Multi-source data fusion respiration management method and respiration management system

Abstract

The invention discloses a multi-source data fusion respiration management method and a respiration management system, and relates to the technical field of health management, the method comprises the following steps: analyzing information of a first login user through a medical care terminal, and determining an initial respiration function of the first login user; and exercise and nutrition plans of the user are obtained at the rehabilitative end and the nutritionist end. And analyzing the plan in combination with the initial respiratory function to predict the respiratory function of the user at the specific time. If the deviation between the actual respiratory function and the predicted value exceeds a set threshold value, an adjusting instruction is sent out, and a rehabilitation teacher and / or a nutritionist implements dynamic respiratory rehabilitation management according to the instruction. The technical problem that existing single data source evaluation of the respiratory function has limitation is solved, and the technical effect of improving the evaluation accuracy and management ability of the respiratory function is achieved.

Description

Technical Field

[0001] The present application relates to the field of health management technology, and in particular to a respiratory management method and a respiratory management system for multi-source data fusion. Background Art

[0002] Patients with interstitial lung disease often face problems such as dyspnea, decreased exercise capacity, reduced health-related quality of life, and fatigue, which seriously affect the patients' daily life and health status. Patients are in urgent need of effective respiratory rehabilitation management. Studies have shown that respiratory rehabilitation can significantly improve patients' exercise capacity, health status, and muscle strength. Therefore, multidimensional respiratory rehabilitation management is particularly important. Traditional respiratory management methods often rely on a single data source, which makes it difficult to fully reflect the patient's true condition, resulting in insufficient pertinence and effectiveness of management plans. Therefore, it is of great significance to develop a respiratory management method based on multi-source data fusion, which can more comprehensively and accurately evaluate the patient's respiratory function and provide a scientific basis for personalized rehabilitation management.

[0003] At the current stage, there is a technical problem that a single data source has limitations in evaluating respiratory function. Summary of the invention

[0004] This application provides a respiratory management method and respiratory management system with multi-source data fusion, which analyzes the information of the first logged-in user through the medical end to determine his / her initial respiratory function; and obtains the user's exercise and nutrition plan at the rehabilitation therapist end and the nutritionist end. The plan is analyzed in combination with the initial respiratory function to predict the user's respiratory function at a specific time. If the deviation between the actual respiratory function and the predicted value exceeds the set threshold, an adjustment instruction is issued, and the rehabilitation therapist and / or nutritionist implements dynamic respiratory rehabilitation management according to this instruction, thereby achieving the technical effect of improving the accuracy of respiratory function assessment and management capabilities.

[0005] The present application provides a respiratory management method for multi-source data fusion, including:

[0006] The medical end performs matching analysis on the first user information of the first logged-in user to obtain the first initial respiratory function of the first logged-in user; the rehabilitation therapist end and the nutritionist end sequentially traverse to obtain the first exercise plan and the first nutrition plan of the first logged-in user; the first exercise plan and the first nutrition plan are collaboratively analyzed in combination with the first initial respiratory function to obtain the first predicted respiratory function of the first logged-in user at the first real-time time; when the first deviation between the first real-time respiratory function and the first predicted respiratory function does not meet the predetermined deviation threshold value, a management adjustment instruction is issued, wherein the first real-time respiratory function refers to the actual respiratory function of the first logged-in user detected at the first real-time time; the rehabilitation therapist end and / or the nutritionist end performs dynamic respiratory rehabilitation management based on the management adjustment instruction.

[0007] The present application also provides a multi-source data fusion respiratory management system, including:

[0008] A first initial respiratory function acquisition module, the first initial respiratory function acquisition module is used to match and analyze the first user information of the first logged-in user through the medical end to obtain the first initial respiratory function of the first logged-in user; a plan acquisition module, the plan acquisition module is used to traverse the first exercise plan and the first nutrition plan of the first logged-in user in turn at the rehabilitation therapist end and the nutritionist end; a first predicted respiratory function acquisition module, the first predicted respiratory function acquisition module is used to perform a collaborative analysis of the first exercise plan and the first nutrition plan in combination with the first initial respiratory function to obtain the first predicted respiratory function of the first logged-in user at the first real time; a management adjustment instruction generation module, the management adjustment instruction generation module is used to issue a management adjustment instruction when the first deviation between the first real-time respiratory function and the first predicted respiratory function does not meet the predetermined deviation threshold value, wherein the first real-time respiratory function refers to the actual respiratory function of the first logged-in user detected at the first real time; a dynamic respiratory rehabilitation management module, the dynamic respiratory rehabilitation management module is used for the rehabilitation therapist end and / or the nutritionist end to perform dynamic respiratory rehabilitation management based on the management adjustment instruction.

[0009] The respiratory management method and respiratory management system proposed in this application with multi-source data fusion are first analyzed through the medical end to determine the initial respiratory function of the first logged-in user; the exercise and nutrition plan of the user is obtained at the rehabilitation therapist end and the nutritionist end. The plan is analyzed in combination with the initial respiratory function to predict the respiratory function of the user at a specific time. If the deviation between the actual respiratory function and the predicted value exceeds the set threshold, an adjustment instruction is issued, and the rehabilitation therapist and / or nutritionist implements dynamic respiratory rehabilitation management according to this instruction, thereby achieving the technical effect of improving the accuracy of respiratory function assessment and management capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flowchart of a respiratory management method for multi-source data fusion provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of the structure of a multi-source data fusion respiratory management system provided in an embodiment of the present application.

[0012] Explanation of reference numerals: first initial respiratory function acquisition module 10 , plan acquisition module 20 , first predicted respiratory function acquisition module 30 , management adjustment instruction generation module 40 , dynamic respiratory rehabilitation management module 50 . DETAILED DESCRIPTION

[0013] This application provides a respiratory management method that integrates multi-source data, thereby solving the technical problem that the existing single data source has limitations in evaluating respiratory function, and achieving the technical effect of improving the accuracy of respiratory function evaluation and management capabilities.

[0014] Example

[0015] like Figure 1 As shown, the present application provides a respiratory management method for multi-source data fusion, which includes:

[0016] Step S100, the medical end performs matching analysis on the first user information of the first logged-in user to obtain the first initial respiratory function of the first logged-in user. Specifically, the first logged-in user provides the medical end with a wide range of first user information including basic personal information, physiological data, medical history information, athletic ability and living habits information, etc. After receiving the information, the medical end performs data cleaning and preprocessing, and then uses advanced algorithms such as similarity calculation to first screen out a preliminary matching set in its embedded user respiratory archive database, and then screens the first respiratory archive based on factors such as specific disease type, age and gender for a second time to determine the most matching record, and then extract relevant data from the archive according to predetermined respiratory characteristics to generate first respiratory feature parameters, and finally substitute these parameters into the respiratory function evaluation function that comprehensively considers the weight distribution of each feature for calculation, thereby obtaining the first initial respiratory function represented by a quantitative value or a specific level, providing basic data support for subsequent rehabilitation management.

[0017] In a possible implementation, the first user information of the first logged-in user is matched and analyzed by the medical terminal to obtain the first initial respiratory function of the first logged-in user. Step S100 further includes step S110, matching the first user information with the user respiratory file embedded in the medical terminal to obtain the first respiratory file of the first logged-in user. Specifically, when the first logged-in user operates in the system, the first user information provided by the user is received by the medical terminal. The first user information covers many aspects, including basic information related to personal identity such as name, age, gender, and various physiological data reflecting physical condition, such as basic signs such as height and weight, and respiratory-related indicators obtained by professional equipment detection, such as vital capacity, peak expiratory flow rate, and the measurement of exercise capacity, including 6-minute walking test, cardiopulmonary exercise test and 2-minute stepping test, etc. At the same time, medical history information is also very critical, such as whether the patient has suffered from lung disease, cardiovascular disease, etc. in the past, as well as information on living habits, such as whether he smokes, how much he drinks, and how much he exercises daily. After the medical terminal integrates the first user information, it will be recorded in the embedded user respiratory file. The respiratory file is a huge database that stores respiratory data of many patients, and each record contains rich information. The medical staff uses a specific matching algorithm, such as an algorithm based on data feature similarity, to compare and analyze the first user information with the records in the file one by one, consider various dimensions of the information, and give higher weight to key information, such as respiratory-related test data, to find the one or several records that best match the first user information of the first logged-in user from many records, thereby obtaining the first respiratory file of the first logged-in user. The first respiratory file is a detailed health record tailored for the user, which contains historical data and feature information closely related to his respiratory condition, and provides an important basis for subsequent analysis and management.

[0018] Step S120, read the predetermined breathing characteristics, and match and generate the first breathing characteristic parameters in the first breathing file based on the predetermined breathing characteristics. Specifically, a series of predetermined breathing characteristics are pre-set in the system. The predetermined breathing characteristics are key indicators closely related to respiratory function determined through a large number of clinical studies and data analysis. For example, the predetermined breathing characteristics include the range of respiratory frequency in a quiet state, the normal tidal volume standard, the reasonable ratio of exhalation and inhalation time, the law of change of respiratory frequency during exercise, and the expected value of oxygen intake under different exercise intensities. After obtaining the first breathing file, the medical end performs precise matching in the file according to the predetermined breathing characteristics, extracts data information corresponding to each predetermined breathing characteristic from the file, and generates the first breathing characteristic parameters. For example, according to the breathing frequency data of the user measured multiple times in a quiet state recorded in the file, the average value is calculated as the first breathing characteristic parameter related to the breathing frequency; the actual value of the user's oxygen intake under a specific exercise intensity is analyzed from the exercise test data as the first breathing characteristic parameter related to the oxygen intake. The first breathing characteristic parameter presents the user's breathing condition in a more quantitative and analyzable form, laying a foundation for accurately evaluating its respiratory function.

[0019] Step S130, introducing a respiratory function evaluation function to evaluate and calculate the first respiratory characteristic parameter to obtain the first initial respiratory function. Specifically, the respiratory function evaluation function is a complex mathematical model that comprehensively considers multiple factors and is used to accurately evaluate the user's respiratory function. Its specific form is as follows: C = w1×F1+w2×F2+w3×F3+…+w n ×F n Wherein, C represents the respiratory function assessment result (i.e., the first initial respiratory function), w1, w2, w3, ..., w n It is the weight coefficient of each feature in the predetermined respiratory feature. The weight coefficient reflects the importance of different features on respiratory function. It is determined by statistical analysis of a large number of clinical case data, machine learning algorithm training, etc. For example, for the oxygen uptake change curve related feature (F1), if it is crucial to the judgment of respiratory function, it may be given a higher weight coefficient w1, such as 0.3; while for the inhalation time to exhalation time ratio feature (F2), which has a relatively small impact, the weight coefficient w2 may be 0.1.

[0020] F1, F2, F3, ..., F n, which is the first respiratory characteristic parameter generated earlier. The medical end substitutes the generated first respiratory characteristic parameter into the respiratory function evaluation function for calculation. For example, if the value of F1 (oxygen uptake change curve related parameter) is 0.8 and the value of F2 (inhalation time to exhalation time ratio parameter) is 1.2, according to the respective weight coefficients, it can be calculated that: C = 0.3 × 0.8 + 0.1 × 1.2 + ... (plus the product of other characteristic parameters and weight coefficients). After a series of complex calculations, a numerical value C that can quantitatively reflect the respiratory function of the first logged-in user is finally obtained. This value is the first initial respiratory function. This first initial respiratory function can be converted into a specific score, such as a full score of 100 points, and the user's respiratory function score is determined according to the value size; it can also be divided into different levels according to pre-set grade standards, such as dividing scores between 85-100 points into excellent grades, 70-84 points into good grades, etc., so as to intuitively understand the user's initial respiratory function status and provide a key basis for subsequent rehabilitation management.

[0021] In a possible implementation, a predetermined respiratory feature is read, and a first respiratory feature parameter is generated by matching in the first respiratory file based on the predetermined respiratory feature. Step S120 further includes step S121, forming a respiratory feature set. Specifically, forming a respiratory feature set is the basic link of the whole process. Researchers and medical experts have extensively consulted a large amount of medical literature, which covers multiple fields such as respiratory physiology, pathology, and clinical research. From basic respiratory physiology research, they obtain the normal range and change rules of various physiological indicators involved in the normal breathing process, such as respiratory rate, tidal volume, inhalation time, exhalation time, respiratory ratio, etc. From pathological research, they understand the possible changes in respiratory characteristics under different disease states, such as the respiratory rate may increase in lung infection and the exhalation time may be prolonged in emphysema. With reference to the observation and recording data of patients' respiratory characteristics in many clinical studies, the medical team uses advanced monitoring equipment to collect a large amount of actual respiratory data of patients. The equipment includes a spirometer for accurately measuring vital capacity, forced vital capacity and other indicators; a respiratory flow meter , which can monitor the peak expiratory flow rate, average expiratory flow rate, etc. in real time; and the blood gas analyzer, which can obtain blood gas related data such as arterial oxygen partial pressure, arterial carbon dioxide partial pressure, blood oxygen saturation, etc., and collect the patient's respiratory data in different states (such as quiet, after exercise, etc.) to fully understand the dynamic changes of respiratory function, and combine the theoretical knowledge obtained from the literature with the actual monitored data. After careful screening and classification, a series of characteristics closely related to respiratory function are determined, such as static respiratory characteristics (respiratory rate and tidal volume in a quiet state), dynamic respiratory characteristics (changes in respiratory rate and depth during exercise, changes in oxygen intake, etc.) and blood gas characteristics (changes in blood gas indicators in different states), etc., thereby forming a respiratory feature set. The respiratory feature set contains respiratory-related characteristics in multiple dimensions, which provides a rich reference basis for subsequent comparative analysis.

[0022] Step S122, setting up a comparative test group, the comparative test group includes control users and test users, wherein the test users refer to users suffering from interstitial lung disease. Specifically, when setting up a comparative test group, it is necessary to select control users and test users. For control users, the selection criteria are people who are in good health, have no obvious history of respiratory diseases and other major diseases that may affect respiratory function (such as cardiovascular diseases, nervous system diseases, etc.), have normal motor skills and good living habits (no smoking, moderate drinking or no drinking, regular exercise, etc.). Through extensive community recruitment, hospital physical examination center screening, etc., a certain number of qualified control users are collected. For example, in community health promotion activities, the test project is promoted to residents, and qualified residents are invited to voluntarily participate as control users; at the hospital physical examination center, a detailed health assessment is conducted on the people who come for the physical examination to screen out suitable control users, and the test users are clearly those suffering from interstitial lung disease. The test users were selected from the respiratory wards, outpatient clinics and related patient databases of the hospital. The patients were diagnosed with interstitial lung diseases by professional doctors, including different types of interstitial lung diseases such as idiopathic pulmonary fibrosis, sarcoidosis, and allergic pneumonia. In the selection process, we tried to cover patients with different disease severity (mild, moderate, severe), different stages of the disease (early, middle, late), and different genders and age groups to ensure that the test results are broadly representative. For example, from the respiratory wards of multiple hospitals, patients with interstitial lung diseases at different stages of the disease were selected according to a predetermined ratio, and their detailed medical records were recorded, including onset time, symptoms, treatment history, changes in exercise ability, etc., to provide comprehensive data support for subsequent analysis.

[0023] Step S123, respectively obtain the control breathing characteristic parameters of the control user and the test breathing characteristic parameters of the test user in combination with the breathing characteristic set. Specifically, for the control users, they are taken to a special breathing detection laboratory, where they are subjected to a comprehensive respiratory function test using high-precision breathing detection equipment. First, the control users are allowed to breathe naturally in a quiet state, and static breathing characteristic parameters such as breathing rate, tidal volume, inhalation time, and exhalation time are continuously monitored through sensors connected to the mouth and nose, and stable data is recorded over a period of time. The control users are allowed to perform exercise tests on a treadmill with gradually increasing loads, and dynamic breathing characteristic parameters such as breathing rate, oxygen intake, and carbon dioxide emissions are monitored in real time during exercise. At the same time, arterial blood samples are collected regularly through a blood gas analyzer to detect blood gas characteristic parameters such as arterial oxygen partial pressure, arterial carbon dioxide partial pressure, and blood oxygen saturation. Multiple breathing characteristic parameters obtained under different conditions are sorted and recorded to form a control breathing data set for the control users. For the test users, the test is also carried out in a professional respiratory testing environment. Since the respiratory function of patients with interstitial lung disease may be poor, the patient's physical condition will be paid closer attention during the test, and the test plan will be adjusted according to the patient's actual tolerance. In a quiet state, the respiratory rate, tidal volume and other parameters are carefully measured, and attention is paid to observe whether the patient's breathing rhythm and depth are abnormal. When conducting exercise tests, a milder exercise method is adopted, such as slow walking or low-intensity cycling, and the changes in respiratory parameters and blood gas indicators are closely monitored. For some patients with more serious conditions who cannot undergo exercise testing, the focus is on obtaining their respiratory characteristic parameters in a quiet state, and combined with indirect information such as chest imaging examination results (such as lung lesions shown by high-resolution CT), their respiratory function is comprehensively evaluated, so as to obtain the test user's experimental respiratory characteristic parameter set.

[0024] Step S124, compare and analyze the control respiratory characteristic parameters and the test respiratory characteristic parameters to obtain a parameter difference ratio set. Specifically, the control respiratory characteristic parameter set of the control user and the test respiratory characteristic parameter set of the test user are compared and analyzed, and for each respiratory characteristic parameter, the difference ratio between the two groups of users is calculated. For example, for the feature of respiratory frequency, the difference between the average respiratory frequency of the test user and the average respiratory frequency of the control user is calculated, and then divided by the average respiratory frequency of the control user to obtain the difference ratio of respiratory frequency. Similarly, other respiratory characteristic parameters such as tidal volume, oxygen intake, and blood gas index are also calculated in a similar way. Through calculation, a series of parameter difference ratios are obtained, and these ratios constitute a parameter difference ratio set. The parameter difference ratio set can intuitively reflect the degree of difference in respiratory characteristic parameters between the test users with interstitial lung disease and the healthy control users, and provides a data basis for further screening of key features. For example, if the difference ratio of a certain respiratory characteristic parameter is large, it means that the parameter has a significant change in patients with interstitial lung disease compared with healthy people, which is of great significance for the diagnosis of the disease and the evaluation of respiratory function.

[0025] Step S125, screening the parameters in the parameter difference ratio set whose difference ratio is not within the predetermined ratio threshold value, and reverse matching its characteristics to form the predetermined respiratory characteristics. Specifically, each difference ratio in the parameter difference ratio set is compared with the predetermined ratio threshold value. The predetermined ratio threshold value is a reasonable range determined based on a large amount of previous research and clinical experience, and is used to determine whether a change in a certain respiratory characteristic parameter under a disease state is significant. For example, the difference ratio threshold value for respiratory frequency may be set to ±20%. If the respiratory frequency difference ratio between a certain experimental user group and a control user group exceeds this threshold value, it is considered that the respiratory frequency has changed significantly in patients with interstitial lung disease. Screening out parameters whose difference ratio is not within the predetermined ratio threshold value is a parameter that shows obvious abnormalities when suffering from interstitial lung disease. Respiratory characteristic parameters, and then, according to these abnormal parameters, reversely match the corresponding original features in the respiratory feature set. For example, if the difference ratio of oxygen uptake is screened out to be abnormal, reversely find the relevant feature description of oxygen uptake in the respiratory feature set, including its measurement method, normal range, significance in the respiratory physiological process, etc., and combine the screened respiratory features with significant differences together to form a predetermined respiratory feature. The respiratory feature combination includes exercise capacity features. The predetermined respiratory features will serve as an important basis for subsequent evaluation of the respiratory function of patients with interstitial lung disease and formulation of rehabilitation management plans, because they can accurately reflect the characteristics of the impact of the disease on respiratory function.

[0026] In a possible implementation, a respiratory function evaluation function is introduced to evaluate and calculate the first respiratory characteristic parameter to obtain the first initial respiratory function. Step S130 further includes step S131, and the respiratory function evaluation function includes the weight distribution of each feature in the predetermined respiratory feature. Specifically, in order to determine the weight distribution of the predetermined respiratory feature in the respiratory function evaluation function, it is necessary to collect data of different types of patients from multiple sources, including hospital medical records, clinical research and follow-up data, and check, supplement, correct errors and classify and annotate in a unified format during preprocessing. Then, feature engineering is performed to analyze and process the respiratory features and select features that are important for respiratory function evaluation. Then, a weight distribution model is constructed using algorithms such as multivariate linear regression, decision trees or neural networks, and a large amount of data is used for training so that the model adjusts the weight according to the data rules to minimize the error between the prediction and the actual result, and then an independent test set is used for evaluation. If it is not ideal, it is optimized, the hyperparameters are adjusted, the amount of data is increased or the algorithm is converted, and the training evaluation is repeated until the performance is satisfactory. The final weight is the weight distribution in the respiratory function evaluation function.

[0027] Step S200, traverse the first exercise plan and the first nutrition plan of the first logged-in user in turn on the rehabilitation therapist side and the nutritionist side. Specifically, after receiving the information of the first logged-in user, the rehabilitation therapist side first analyzes the basic information and the first initial respiratory function, and formulates the first exercise plan with reference to the clinical exercise rehabilitation guidelines and research results. For example, in the morning, arrange low-intensity exercises such as 15-20 minutes of breathing exercises, and the exercise breathing impact coefficient is marked low; in the afternoon, if conditions permit, arrange 20-30 minutes of light aerobic exercise such as slow cycling indoors, and the exercise breathing impact coefficient is marked medium. The process will be dynamically adjusted according to the user's physiological indicators. After obtaining the information, the nutritionist side interprets the medical history, health status and initial respiratory function, and formulates the first nutrition plan based on nutritional knowledge, experience and research literature. Breakfast provides foods rich in high-quality protein, moderate carbohydrates and abundant vitamins and minerals, and is eaten between 7 and 9 o'clock. The nutritional respiratory impact coefficient is marked medium to low; lunch focuses on nutritional balance and density, contains moderate amounts of high-quality protein, sufficient fruits and vegetables, and is eaten between 12 and 14 o'clock, with a medium coefficient; dinner is light, easy to digest and nutritious, and is eaten between 18 and 20 o'clock. The coefficient is marked medium to low, and will be optimized based on user conditions.

[0028] In a possible implementation, the first exercise plan and the first nutrition plan of the first logged-in user are traversed in turn at the rehabilitation therapist end and the nutritionist end, and step S200 further includes step S210, in which the rehabilitation therapist end performs rehabilitation analysis on the first user information and the first initial respiratory function of the first logged-in user to obtain the first exercise plan, and the first exercise plan includes an exercise plan with an exercise breathing influence coefficient identifier, and the exercise plan refers to multiple exercise plans corresponding to multiple exercise times. Specifically, after receiving the first user information and the first initial respiratory function of the first logged-in user, the rehabilitation therapist end analyzes factors such as age, gender, height and weight in the user information, studies respiratory function-related indicators to accurately grasp the respiratory function status, and then formulates the exercise plan in the first exercise plan based on professional knowledge and experience and extensive reference to clinical exercise rehabilitation guidelines and research results. For example, in the morning, arrange 10-15 minutes of low-intensity exercise combining stretching and breathing training, and the exercise breathing impact coefficient is low; in the morning, if the respiratory function allows, set 20-30 minutes of light aerobic exercise of slow indoor walking, with a medium to low coefficient; in the afternoon, for those with a certain foundation of respiratory function, design 30-45 minutes of medium-intensity exercise combining aerobics and light strength training, pay close attention to the user's physiological indicators during the process and adjust according to feedback; in the evening, recommend 20-30 minutes of low-intensity exercise of yoga or stretching and relaxation exercises. During the whole process, the rehabilitation therapist communicates closely with the user, regularly re-evaluates the respiratory function and optimizes the exercise plan to improve the user's respiratory function and exercise endurance, and promote the improvement of health status.

[0029] In a possible implementation, the first exercise plan and the first nutrition plan of the first logged-in user are traversed in turn on the rehabilitation therapist side and the nutritionist side, and step S200 further includes step S220, the nutritionist side performs a nutritional analysis on the first user information and the first initial respiratory function of the first logged-in user to obtain the first nutrition plan, the first nutrition plan includes a nutrition plan with a nutrition respiratory influence coefficient mark, and the nutrition plan refers to multiple nutrition plans corresponding to multiple eating times. Specifically, after the nutritionist side obtains the first user information and the first initial respiratory function of the first logged-in user, it analyzes factors such as age, gender, height and weight in the user information, analyzes the relationship between respiratory function and nutrition, and constructs the nutrition plan in the first nutrition plan by relying on professional knowledge, experience and research literature. For example, breakfast is at 7-9 o'clock, providing food rich in high-quality protein, moderate carbohydrates, and rich vitamins and minerals, and the nutrition respiratory influence coefficient mark is medium and low; lunch is at 12-14 o'clock, containing a variety of ingredients, the staple food is whole grains, with high-quality protein sources and a large number of vegetables, and the coefficient is medium; dinner is at 18-20 o'clock, with light protein, moderate vegetables and carbohydrates, and the coefficient is medium and low. During implementation, we communicate closely with users, make adjustments based on feedback, regularly evaluate nutritional status and respiratory function, and optimize the plan to improve respiratory function and enhance health levels.

[0030] Step S300, in combination with the first initial respiratory function, the first exercise plan and the first nutrition plan are collaboratively analyzed to obtain the first predicted respiratory function of the first logged-in user at the first real-time. Specifically, when the first exercise plan and the first nutrition plan are collaboratively analyzed in combination with the first initial respiratory function to obtain the first predicted respiratory function of the first logged-in user at the first real-time, it is necessary to deeply analyze the exercise type, intensity, time, frequency and corresponding respiratory influence coefficient in the exercise plan, and study the various nutrient components, meal time and nutritional respiratory influence coefficient in the nutrition plan. Establish a complex mathematical model, such as an equation based on the first initial respiratory function, considering the dynamic influence of exercise and nutritional factors, substitute the exercise and nutrition plan data, take into account the current and historical influences, and individual differences during calculation, so as to obtain the first predicted respiratory function that considers the comprehensive effects of multiple factors and the gradual change process, which provides an important basis for subsequent judgment and adjustment.

[0031] Step S400, when the first deviation between the first real-time respiratory function and the first predicted respiratory function does not meet the predetermined deviation threshold value, a management adjustment instruction is issued, wherein the first real-time respiratory function refers to the actual respiratory function of the first logged-in user at the first real-time time obtained by detection. Specifically, in the respiratory management scheme, the first real-time respiratory function of the first logged-in user at the first real-time time is obtained by a professional respiratory detection device, which covers parameters such as respiratory rate, vital capacity, forced vital capacity, and peak expiratory flow rate. The first deviation is calculated in combination with the first predicted respiratory function obtained previously, and an absolute or relative deviation calculation method can be used. The first deviation is compared with the predetermined deviation threshold value determined by a large amount of research and experience and considering multiple factors. When the deviation does not meet the requirements, the system issues a management adjustment instruction containing detailed deviation information. If the respiratory function is lower than expected, the rehabilitation therapist may reduce the exercise intensity, and the nutritionist may increase the nutritional intake; if it is higher than expected, the rehabilitation therapist may increase the exercise intensity, and the nutritionist may optimize the nutritional plan. The system continuously records and analyzes data to form a historical record to optimize the threshold value and provide personalized support to achieve the goal of improving respiratory function and quality of life.

[0032] In a possible implementation, when a first deviation between the first real-time respiratory function and the first predicted respiratory function does not meet a predetermined deviation threshold value, a management adjustment instruction is issued, wherein the first real-time respiratory function refers to the actual respiratory function of the first logged-in user detected at the first real-time time, and step S400 further includes step S410, obtaining a first respiratory detection record of the first logged-in user at the first real-time time. Specifically, the first breathing detection record of the first logged-in user at the first real-time time is obtained. With the help of the breathing detection equipment, first, a breathing rate sensor is used, which adopts a non-invasive design, such as a strap-type sensor on the chest or abdomen, using bioelectrical impedance or induction technology to monitor the ups and downs of the user's chest or abdomen in real time, so as to accurately record the time point of each breath, and then calculate the breathing rate. The sensor will continue to collect data at a certain sampling frequency (for example, collecting data 10 times per second) to ensure that it can capture subtle changes in the user's breathing rate. The spirometer requires the user to follow the standard operating procedures, first try to take a deep breath, and then try to exhale quickly and completely through the mouthpiece or mask connected to the spirometer. The spirometer uses a gas flow sensor and a pressure sensor to accurately measure the volume and flow rate of the exhaled gas, and record the gas flow curve during the exhalation process. From the curve Not only can the vital capacity value (i.e. the maximum amount of gas exhaled when trying to exhale) be obtained, but also the time process of exhalation can be analyzed, such as the duration of exhalation, the time when the peak expiratory flow rate occurs, and other information. The data are of great significance for the comprehensive evaluation of respiratory function. For some more in-depth respiratory function tests, gas analysis equipment will be used. For example, by collecting gas samples exhaled by the user and analyzing the concentrations of oxygen and carbon dioxide therein, the equipment uses electrochemical sensors or infrared spectroscopy technology to accurately measure the partial pressures of oxygen and carbon dioxide in the gas. When the user breathes quietly or performs specific breathing tasks (such as deep breathing, rapid breathing, etc.), the changes in gas composition are continuously monitored, thereby obtaining information about gas exchange efficiency, such as arterial oxygen partial pressure (PaO2), arterial carbon dioxide partial pressure (PaCO2), and end-tidal carbon dioxide partial pressure (PetCO2) and other indicators.These data are closely related to respiratory function and can reflect whether the ventilation and gas exchange functions of the lungs are normal. In order to obtain more comprehensive respiratory mechanics information, a respiratory mechanics monitor is used to measure the pressure changes (such as oral pressure, chest pressure, etc.) and gas flow changes during breathing, and calculate respiratory mechanics parameters such as respiratory resistance and compliance. For example, when the user breathes normally and breathes hard, the monitor can display the size of the respiratory resistance in real time to reflect whether there is airway obstruction; at the same time, by analyzing the relationship between chest pressure and lung volume, the compliance of the lungs, that is, the extensibility of lung tissue under pressure, is evaluated. This is of great value for judging the type and severity of lung diseases. The equipment continuously collects data in the first real-time time, integrates information such as respiratory rate, vital capacity, gas exchange-related indicators, and respiratory mechanics parameters, and forms the first respiratory detection record, which records a data set containing multi-dimensional respiratory information, providing a rich data foundation for the subsequent accurate analysis of the first real-time respiratory function.

[0033] Step S420, analyze the first respiratory detection record to obtain the first real-time respiratory function. Specifically, after obtaining the first respiratory detection record, the data in the record will be deeply analyzed. First, for the respiratory frequency data, data cleaning and preprocessing will be performed to remove possible abnormal values ​​(such as erroneous data points caused by short-term sensor interference or inadvertent user movements), calculate the average respiratory frequency within the first real-time time, and analyze the stability of the respiratory frequency. For example, by calculating the standard deviation or coefficient of variation of the respiratory frequency to evaluate whether the respiratory rhythm is regular. Under normal circumstances, the respiratory frequency should be relatively stable with a small standard deviation. If the standard deviation is large, it may indicate the presence of respiratory rhythm disorders, which may be related to a variety of factors, such as the nervous system. Abnormal regulation, unstable cardiopulmonary function, etc. For vital capacity data, the analysis software will accurately identify the peak value of vital capacity based on the expiratory curve recorded by the spirometer, and compare it with the normal reference value. The normal range of vital capacity varies among people of different ages, genders, heights and weights. By comparing the vital capacity measurement value of the first logged-in user with the corresponding normal range, it is possible to preliminarily determine whether the user's pulmonary ventilation function is normal. At the same time, the shape of the expiratory curve is analyzed, such as whether there is an expiratory plateau (reflecting the functional status of the small airways), the rate of decrease of expiratory flow and other characteristics. If the expiratory curve shape is abnormal, such as shortening or disappearance of the expiratory plateau, it indicates that the small airways are There are problems such as blockage or stenosis. In terms of gas exchange-related indicators, the analysis software will calculate the oxygen intake and carbon dioxide discharge based on the data collected by the gas analysis equipment. By comparing the concentration difference of oxygen and carbon dioxide in the inhaled gas and the exhaled gas, and combining parameters such as respiratory rate and tidal volume (the amount of gas inhaled or exhaled during each breath, which can be calculated using spirometer data), the gas exchange efficiency of the lungs is evaluated. For example, under normal circumstances, the arterial oxygen partial pressure (PaO2) should be maintained within a certain range (generally 80-100 mmHg). If PaO2 is lower than the normal range, it may indicate insufficient oxygen intake in the lungs and the presence of ventilation / blood. The partial pressure of carbon dioxide in arterial blood (PaCO2) should usually be maintained at a relatively stable level (35-45 mmHg). An increase in PaCO2 may indicate insufficient ventilation, resulting in obstruction of carbon dioxide excretion. For respiratory mechanics parameters, such as respiratory resistance and compliance, the analysis software will calculate and analyze the data collected by the respiratory mechanics monitor. An increase in respiratory resistance may be due to airway stenosis (such as bronchospasm, increased airway secretions, etc.) or decreased elasticity of lung tissue (such as diseases such as emphysema). A decrease in compliance may indicate that diseases such as pulmonary fibrosis cause the lung tissue to harden and become difficult to expand and contract.By analyzing these respiratory mechanics parameters, we can further understand the structure and functional status of the lungs, providing an important basis for evaluating the first real-time respiratory function. Combining the above analysis results of various data such as respiratory rate, vital capacity, gas exchange indicators and respiratory mechanics parameters, the system finally obtains the first real-time respiratory function of the first logged-in user at the first real-time time. The first real-time respiratory function is a comprehensive evaluation result that can comprehensively reflect the user's respiratory function status at that moment, including ventilation function, gas exchange efficiency, respiratory mechanics characteristics and other aspects, which provides key data support for subsequent comparative analysis with the first predicted respiratory function and the implementation of the entire respiratory management plan.

[0034] In a possible implementation, when the first deviation between the first real-time respiratory function and the first predicted respiratory function does not meet the predetermined deviation threshold, a management adjustment instruction is issued, wherein the first real-time respiratory function refers to the actual respiratory function of the first logged-in user detected at the first real-time time, and step S400 further includes step S430, which also includes adding the first respiratory detection record to the first respiratory file based on the management adjustment instruction. Specifically, when operating based on the management adjustment instruction, it is very important to add the first respiratory detection record to the first respiratory file. After receiving the instruction, the system first uses the information such as the detection time, device identification and user identification code in the instruction to associate the detection record with the file. Then pre-process and format the record, clean the original data, correct errors, unify the format, and then merge it with the existing data in the file, and insert it in order by time and type. Automatically update statistical information and analysis results during fusion, mark respiratory function change points and annotate. Adding records is helpful for long-term tracking and retrospective analysis, and can mine laws and evaluate the effectiveness of the program to optimize management. Finally, the system backs up and stores management files to ensure data security and integrity, set strict access rights to protect privacy, realize effective data management and utilization, and support personalized respiratory rehabilitation management.

[0035] Step S500, the rehabilitation therapist side and / or the nutritionist side performs dynamic respiratory rehabilitation management based on the management adjustment instruction. Specifically, after the rehabilitation therapist side and / or the nutritionist side receives the management adjustment instruction, the rehabilitation therapist side will interpret the instruction information, analyze the exercise plan problems, such as whether the exercise intensity, type, and frequency are appropriate, and evaluate according to the test results of the exercise ability. These tests include a 6-minute walking test, a cardiopulmonary exercise test, and a 2-minute stepping test. The rehabilitation therapist will adjust the exercise plan accordingly, such as replacing the type of exercise, reducing the intensity, and adjusting the frequency. At the same time, pay close attention to the user's exercise performance, evaluate the effect through communication, observation, and obtaining test data, and continuously optimize. The nutritionist side also analyzes the instructions, pays attention to the relationship between nutrition and respiratory function, optimizes the nutrition plan, adjusts the nutrient combination and the diet schedule, such as increasing protein, supplementing vitamins and minerals, and adjusting the diet according to the exercise time. The two parties will also work closely together to continuously track the user's situation and dynamically adjust the plan at any time according to changes to achieve the best respiratory rehabilitation effect and improve the quality of life.

[0036] In the above, refer to Figure 1 A respiratory management method according to a multi-source data fusion embodiment of the present invention is described in detail. Figure 2 A respiratory management system based on multi-source data fusion according to an embodiment of the present invention is described.

[0037] A respiratory management system with multi-source data fusion according to an embodiment of the present invention is used to solve the technical problem that the existing single data source has limitations in evaluating respiratory function, and achieves the technical effect of improving the evaluation accuracy and management ability of respiratory function. A respiratory management system with multi-source data fusion includes: a first initial respiratory function acquisition module 10, a plan acquisition module 20, a first predicted respiratory function acquisition module 30, a management adjustment instruction generation module 40, and a dynamic respiratory rehabilitation management module 50.

[0038] The first initial respiratory function acquisition module 10 is used to perform matching analysis on the first user information of the first logged-in user through the medical terminal to obtain the first initial respiratory function of the first logged-in user.

[0039] The plan acquisition module 20 is used to traverse the first exercise plan and the first nutrition plan of the first logged-in user in turn on the rehabilitation therapist side and the nutritionist side.

[0040] The first predicted respiratory function acquisition module 30 is used to perform a collaborative analysis on the first exercise plan and the first nutrition plan in combination with the first initial respiratory function to obtain a first predicted respiratory function of the first logged-in user at a first real time.

[0041] The management adjustment instruction generation module 40 is used to issue a management adjustment instruction when a first deviation between the first real-time respiratory function and the first predicted respiratory function does not meet a predetermined deviation threshold value, wherein the first real-time respiratory function refers to the actual respiratory function of the first logged-in user detected at the first real-time time.

[0042] The dynamic respiratory rehabilitation management module 50 is used by the rehabilitation therapist and / or the nutritionist to perform dynamic respiratory rehabilitation management based on the management adjustment instruction.

[0043] The specific configuration of the first initial respiratory function acquisition module 10 will be described in detail below. As described above, the first user information of the first logged-in user is matched and analyzed by the medical terminal to obtain the first initial respiratory function of the first logged-in user. The first initial respiratory function acquisition module 10 further includes: a first respiratory file acquisition unit, the first respiratory file acquisition unit is used to match the first user information in the user respiratory file embedded in the medical terminal to obtain the first respiratory file of the first logged-in user; a first respiratory feature parameter generation unit, the first respiratory feature parameter generation unit is used to read a predetermined respiratory feature, and match and generate a first respiratory feature parameter in the first respiratory file based on the predetermined respiratory feature; an evaluation calculation unit, the evaluation calculation unit is used to introduce a respiratory function evaluation function to evaluate and calculate the first respiratory feature parameter to obtain the first initial respiratory function.

[0044] Wherein, a predetermined breathing feature is read, and a first breathing feature parameter is generated by matching in the first breathing file based on the predetermined breathing feature, and the first breathing feature parameter generating unit further includes: a breathing feature set component subunit, the breathing feature set component subunit is used to form a breathing feature set; a test group comparison subunit, the test group comparison subunit is used to set a comparison test group, the comparison test group includes a control user and a test user, wherein the test user refers to a user with interstitial lung disease; a breathing feature parameter acquisition subunit, the breathing feature parameter acquisition subunit is used to respectively acquire the control breathing feature parameters of the control user and the test breathing feature parameters of the test user in combination with the breathing feature set; a parameter difference ratio set acquisition subunit, the parameter difference ratio set acquisition subunit is used to compare and analyze the control breathing feature parameters with the test breathing feature parameters to obtain a parameter difference ratio set; a predetermined breathing feature composition subunit, the predetermined breathing feature composition subunit is used to screen parameters in the parameter difference ratio set whose difference ratio is not within a predetermined ratio threshold value, and reversely match its characteristics to form the predetermined breathing feature.

[0045] Among them, a respiratory function evaluation function is introduced to evaluate and calculate the first respiratory characteristic parameter to obtain the first initial respiratory function, and the evaluation calculation unit further includes: a respiratory function evaluation function composition subunit, and the respiratory function evaluation function composition subunit is used for the respiratory function evaluation function to include the weight allocation of each feature in the predetermined respiratory feature.

[0046] The specific configuration of the plan acquisition module 20 will be described in detail below. As described above, the first exercise plan and the first nutrition plan of the first logged-in user are traversed in turn at the rehabilitation therapist end and the nutritionist end, and the plan acquisition module 20 further includes: a first exercise plan acquisition unit, the first exercise plan acquisition unit is used for the rehabilitation therapist end to perform rehabilitation analysis on the first user information and the first initial respiratory function of the first logged-in user to obtain the first exercise plan, the first exercise plan includes an exercise plan with an exercise breathing influence coefficient identifier, and the exercise plan refers to multiple exercise plans corresponding to multiple exercise times.

[0047] Among them, the plan acquisition module further includes: a first nutrition plan acquisition unit, which is used by the nutritionist to perform nutritional analysis on the first user information of the first logged-in user and the first initial respiratory function to obtain the first nutrition plan, and the first nutrition plan includes a nutrition plan with a nutrition-respiratory influence coefficient identifier, and the nutrition plan refers to multiple nutrition plans corresponding to multiple eating times.

[0048] The specific configuration of the management adjustment instruction generation module 40 will be described in detail below. As described above, when the first deviation between the first real-time respiratory function and the first predicted respiratory function does not meet the predetermined deviation threshold value, a management adjustment instruction is issued, wherein the first real-time respiratory function refers to the actual respiratory function of the first logged-in user at the first real-time time obtained by detection, and the management adjustment instruction generation module 40 further includes: a first respiratory detection record acquisition unit, the first respiratory detection record acquisition unit is used to obtain the first respiratory detection record of the first logged-in user at the first real-time time; a first real-time respiratory function analysis unit, the first real-time respiratory function analysis unit is used to analyze the first respiratory detection record to obtain the first real-time respiratory function.

[0049] The management adjustment instruction generating module 40 further comprises: a first breathing file adding unit, wherein the first breathing file adding unit is further configured to add the first breathing detection record to the first breathing file based on the management adjustment instruction.

[0050] In summary, the embodiments of the present application have at least the following technical effects:

[0051] The embodiment of the present application adopts the method of analyzing the information of the first logged-in user through the medical side to determine his / her initial respiratory function; obtaining the exercise and nutrition plan of the user at the rehabilitation therapist side and the nutritionist side. The plan is analyzed in combination with the initial respiratory function to predict the respiratory function of the user at a specific time. If the deviation between the actual respiratory function and the predicted value exceeds the set threshold, an adjustment instruction is issued, and the rehabilitation therapist and / or nutritionist implements dynamic respiratory rehabilitation management according to this instruction, thereby achieving the technical effect of improving the accuracy of respiratory function assessment and management capabilities.

[0052] This specification and drawings are merely exemplary illustrations of the present application, and various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. These modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, and the present application intends to include these modifications and variations.

Claims

1. A respiratory management method based on multi-source data fusion, characterized in that: The multi-source data fusion respiratory management method is applied to a multi-source data fusion respiratory management system, the multi-source data fusion respiratory management system is communicatively connected with a medical care end, a rehabilitation therapist end, and a nutritionist end, and the multi-source data fusion respiratory management method includes: Performing matching analysis on the first user information of the first logged-in user by the medical terminal to obtain the first initial respiratory function of the first logged-in user; The first exercise plan and the first nutrition plan of the first logged-in user are obtained by traversing the first rehabilitation therapist end and the first nutrition therapist end in turn; performing a collaborative analysis of the first exercise plan and the first nutrition plan in combination with the first initial respiratory function to obtain a first predicted respiratory function of the first logged-in user at a first real-time time; When a first deviation between the first real-time respiratory function and the first predicted respiratory function does not meet a predetermined deviation threshold, issuing a management adjustment instruction, wherein the first real-time respiratory function refers to the actual respiratory function of the first logged-in user detected at the first real-time time; The rehabilitation therapist and / or the nutritionist performs dynamic respiratory rehabilitation management based on the management adjustment instruction.

2. A respiratory management method based on multi-source data fusion according to claim 1, characterized in that: include: Matching the first user information with the user breathing profile embedded in the medical terminal to obtain the first breathing profile of the first logged-in user; Reading a predetermined breathing feature, and matching and generating a first breathing feature parameter in the first breathing file based on the predetermined breathing feature; A respiratory function evaluation function is introduced to evaluate and calculate the first respiratory characteristic parameter to obtain the first initial respiratory function.

3. A respiratory management method based on multi-source data fusion according to claim 2, characterized in that: include: Build a respiratory feature set; Setting a comparative test group, the comparative test group includes a control user and a test user, wherein the test user refers to a user suffering from interstitial lung disease; respectively acquiring a control breathing characteristic parameter of the control user and a test breathing characteristic parameter of the test user in combination with the breathing characteristic set; Comparing and analyzing the control breathing characteristic parameters and the test breathing characteristic parameters to obtain a parameter difference ratio set; Parameters whose difference ratios are not within a predetermined ratio threshold value in the parameter difference ratio set are screened, and their features are reversely matched to form the predetermined breathing feature.

4. A respiratory management method based on multi-source data fusion according to claim 3, characterized in that: The respiratory function evaluation function includes weight distribution of each feature in the predetermined respiratory feature.

5. The respiratory management method of multi-source data fusion according to claim 1, characterized in that: The rehabilitation therapist performs rehabilitation analysis on the first user information and the first initial respiratory function of the first logged-in user to obtain the first exercise plan, wherein the first exercise plan includes an exercise plan with an exercise breathing influence coefficient identifier, and the exercise plan refers to multiple exercise plans corresponding to multiple exercise times.

6. A respiratory management method based on multi-source data fusion according to claim 5, characterized in that: The nutritionist side performs a nutritional analysis on the first user information of the first logged-in user and the first initial respiratory function to obtain the first nutritional plan, wherein the first nutritional plan includes a nutritional plan with a nutritional respiratory influence coefficient identifier, and the nutritional plan refers to multiple nutritional plans corresponding to multiple eating times.

7. A respiratory management method based on multi-source data fusion according to claim 6, characterized in that: include: Obtaining a first breathing detection record of the first logged-in user at the first real time; The first breathing detection record is analyzed to obtain the first real-time breathing function.

8. The respiratory management method of multi-source data fusion according to claim 2, characterized in that: The method also includes adding the first breathing detection record to the first breathing profile based on the management adjustment instruction.

9. A respiratory management system with multi-source data fusion, characterized in that: The system is used to implement a respiratory management method of multi-source data fusion according to any one of claims 1 to 8, and the system comprises: A first initial respiratory function acquisition module, the first initial respiratory function acquisition module is used to match and analyze the first user information of the first logged-in user through the medical terminal to obtain the first initial respiratory function of the first logged-in user; A plan acquisition module, the plan acquisition module is used to sequentially traverse the first exercise plan and the first nutrition plan of the first logged-in user on the rehabilitation therapist end and the nutritionist end; a first predicted respiratory function acquisition module, the first predicted respiratory function acquisition module being used to collaboratively analyze the first exercise plan and the first nutrition plan in combination with the first initial respiratory function to obtain a first predicted respiratory function of the first logged-in user at a first real time; a management adjustment instruction generation module, the management adjustment instruction generation module being used to issue a management adjustment instruction when a first deviation between a first real-time respiratory function and the first predicted respiratory function does not meet a predetermined deviation threshold, wherein the first real-time respiratory function refers to the actual respiratory function of the first logged-in user detected at the first real-time time; A dynamic respiratory rehabilitation management module is used by the rehabilitation therapist and / or the nutritionist to perform dynamic respiratory rehabilitation management based on the management adjustment instruction.

Images