification processing on the pulmonary respiratory state assessment level of each of the real-time monitoring element information to obtain a first transitional pulmonary respiratory state assessment level of each of the real-time monitoring element information; the difference quantification processing is used to quantify the difference between the pulmonary respiratory state assessment levels corresponding to different real-time monitoring element information; Loading the first transitional lung respiratory state assessment level of each of the real-time monitoring element information into the respiratory training debugging network to obtain the second transitional lung respiratory state assessment level of each of the real-time monitoring element information; The first transition pulmonary respiratory state assessment level of each of the real-time monitoring element information is rounded to obtain the target pulmonary respiratory state assessment level corresponding to each of the real-time monitoring element information.

7. The method according to claim 1, wherein The step of determining the patient respiratory function assessment result of the patient to be processed providing immersive training information in the patient respiratory state information in combination with the patient respiratory state information includes: performing clustering processing on the patient's respiratory state information to obtain a clustering result; wherein a respiratory feature description element representing a respiratory feature of respiratory training is different from a respiratory feature description element representing a respiratory feature of non-respiratory training in the clustering result; Extracting one or more second reference breathing features from a target breathing feature region in the clustering result; the target breathing feature region is a breathing feature region of the region represented by the breathing training scene description information in the clustering result; determining a target breathing feature region for each second reference breathing feature in the clustering result; Determining a patient respiratory function assessment result of the patient to be processed providing immersive training information in the patient respiratory state information by combining the respiratory feature description elements of each respiratory feature in the target respiratory feature region of each second reference respiratory feature in the clustering result; The respiratory feature description element used to represent the respiratory feature of the respiratory training in the clustering result is a target respiratory feature description element; and the method of combining the respiratory feature description elements of each respiratory feature in the target respiratory feature area of ​​each second reference respiratory feature in the clustering result to determine the patient respiratory function assessment result of the immersive training information provided by the patient to be processed in the patient respiratory state information includes: Determining a breathing training description element corresponding to each second reference breathing feature, based on a weight of the breathing feature description element in the target breathing feature region of each second reference breathing feature in the clustering result, where the breathing training description element is a breathing training description segment matching element or a breathing training description segment non-matching element; The patient respiratory function assessment result of the immersive training information provided by the patient to be processed in the patient respiratory state information is determined based on the number of second reference respiratory features of the matching elements of the respiratory training description segment and the number of second reference respiratory features of the non-matching elements of the respiratory training description segment.

8. The method according to claim 1, wherein The determining, by combining at least two of the patient's respiratory function assessment result, the respiratory training control information, and the respiratory training similarity, a respiratory training personalized debugging result of the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data includes: If the patient's respiratory function assessment result is less than the patient's respiratory function assessment result threshold, the respiratory training control information is less than the abnormal respiratory training probability threshold, and the respiratory training similarity is less than the respiratory training similarity threshold, it is obtained that the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data is a personalized debugging result of respiratory training.

9. The method according to claim 1, wherein The determining, by combining at least two of the patient's respiratory function assessment result, the respiratory training control information, and the respiratory training similarity, a respiratory training personalized debugging result of the immersive training information provided by the patient to be processed in the first virtual reality pulmonary function data includes: Inputting the patient's respiratory function assessment results, respiratory training control information, and respiratory training similarity corresponding to the immersive training information provided by the patient to be processed into the respiratory training parsing network to obtain an abnormal probability result that the immersive training information provided by the patient to be processed is predicted to be respiratory training; If the abnormal probability result of the breathing training predicted by the immersive training information provided by the patient to be processed is less than the breathing training probability threshold, obtaining a personalized debugging result of the first virtual reality pulmonary function data of the immersive training information provided by the patient to be processed not being breathing training; If the abnormal probability result of the breathing training predicted by the immersive training information provided by the patient to be processed is not less than the breathing training probability threshold, obtaining a personalized debugging result of the breathing training in the first virtual reality pulmonary function data of the immersive training information provided by the patient to be processed; After determining the personalized debugging result of the breathing training of the patient in the first virtual reality pulmonary function data by combining the patient's respiratory function assessment result, the breathing training control information, and the breathing training similarity, the method further includes: If the personalized debugging result of the breathing training is that immersive training information is provided for the patient to be processed and the first virtual reality pulmonary function data is breathing training, the immersive training information provided for the patient to be processed in the first virtual reality pulmonary function data is warned.

10. A personalized debugging system for an intelligent breathing trainer, characterized in that: The invention comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 9.

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