A temperature-controlled intelligent adjustment method and system for a humidification therapy device

Abstract

The present application provides a method and system for intelligent adjustment of a humidification therapy device based on temperature control, which relates to the field of instrument adjustment technology. The method includes: collecting air characteristic parameters and user characteristic information in the current environment, then obtaining a heating parameter range for heating the air, then randomly obtaining a first heating for humidification prediction, obtaining predicted humidity parameters, obtaining control delay parameters and control accuracy parameters, calculating verification accuracy, and then calculating a first fitness, optimizing and discriminating the heating parameters within the heating parameter range according to the first fitness to obtain the optimal heating parameters, and performing temperature control adjustment. The present application mainly solves the problem that the existing technology cannot achieve precise temperature control, resulting in large temperature fluctuations during treatment, affecting treatment, and being unable to perform intelligent adjustment according to the actual needs of the user and environmental conditions. It improves the performance and adaptability of the humidification therapy device, and improves the treatment effect and user satisfaction.

Description

Technical Field

[0001] The present application relates to the field of instrument adjustment technology, and in particular to an intelligent adjustment method and system for a humidification therapy device based on temperature control. Background Art

[0002] With the advancement of sensor and computer technology, precise temperature control has become possible. By monitoring the output catheter temperature and ambient temperature in real time and feeding this data back to the system computer, the system computer can determine whether the catheter needs to be heated based on the temperature difference, thereby preventing condensation caused by large temperature differences. Improper temperature control during humidification therapy can cause user discomfort or safety issues. Therefore, a humidification therapy device capable of intelligent temperature regulation can better ensure user safety.

[0003] However, in the process of implementing the technical solutions of the invention in the embodiments of this application, it was found that the above technology has at least the following technical problems:

[0004] Existing technologies are unable to achieve precise temperature control, resulting in large temperature fluctuations during treatment, affecting treatment, and are unable to perform intelligent adjustments based on the user's actual needs and environmental conditions. Summary of the Invention

[0005] This application mainly solves the problem that the existing technology cannot achieve precise temperature control, resulting in large temperature fluctuations during treatment, affecting treatment, and cannot be intelligently adjusted according to the user's actual needs and environmental conditions.

[0006] In view of the above problems, the present application provides an intelligent adjustment method and system for a humidification therapy device based on temperature control. In the first aspect, the present application provides an intelligent adjustment method for a humidification therapy device based on temperature control, the method comprising: collecting characteristic air parameters in the current environment and characteristic information of users using the humidification therapy device, wherein the characteristic air parameters include air temperature, air humidity and air oxygen content; according to the humidification therapy device, obtaining a heating parameter range for heating the air, randomly selecting a first heating parameter within the heating parameter range, performing humidification prediction in combination with the air characteristic parameters, obtaining a predicted temperature parameter and a predicted humidity parameter, and performing humidification control analysis according to the first heating parameter to obtain a control delay parameter and a control accuracy parameter; based on the The first heating parameter and the air characteristic parameter are used to simulate humidification control, and according to the control delay parameter, the simulated temperature parameter and the simulated humidity parameter are tested to obtain the simulated temperature parameter and the simulated humidity parameter; according to the control accuracy parameter, the predicted temperature parameter and the predicted humidity parameter are compensated to obtain the temperature control interval and the humidity control interval, and the control verification accuracy is calculated in combination with the simulated temperature parameter and the simulated humidity parameter; according to the simulated temperature parameter and the simulated humidity parameter, in combination with the user characteristic information, the first comfort of the first heating parameter is analyzed and obtained, and the first fitness is calculated in combination with the control verification accuracy; according to the first fitness, the heating parameter is continued to be optimized and judged within the heating parameter range to obtain the optimal heating parameter, and the temperature control adjustment of the humidification therapy device is performed.

[0007] In a second aspect, the present application provides an intelligent adjustment system for a humidification therapy device based on temperature control, the system comprising: a characteristic information acquisition module, the characteristic information acquisition module being configured to collect characteristic air parameters in a current environment and user characteristic information of a user using the humidification therapy device, wherein the characteristic air parameters include air temperature, air humidity, and air oxygen content;

[0008] A heating parameter range acquisition module, the heating parameter range acquisition module is used to obtain a heating parameter range for heating the air according to the humidification therapy device, randomly select a first heating parameter within the heating parameter range, combine the air characteristic parameters, perform humidification prediction, obtain a predicted temperature parameter and a predicted humidity parameter, and perform humidification control analysis based on the first heating parameter to obtain a control delay parameter and a control accuracy parameter; a simulation humidification control module, the simulation humidification control module is based on the first heating parameter and the air characteristic parameter, and according to the control delay parameter, a simulated temperature parameter and a simulated humidity parameter are obtained by testing; a verification accuracy calculation acquisition module, the verification accuracy calculation acquisition module is used to calculate the accuracy of the air according to the first heating parameter and the air characteristic parameter. The control accuracy parameter is used to compensate the predicted temperature parameter and the predicted humidity parameter to obtain the temperature control interval and the humidity control interval, and the control verification accuracy is calculated in combination with the simulated temperature parameter and the simulated humidity parameter; a first fitness acquisition module is used to analyze and obtain the first comfort of the first heating parameter based on the simulated temperature parameter and the simulated humidity parameter in combination with the user characteristic information, and the first fitness is calculated in combination with the control verification accuracy; an optimal heating parameter acquisition module is used to continue to optimize and judge the heating parameters within the heating parameter range based on the first fitness, obtain the optimal heating parameters, and perform temperature control adjustment of the humidification therapy device.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The present application provides an intelligent adjustment method and system for a humidification therapy device based on temperature control, which relates to the field of instrument adjustment technology. The method includes: collecting air characteristic parameters and user characteristic information in the current environment, then obtaining a heating parameter range for heating the air, then randomly obtaining a first heating for humidification prediction, obtaining a predicted humidity parameter, obtaining a control delay parameter and a control accuracy parameter, calculating a verification accuracy, and then calculating a first fitness, optimizing and judging the heating parameters within the heating parameter range according to the first fitness to obtain the optimal heating parameters, and performing temperature control adjustment.

[0011] This application mainly solves the problem that existing technologies cannot achieve precise temperature control, resulting in large temperature fluctuations during treatment, affecting treatment, and cannot be intelligently adjusted according to the user's actual needs and environmental conditions. It improves the performance and adaptability of humidification therapy devices, improving treatment effects and user satisfaction.

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0014] Figure 1 A flow chart of an intelligent adjustment method for a humidification therapy device based on temperature control is provided for an embodiment of the present application;

[0015] Figure 2 A schematic flow chart of a method for obtaining predicted temperature parameters and predicted humidity parameters in an intelligent adjustment method for a humidification therapy device based on temperature control is provided for an embodiment of the present application;

[0016] Figure 3 A schematic flow chart of a method for obtaining a first fitness level in an intelligent adjustment method for a humidification therapy device based on temperature control is provided for an embodiment of the present application;

[0017] Figure 4 A structural schematic diagram of an intelligent adjustment system for a humidification therapy device based on temperature control is provided for an embodiment of the present application.

[0018] Description of the reference numerals: characteristic information acquisition module 10 , heating parameter range acquisition module 20 , simulated humidification control module 30 , verification accuracy calculation acquisition module 40 , first fitness acquisition module 50 , optimal heating parameter acquisition module 60 . DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] This application mainly solves the problem that existing technologies cannot achieve precise temperature control, resulting in large temperature fluctuations during treatment, affecting treatment, and cannot be intelligently adjusted according to the user's actual needs and environmental conditions. It improves the performance and adaptability of humidification therapy devices, improving treatment effects and user satisfaction.

[0021] The technical solution in the embodiments of the present application is to solve the above problems, and the overall solution idea is as follows:

[0022] In order to better understand the above technical solution, the following will introduce the above solution in detail with reference to the accompanying drawings and specific implementation methods:

[0023] Example 1

[0024] like Figure 1 A method for intelligently adjusting a humidification therapy device based on temperature control is shown, the method comprising:

[0025] Collecting characteristic air parameters in the current environment and user characteristic information of the user using the humidification therapy device, wherein the characteristic air parameters include air temperature, air humidity and air oxygen content;

[0026] Specifically, the characteristic parameters of the air in the current environment and the user characteristic information of the user using the humidification therapy device are collected. To collect the characteristic parameters of the air: use temperature sensors, humidity sensors, oxygen sensors and other equipment to monitor the air temperature, air humidity and air oxygen content in the current environment in real time. Transmit the sensor data to the data processing center for real-time analysis and processing. Collect user characteristic information: Install a user characteristic information collection module on the humidification therapy device. This module can collect information such as the user's age, gender, disease type, and breathing method. Through interaction with the humidification therapy device, real-time data such as the user's usage habits, breathing frequency, and breathing depth are obtained. The collection of characteristic parameters of the air and user characteristic information in the current environment can be realized, and intelligent adjustment based on this information can be achieved.

[0027] Obtaining a heating parameter range for controlling air heating according to the humidification therapy device, randomly selecting a first heating parameter within the heating parameter range, performing humidification prediction based on the air characteristic parameters to obtain a predicted temperature parameter and a predicted humidity parameter, and performing humidification control analysis based on the first heating parameter to obtain a control delay parameter and a control accuracy parameter;

[0028] Specifically, based on the humidification therapy device, a heating parameter range for controlling air heating is obtained. A first heating parameter is randomly selected within the heating parameter range. Humidification prediction is performed based on the air characteristic parameters to obtain a predicted temperature parameter and a predicted humidity parameter. Humidification control analysis is then performed based on the first heating parameter to obtain a control delay parameter and a control accuracy parameter. The heating parameter range is determined by researching the performance of the humidification therapy device and empirical data. The heating parameter range may include upper and lower temperature limits, heating rate, and other factors. The first heating parameter is randomly selected within the determined heating parameter range as the first heating parameter. The first heating parameter may be selected using a random number generator or a preset algorithm. Humidification prediction is performed based on the air characteristic parameters by combining the first heating parameter with the air characteristic parameters (such as air temperature, humidity, and oxygen content). A mathematical model or algorithm is used to predict the humidification effect to obtain a predicted temperature parameter and a predicted humidity parameter. Humidification control analysis is performed based on the predicted temperature and humidity parameters to analyze humidification control. Factors such as the control system's stability, response speed, and control accuracy can be considered. Obtaining control lag parameters and control accuracy parameters: Based on the results of the humidification control analysis, the control system's lag parameters are calculated. These lag parameters reflect the control system's response speed to changes in temperature and humidity. Simultaneously, the control system's accuracy parameters are calculated to assess its ability to track predicted temperature and humidity. This allows for the selection of heating parameter ranges for air heating control, humidification prediction, and control analysis. By evaluating control lag parameters and control accuracy parameters, the control strategy of the humidification therapy device can be further optimized, improving its stability and reliability.

[0029] Performing simulated humidification control based on the first heating parameter and the air characteristic parameter, and testing to obtain simulated temperature parameters and simulated humidity parameters according to the control delay parameter;

[0030] Specifically, simulated humidification control is performed based on the first heating parameter and air characteristic parameters, and simulated temperature and humidity parameters are tested and obtained based on the control delay parameter. Simulated humidification control: Based on the first heating parameter and air characteristic parameters, simulation software is used to simulate control of the humidification therapy device. During the simulated control process, heating and humidification processes can be simulated, and changes in simulated temperature and humidity can be monitored in real time. Testing simulated temperature parameters: During the simulated control process, simulated temperature change data is recorded. The simulated temperature data is processed and analyzed based on the control delay parameter. The accuracy and effectiveness of the simulated control are evaluated by comparing it with the predicted temperature parameters. Testing simulated humidity parameters: During the simulated control process, simulated humidity change data is recorded. The simulated humidity data is processed and analyzed based on the control delay parameter. The accuracy and effectiveness of the simulated control are evaluated by comparing it with the predicted humidity parameters. The simulated control of the humidification therapy device can be tested to obtain simulated temperature and humidity parameters. These parameters can be used to evaluate and control the performance and effectiveness of the humidification therapy device.

[0031] Compensating the predicted temperature parameter and the predicted humidity parameter according to the control accuracy parameter to obtain a temperature control interval and a humidity control interval, and calculating and obtaining a control verification accuracy by combining the simulated temperature parameter and the simulated humidity parameter;

[0032] Specifically, the predicted temperature parameter and the predicted humidity parameter are compensated based on the control accuracy parameter to obtain a temperature control range and a humidity control range. The control verification accuracy is calculated by combining the simulated temperature parameter and the simulated humidity parameter. The predicted temperature parameter and the predicted humidity parameter are compensated: deviations in the predicted temperature parameter and the predicted humidity parameter are analyzed based on the control accuracy parameter. A compensation algorithm or mathematical model is used to correct or compensate the predicted parameters. The compensated predicted parameters are more closely aligned with actual temperature and humidity variations during humidification. The temperature control range and the humidity control range are obtained: the temperature control range and the humidity control range are determined based on the compensated predicted temperature parameter and the predicted humidity parameter. The control range can be set based on medical requirements, user conditions, and the performance of the humidification therapy device. The control range can be set within a certain range to accommodate different environmental conditions and individual differences. The simulated temperature parameter and the simulated humidity parameter are combined: the simulated temperature parameter and the simulated humidity parameter are compared with the control range. The degree of match between the simulated parameters and the control range is analyzed to assess the accuracy of the simulated control. The control verification accuracy is calculated: the degree of match between the simulated parameters and the control range is calculated. Control verification accuracy reflects the humidification device's ability to control temperature and humidity under analog control. Statistical methods or related indicators can be used to quantify and evaluate control verification accuracy. Control verification accuracy can be calculated by combining control accuracy parameters, prediction parameters, simulation parameters, and control intervals. This accuracy can be used to evaluate the performance and reliability of the humidification device and optimize humidification therapy plans and control strategies.

[0033] Analyzing and obtaining a first comfort level of the first heating parameter based on the simulated temperature parameter and the simulated humidity parameter in combination with the user characteristic information, and calculating and obtaining a first fitness level based on the control verification accuracy;

[0034] Specifically, based on the simulated temperature and humidity parameters, combined with the user's characteristic information, a first comfort level for the first heating parameter is analyzed and obtained. Combined with the control verification accuracy, a first fitness level is calculated. The first comfort level for the first heating parameter is analyzed by combining the simulated temperature and humidity parameters with user characteristic information (such as the user's age, gender, and disease type). A comfort assessment algorithm or model is used to analyze the impact of the first heating parameter on the user's comfort level. The comfort assessment can comprehensively consider factors such as temperature, humidity, and airflow, as well as individual user differences and needs. The first fitness level is calculated by combining the first comfort level with the control verification accuracy. The calculation of the first fitness level takes into account the performance, control strategy, and user needs of the humidification therapy device. The first fitness level can reflect the comprehensive performance of the humidification therapy device in meeting user comfort and control accuracy. The first comfort level for the first heating parameter can be analyzed and calculated by combining the simulated parameters, user characteristic information, and control verification accuracy.

[0035] According to the first adaptability, the heating parameters are continuously optimized and judged within the heating parameter range to obtain the optimal heating parameters and perform temperature control and adjustment of the humidification therapy device.

[0036] Specifically, based on the first fitness, the heating parameters are continuously optimized and identified within the heating parameter range to obtain the optimal heating parameters. Temperature control and adjustment of the humidification therapy device are then performed. Heating parameter optimization and identification are performed: within the initially selected heating parameter range, the heating parameters are continuously adjusted and optimized. Based on the evaluation results of the first fitness, heating parameters that achieve optimal comfort and control accuracy are selected. Optimization methods such as trial and error, gradient descent, and genetic algorithms can be used to iteratively optimize the heating parameters. Obtaining the optimal heating parameters: During the optimization process, the first fitness under different heating parameters is continuously compared to select the optimal heating parameters. The optimal heating parameters should meet the user's comfort and treatment needs while also providing high control accuracy. Temperature control of the humidification therapy device is then performed: based on the obtained optimal heating parameters, the temperature of the humidification therapy device is controlled and adjusted. Temperature control under the optimal heating parameters is achieved by adjusting the humidification therapy device's heating element, temperature sensor, and other components. During the temperature control process, temperature stability and control accuracy must be ensured to meet the user's treatment needs. According to the evaluation result of the first fitness, the heating parameters of the humidification therapy device can be optimized and judged to obtain the optimal heating parameters and perform temperature control and adjustment.

[0037] Furthermore, if Figure 2As shown, the method of the present application randomly selects a first heating parameter within the heating parameter range, combines the air characteristic parameters, performs humidification prediction, and obtains predicted temperature parameters and predicted humidity parameters, including:

[0038] Based on the usage data record of the humidification therapy device, a sample heating parameter set, a sample control characteristic parameter set, a sample temperature parameter set, and a sample humidity parameter set are obtained;

[0039] Using the sample heating parameter set and the sample control characteristic parameter set as input data, and using the sample temperature parameter set and the sample humidity parameter set as output data, a humidification predictor is constructed;

[0040] According to the first heating parameter and the air characteristic parameter, based on the humidification predictor, heating control prediction is performed to obtain the predicted temperature parameter and the predicted humidity parameter.

[0041] Specifically, based on the usage data records of the humidification therapy device, a sample heating parameter set, a sample control characteristic parameter set, a sample temperature parameter set, and a sample humidity parameter set are obtained; a humidification predictor is constructed using the sample heating parameter set and the sample control characteristic parameter set as input data and the sample temperature parameter set and the sample humidity parameter set as output data; a heating control prediction is performed based on the humidification predictor according to the first heating parameter and the air characteristic parameter, obtaining the predicted temperature parameter and the predicted humidity parameter. The sample heating parameter set, the sample control characteristic parameter set, the sample temperature parameter set, and the sample humidity parameter set are obtained: the sample heating parameter set, the sample control characteristic parameter set, the sample temperature parameter set, and the sample humidity parameter set are extracted from the usage data records of the humidification therapy device. These sets may include multiple sample data points, each corresponding to a specific heating parameter, control characteristic parameter, temperature parameter, and humidity parameter. The humidification predictor is constructed using the sample heating parameter set and the sample control characteristic parameter set as input data and the sample temperature parameter set and the sample humidity parameter set as output data. The humidification predictor is constructed using an appropriate machine learning algorithm or model (e.g., a regression model, a neural network, etc.). The humidification predictor predicts the output temperature and humidity based on the input heating parameters and control characteristic parameters. Heating control prediction based on the humidification predictor: Based on the first heating parameter and air characteristic parameters, these are provided as input data to the humidification predictor. The humidification predictor performs predictions based on this input data, generating predicted temperature parameters and predicted humidity parameters. These predicted parameters can be used to guide the heating control of the humidification therapy device, achieving more precise temperature and humidity regulation. A humidification predictor can be constructed based on the usage data records of the humidification therapy device and used to perform heating control predictions.

[0042] Furthermore, the method of the present application performs humidification control analysis based on the first heating parameter to obtain a control delay parameter and a control accuracy parameter, including:

[0043] Obtaining a sample heating parameter set, and obtaining a sample control delay parameter set and a sample control accuracy parameter set;

[0044] Using the sample heating parameter set as input data, and the sample control delay parameter set and the sample control accuracy parameter set as output data, respectively constructing a control delay analysis branch and a control accuracy analysis branch to obtain a humidification control analyzer;

[0045] According to the first heating parameter, based on the humidification control analyzer, a humidification control analysis is performed to obtain the control delay parameter and the control accuracy parameter.

[0046] Specifically, a sample heating parameter set is obtained, along with a sample control latency parameter set and a sample control accuracy parameter set. A control latency analysis branch and a control accuracy analysis branch are constructed, respectively, using the sample heating parameter set as input data and the sample control latency parameter set and the sample control accuracy parameter set as output data, thereby obtaining a humidification control analyzer. Based on the first heating parameter and the humidification control analyzer, a humidification control analysis is performed to obtain the control latency parameter and the control accuracy parameter. The sample heating parameter set is obtained by extracting the sample heating parameter set from the usage data records of the humidification therapy device. These sample heating parameters may include different heating rates, heating times, etc. A sample control latency parameter set and a sample control accuracy parameter set are obtained by extracting the sample control latency parameter set from the usage data records of the humidification therapy device. Simultaneously, a sample control accuracy parameter set is extracted. These sets may include multiple sample data points, each corresponding to a specific control latency parameter and control accuracy parameter. The control latency analysis branch and the control accuracy analysis branch are constructed by using the sample heating parameter set as input data. The sample control delay parameter set and the sample control accuracy parameter set are used as output data. Using appropriate machine learning algorithms or models (such as regression models, neural networks, etc.), a control delay analysis branch and a control accuracy analysis branch are constructed respectively. The control delay analysis branch predicts the output control delay parameters based on the input heating parameters. The control accuracy analysis branch predicts the output control accuracy parameters based on the input heating parameters. Humidification control analysis is performed based on the humidification control analyzer: Based on the first heating parameter, the parameter is provided as input data to the humidification control analyzer. The humidification control analyzer analyzes these input data and generates control delay parameters and control accuracy parameters. These parameters can be used to evaluate the control performance and effect of the humidification therapy device, providing a reference for further optimization and treatment. A humidification control analyzer can be constructed based on the usage data records of the humidification therapy device, and the analyzer can be used to perform humidification control analysis.

[0047] Furthermore, the method of the present application performs simulated humidification control based on the first heating parameter, and tests and obtains simulated temperature parameters and simulated humidity parameters according to the control delay parameter, including:

[0048] performing simulated humidification control according to the first heating parameter and the air characteristic parameter;

[0049] According to the control delay parameter, after the control time reaches the control delay parameter, the simulated temperature parameter and the simulated humidity parameter are obtained by testing.

[0050] Specifically, simulated humidification control is performed based on the first heating parameter and air characteristic parameters. After the control time reaches the control delay parameter, simulated temperature and humidity parameters are tested and obtained. Simulated humidification control is then performed based on the first heating parameter and air characteristic parameters. The humidification therapy device is simulated based on the provided first heating parameter and air characteristic parameters. Simulation software simulates the heating and humidification processes of the humidification therapy device. During the simulation, changes in simulated temperature and humidity are monitored and controlled in real time. Testing is performed based on the control delay parameter. During the simulated humidification control process, a control time is set and recorded. When the control time reaches the control delay parameter, simulated control is terminated. Simulated temperature and humidity parameters are tested and obtained. After the control time reaches the control delay parameter, the simulated temperature and humidity parameters are recorded. These parameters can be used to evaluate and control the performance and effectiveness of the humidification therapy device. Simulated humidification control can be performed based on the first heating parameter and air characteristic parameters, and after the control time reaches the control delay parameter, the simulated temperature and humidity parameters are tested and obtained.

[0051] Furthermore, the method of the present application compensates the predicted temperature parameter and the predicted humidity parameter according to the control accuracy parameter to obtain the temperature control interval and the humidity control interval, and calculates the control verification accuracy in combination with the simulated temperature parameter and the simulated humidity parameter, including:

[0052] Compensating the predicted temperature parameter and the predicted humidity parameter according to the control accuracy parameter to obtain a temperature control interval and a humidity control interval;

[0053] Obtaining deviations of the simulated temperature parameter and the simulated humidity parameter falling within the temperature control interval and the humidity control interval respectively, and calculating the temperature control accuracy and the humidity control accuracy;

[0054] The temperature control accuracy and the humidity control accuracy are weightedly calculated to obtain the control verification accuracy.

[0055] Specifically, the predicted temperature parameter and predicted humidity parameter are compensated based on the control accuracy parameter to obtain a temperature control interval and a humidity control interval. The deviations of the simulated temperature parameter and simulated humidity parameter falling within the temperature control interval and the humidity control interval are respectively obtained to calculate the temperature control accuracy and the humidity control accuracy. The temperature control accuracy and the humidity control accuracy are weighted to obtain the control verification accuracy. The predicted temperature parameter and the predicted humidity parameter are compensated based on the control accuracy parameter. The predicted temperature parameter and the predicted humidity parameter are corrected or compensated based on the provided control accuracy parameter. The purpose of compensation is to make the predicted parameters more closely match the actual treatment needs and goals. The temperature control interval and the humidity control interval are obtained. The temperature control interval and the humidity control interval are determined based on the compensated predicted temperature parameter and the predicted humidity parameter. The control interval can be set based on medical requirements, user conditions, and the performance of the humidification therapy device. The deviations of the simulated temperature parameter and simulated humidity parameter falling within the temperature control interval and the humidity control interval are respectively obtained. The simulated temperature parameter is evaluated to determine whether it falls within the temperature control interval. Simultaneously, the simulated humidity parameter is evaluated to determine whether it falls within the humidity control interval. Record the deviation of each parameter; smaller deviations indicate higher control accuracy. Calculate temperature and humidity control accuracy: Calculate the temperature and humidity control accuracy based on the deviation of the simulated temperature and humidity parameters. Accuracy can reflect the humidification device's ability to control temperature and humidity. Perform a weighted calculation of the temperature and humidity control accuracy: Assign appropriate weights to the temperature and humidity control accuracies. Calculate the comprehensive control verification accuracy using methods such as weighted averaging. Compensate the predicted temperature and humidity parameters based on the control accuracy parameters to obtain the temperature and humidity control ranges.

[0056] Furthermore, if Figure 3 As shown, the method of the present application analyzes and obtains a first comfort level of the first heating parameter based on the simulated temperature parameter and the simulated humidity parameter in combination with the user characteristic information, and calculates and obtains a first fitness level in combination with the control verification accuracy, including:

[0057] Obtaining a sample simulation temperature parameter set, a sample simulation temperature parameter set, a sample user feature information set, and a sample comfort level set;

[0058] Constructing a humidification comfort analyzer using the sample simulation temperature parameter set, the sample simulation temperature parameter set, the sample user feature information set, and the sample comfort set;

[0059] According to the simulated temperature parameter, the simulated temperature parameter and the user characteristic information, the humidification comfort analyzer is used to analyze and obtain the first comfort level;

[0060] The control verification accuracy and the first comfort level are weightedly calculated to obtain the first fitness level.

[0061] Specifically, a sample simulated temperature parameter set, a sample simulated humidity parameter set, a sample user characteristic information set, and a sample comfort set are obtained; a humidification comfort analyzer is constructed using the sample simulated temperature parameter set, the sample simulated humidity parameter set, the sample user characteristic information set, and the sample comfort set; the first comfort level is obtained through analysis based on the simulated temperature parameter, the simulated humidity parameter, and the user characteristic information and the humidification comfort analyzer; the control verification accuracy and the first comfort level are weighted and calculated to obtain the first fitness level, and a sample simulated temperature parameter set, a sample simulated humidity parameter set, a sample user characteristic information set, and a sample comfort level set are obtained: a sample simulated temperature parameter set and a sample simulated humidity parameter set are extracted from the usage data record of the humidification therapy device. At the same time, a sample user characteristic information set and a sample comfort level set are extracted. These sets may include multiple sample data points, each of which corresponds to a specific simulated temperature parameter, simulated humidity parameter, user characteristic information, and comfort level. A humidification comfort analyzer is constructed using a set of sample simulated temperature parameters, a set of sample simulated humidity parameters, a set of sample user profile information, and a set of sample comfort levels. The humidification comfort analyzer is constructed using an appropriate machine learning algorithm or model (such as a neural network). The humidification comfort analyzer predicts an output comfort level based on the input simulated temperature parameters, simulated humidity parameters, and user profile information. A first comfort level is obtained based on the simulated temperature parameters, simulated humidity parameters, and user profile information. The simulated temperature parameters, simulated humidity parameters, and user profile information are provided as input data to the humidification comfort analyzer. The humidification comfort analyzer predicts and generates a first comfort level based on this input data. A weighted calculation is performed on the control verification accuracy and the first comfort level to obtain a first fitness level. Appropriate weights are assigned to the control verification accuracy and the first comfort level. The first fitness level is calculated using a weighted average method. The first fitness level reflects the comprehensive performance of the humidification therapy device in meeting user comfort and control accuracy. A humidification comfort analyzer can be constructed based on the acquired sample data and used to perform comfort analysis.

[0062] Furthermore, the method of the present application, based on the first fitness, continues to optimize and identify the heating parameters within the heating parameter range to obtain the optimal heating parameters, including:

[0063] Randomly selecting a second heating parameter within the heating parameter range, and analyzing and calculating to obtain a second fitness;

[0064] Determine whether the second fitness is greater than the first fitness. If so, use the second heating parameter as the optimization result. If not, calculate the exit probability based on the second fitness and the first fitness, as shown in the following formula:

[0065] ;

[0066] Among them, P is the bounce probability, is the second fitness, is the first fitness;

[0067] Calculate and generate a bounce probability distribution based on the bounce probability, randomly generate a random number between 0 and 1, and determine the optimization result based on the position of the random number within the bounce probability distribution;

[0068] The optimization of the heating parameters is continued until the convergence times are reached, and the final optimization result is output to obtain the optimal heating parameters.

[0069] Specifically, a second heating parameter is randomly selected within the heating parameter range: The range of the heating parameters, such as the temperature range or heating rate range, is determined. A random number generator is used to randomly select a second heating parameter within the heating parameter range. A second fitness is analyzed and calculated: A humidification comfort analyzer or other related model is used, taking the second heating parameter as input and analyzing and calculating the second fitness. Whether the second fitness is greater than the first fitness is determined: The second fitness is compared with the first fitness. If the second fitness is greater than the first fitness, the second heating parameter is used as the optimization result. The bounce probability is calculated based on the second fitness and the first fitness: The maximum fitness can be a pre-set threshold or the maximum value of all fitnesses. A bounce probability distribution is calculated and generated based on the bounce probability: The bounce probability is normalized to a value between [0, 1]. A probability distribution function (such as the normal distribution or uniform distribution) is used to calculate and generate the corresponding bounce probability distribution. A random number between 0 and 1 is randomly generated: A random number generator is used to generate a random number between 0 and 1. Determine the optimization result based on the position where the random number falls within the jump probability distribution: compare the generated random number with the jump probability distribution. Based on the position where the random number falls, determine whether to accept the new heating parameters as the optimization result. Continue to optimize the heating parameters until the convergence number is reached: if the new heating parameters are accepted as the optimization result, use them as the new first heating parameters, and repeat the above steps for the next round of optimization. Set a convergence number, and stop the optimization when it is reached. Output the final optimization result and obtain the optimal heating parameters: in all rounds of optimization, select the heating parameters with the highest fitness as the optimal heating parameters. Output the optimal heating parameters as the result. Through the above refinement steps, the heating parameters can be gradually optimized, and eventually the optimal heating parameter combination can be obtained.

[0070] Example 2

[0071] Based on the same inventive concept as the aforementioned embodiment of a temperature-controlled humidification therapy device intelligent adjustment method, as Figure 4 As shown, the present application provides an intelligent adjustment system for a humidification therapy device based on temperature control, the system comprising:

[0072] A characteristic information acquisition module 10 is used to collect characteristic parameters of the air in the current environment and user characteristic information of the user using the humidification therapy device, wherein the characteristic parameters of the air include air temperature, air humidity and air oxygen content;

[0073] a heating parameter range acquisition module 20 configured to acquire a heating parameter range for controlling air heating based on the humidification therapy device, randomly select a first heating parameter within the heating parameter range, perform humidification prediction based on the air characteristic parameters to obtain a predicted temperature parameter and a predicted humidity parameter, and perform humidification control analysis based on the first heating parameter to obtain a control delay parameter and a control accuracy parameter;

[0074] A simulated humidification control module 30, which performs simulated humidification control based on the first heating parameter and the air characteristic parameter, and obtains simulated temperature parameters and simulated humidity parameters through testing according to the control delay parameter;

[0075] A verification accuracy calculation and acquisition module 40 is used to compensate the predicted temperature parameter and the predicted humidity parameter according to the control accuracy parameter to obtain a temperature control interval and a humidity control interval, and calculate and obtain the control verification accuracy in combination with the simulated temperature parameter and the simulated humidity parameter;

[0076] A first fitness acquisition module 50 is configured to analyze and obtain a first comfort level of the first heating parameter based on the simulated temperature parameter and the simulated humidity parameter in combination with the user characteristic information, and calculate and obtain a first fitness level in combination with the control verification accuracy;

[0077] The optimal heating parameter acquisition module 60 is used to continue to optimize and judge the heating parameters within the heating parameter range according to the first fitness, obtain the optimal heating parameters, and perform temperature control and adjustment of the humidification therapy device.

[0078] Furthermore, the system also includes:

[0079] The predicted humidity parameter acquisition module obtains a sample heating parameter set, a sample control characteristic parameter set, a sample temperature parameter set and a sample humidity parameter set based on the usage data record of the humidification therapy device; uses the sample heating parameter set and the sample control characteristic parameter set as input data, and uses the sample temperature parameter set and the sample humidity parameter set as output data to construct a humidification predictor; and performs heating control prediction based on the humidification predictor according to the first heating parameter and the air characteristic parameter to obtain the predicted temperature parameter and the predicted humidity parameter.

[0080] Furthermore, the system also includes:

[0081] An accuracy parameter acquisition module is used to obtain a sample heating parameter set, and obtain a sample control delay parameter set and a sample control accuracy parameter set; using the sample heating parameter set as input data, and the sample control delay parameter set and the sample control accuracy parameter set as output data, respectively constructing a control delay analysis branch and a control accuracy analysis branch to obtain a humidification control analyzer; according to the first heating parameter, based on the humidification control analyzer, a humidification control analysis is performed to obtain the control delay parameter and the control accuracy parameter.

[0082] Furthermore, the system also includes:

[0083] The simulated temperature parameter acquisition module is used to perform simulated humidification control according to the first heating parameter and the air characteristic parameter; according to the control delay parameter, after the control time reaches the control delay parameter, the simulated temperature parameter and the simulated humidity parameter are tested and obtained.

[0084] Furthermore, the system also includes:

[0085] A control verification accuracy acquisition module is used to compensate the predicted temperature parameter and the predicted humidity parameter according to the control accuracy parameter to obtain the temperature control interval and the humidity control interval; obtain the falling deviation of the simulated temperature parameter and the simulated humidity parameter into the temperature control interval and the humidity control interval respectively, and calculate the temperature control accuracy and the humidity control accuracy; perform weighted calculation on the temperature control accuracy and the humidity control accuracy to obtain the control verification accuracy.

[0086] Furthermore, the system also includes:

[0087] The first comfort analysis acquisition module is used to obtain a sample simulated temperature parameter set, a sample simulated temperature parameter set, a sample user characteristic information set and a sample comfort set; a humidification comfort analyzer is constructed using the sample simulated temperature parameter set, the sample simulated temperature parameter set, the sample user characteristic information set and the sample comfort set; according to the simulated temperature parameters, the simulated temperature parameters and the user characteristic information, based on the humidification comfort analyzer, the first comfort is analyzed and obtained; the control verification accuracy and the first comfort are weightedly calculated to obtain the first fitness.

[0088] Furthermore, the system also includes:

[0089] The optimal heating parameter acquisition module is used to randomly select a second heating parameter within the heating parameter range, and analyze and calculate to obtain a second fitness; determine whether the second fitness is greater than the first fitness; if so, use the second heating parameter as the optimization result; if not, calculate the jump probability based on the second fitness and the first fitness, as shown in the following formula: ; Where P is the probability of jumping out, is the second fitness, is the first fitness; according to the jump-out probability, a jump-out probability distribution is calculated and generated, a random number between 0 and 1 is randomly generated, and the optimization result is obtained according to the position where the random number falls within the jump-out probability distribution; the optimization of the heating parameters is continued until the convergence number is reached, and the final optimization result is output to obtain the optimal heating parameters.

[0090] Through the detailed description of the aforementioned intelligent adjustment method of a humidification therapy device based on temperature control, those skilled in the art can clearly understand the intelligent adjustment system of a humidification therapy device based on temperature control in this embodiment. For the system disclosed in the embodiment, since it corresponds to the device disclosed in the embodiment, the description is relatively simple. For relevant matters, please refer to the method part.

[0091] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for intelligently adjusting a humidification therapy device based on temperature control, characterized in that: The method comprises: Collecting characteristic air parameters in the current environment and user characteristic information of the user using the humidification therapy device, wherein the characteristic air parameters include air temperature, air humidity and air oxygen content; Obtaining a heating parameter range for controlling air heating according to the humidification therapy device, randomly selecting a first heating parameter within the heating parameter range, performing humidification prediction based on the air characteristic parameters to obtain a predicted temperature parameter and a predicted humidity parameter, and performing humidification control analysis based on the first heating parameter to obtain a control delay parameter and a control accuracy parameter; Performing simulated humidification control based on the first heating parameter and the air characteristic parameter, and testing to obtain simulated temperature parameters and simulated humidity parameters according to the control delay parameter; Compensating the predicted temperature parameter and the predicted humidity parameter according to the control accuracy parameter to obtain a temperature control interval and a humidity control interval, and calculating and obtaining a control verification accuracy by combining the simulated temperature parameter and the simulated humidity parameter; Analyzing and obtaining a first comfort level of the first heating parameter based on the simulated temperature parameter and the simulated humidity parameter in combination with the user characteristic information, and calculating and obtaining a first fitness level based on the control verification accuracy; Based on the first fitness, continue to optimize and identify the heating parameters within the heating parameter range to obtain optimal heating parameters and perform temperature control and adjustment on the humidification therapy device; According to the first fitness, continuing to optimize and identify the heating parameters within the heating parameter range to obtain the optimal heating parameters includes: Randomly selecting a second heating parameter within the heating parameter range, and analyzing and calculating to obtain a second fitness; Determine whether the second fitness is greater than the first fitness. If so, use the second heating parameter as the optimization result. If not, calculate the exit probability based on the second fitness and the first fitness, as shown in the following formula: ； Among them, P is the bounce probability, is the second fitness, is the first fitness; Calculate and generate a bounce probability distribution based on the bounce probability, randomly generate a random number between 0 and 1, and determine the optimization result based on the position of the random number within the bounce probability distribution; The optimization of the heating parameters is continued until the convergence times are reached, and the final optimization result is output to obtain the optimal heating parameters.

2. The method according to claim 1, characterized in that Randomly selecting a first heating parameter within the heating parameter range, and performing humidification prediction in combination with the air characteristic parameter to obtain a predicted temperature parameter and a predicted humidity parameter, including: Based on the usage data record of the humidification therapy device, a sample heating parameter set, a sample control characteristic parameter set, a sample temperature parameter set, and a sample humidity parameter set are obtained; Using the sample heating parameter set and the sample control characteristic parameter set as input data, and using the sample temperature parameter set and the sample humidity parameter set as output data, a humidification predictor is constructed; According to the first heating parameter and the air characteristic parameter, based on the humidification predictor, heating control prediction is performed to obtain the predicted temperature parameter and the predicted humidity parameter.

3. The method according to claim 1, characterized in that Performing humidification control analysis based on the first heating parameter to obtain a control delay parameter and a control accuracy parameter includes: Obtaining a sample heating parameter set, and obtaining a sample control delay parameter set and a sample control accuracy parameter set; Using the sample heating parameter set as input data, and the sample control delay parameter set and the sample control accuracy parameter set as output data, respectively constructing a control delay analysis branch and a control accuracy analysis branch to obtain a humidification control analyzer; According to the first heating parameter, based on the humidification control analyzer, a humidification control analysis is performed to obtain the control delay parameter and the control accuracy parameter.

4. The method according to claim 1, wherein Performing simulated humidification control based on the first heating parameter and testing to obtain simulated temperature parameters and simulated humidity parameters according to the control delay parameter includes: performing simulated humidification control according to the first heating parameter and the air characteristic parameter; According to the control delay parameter, after the control time reaches the control delay parameter, the simulated temperature parameter and the simulated humidity parameter are obtained by testing.

5. The method according to claim 1, wherein According to the control accuracy parameter, the predicted temperature parameter and the predicted humidity parameter are compensated to obtain a temperature control interval and a humidity control interval, and the control verification accuracy is calculated in combination with the simulated temperature parameter and the simulated humidity parameter, including: Compensating the predicted temperature parameter and the predicted humidity parameter according to the control accuracy parameter to obtain a temperature control interval and a humidity control interval; Obtaining deviations of the simulated temperature parameter and the simulated humidity parameter falling within the temperature control interval and the humidity control interval respectively, and calculating the temperature control accuracy and the humidity control accuracy; The temperature control accuracy and the humidity control accuracy are weightedly calculated to obtain the control verification accuracy.

6. The method according to claim 1, characterized in that The method further comprises: analyzing and obtaining a first comfort level of the first heating parameter based on the simulated temperature parameter and the simulated humidity parameter in combination with the user characteristic information; and calculating and obtaining a first fitness level based on the control verification accuracy, including: Obtaining a sample simulation temperature parameter set, a sample simulation humidity parameter set, a sample user feature information set, and a sample comfort level set; Constructing a humidification comfort analyzer using the sample simulated temperature parameter set, the sample simulated humidity parameter set, the sample user characteristic information set, and the sample comfort set; According to the simulated temperature parameter, the simulated humidity parameter and the user characteristic information, the first comfort level is obtained by analyzing based on the humidification comfort level analyzer; The control verification accuracy and the first comfort level are weightedly calculated to obtain the first fitness level.

7. An intelligent adjustment system for a humidification therapy device based on temperature control, characterized in that: The system comprises: A characteristic information acquisition module, which is used to collect characteristic parameters of the air in the current environment and user characteristic information of the user using the humidification therapy device, wherein the air characteristic parameters include air temperature, air humidity and air oxygen content; a heating parameter range acquisition module, the heating parameter range acquisition module being configured to obtain a heating parameter range for controlling air heating based on the humidification therapy apparatus, randomly select a first heating parameter within the heating parameter range, perform humidification prediction based on the air characteristic parameters to obtain a predicted temperature parameter and a predicted humidity parameter, and perform humidification control analysis based on the first heating parameter to obtain a control delay parameter and a control accuracy parameter; a simulated humidification control module, wherein the simulated humidification control module performs simulated humidification control based on the first heating parameter and the air characteristic parameter, and obtains simulated temperature parameters and simulated humidity parameters through testing according to the control delay parameter; A verification accuracy calculation and acquisition module is used to compensate the predicted temperature parameter and the predicted humidity parameter according to the control accuracy parameter to obtain a temperature control interval and a humidity control interval, and calculate and obtain the control verification accuracy in combination with the simulated temperature parameter and the simulated humidity parameter; a first fitness acquisition module, configured to analyze and obtain a first comfort level of the first heating parameter based on the simulated temperature parameter and the simulated humidity parameter in combination with the user characteristic information, and calculate and obtain a first fitness level in combination with the control verification accuracy; an optimal heating parameter acquisition module, configured to continue optimizing and distinguishing heating parameters within the heating parameter range based on the first fitness, obtain optimal heating parameters, and perform temperature control and adjustment of the humidification therapy device; The system further comprises: The optimal heating parameter acquisition module is used to randomly select a second heating parameter within the heating parameter range, and analyze and calculate to obtain a second fitness; determine whether the second fitness is greater than the first fitness; if so, use the second heating parameter as the optimization result; if not, calculate the jump probability based on the second fitness and the first fitness, as shown in the following formula: ; Where P is the bounce probability, is the second fitness, is the first fitness; according to the jump-out probability, a jump-out probability distribution is calculated and generated, a random number between 0 and 1 is randomly generated, and the optimization result is obtained according to the position where the random number falls within the jump-out probability distribution; the optimization of the heating parameters is continued until the convergence number is reached, and the final optimization result is output to obtain the optimal heating parameters.

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