Early warning method and device for human body exhaled gas fused with multi-element sensor

Abstract

The invention relates to a human body exhaled gas early warning method fusing multiple sensors. The method comprises the following steps: determining the types of sensors needing to be arranged according to all types of human body exhaled gas; the weight value of each type of human body exhaled gas is pre-determined; determining a standard value of each type of gas exhaled by the human body; human body exhaled gas of a target user is obtained, and the real-time value of each type of human body exhaled gas is obtained; calculating a deviation coefficient between the real-time value of each type of human body exhaled gas of the target user and the standard value of the real-time value; calculating an abnormal risk value according to the deviation coefficient of each type of human body exhaled gas of the target user and the weighted value of the type; and judging whether the abnormal risk value is greater than a preset risk threshold, if so, judging that an abnormal risk exists, and if not, judging that the abnormal risk does not exist. According to the invention, a certain prevention effect on the health of the user can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field related to human exhaled gas analysis, and particularly to a warning method and device for human exhaled gas integrating multiple sensors. Background Art

[0002] Human exhaled gas analysis (also known as exhaled gas detection or exhaled gas analysis) is a non-invasive physiological monitoring method, which usually evaluates the health status, diagnoses diseases or conducts real-time monitoring by analyzing the components in exhaled gas, such as gas concentrations (such as carbon dioxide, oxygen, nitrogen, etc.), volatile organic compounds (VOCs), ammonia, ethanol, etc.

[0003] Currently, the abnormal warning methods for human exhaled gas analysis on the market often set simple thresholds. This method can indeed identify obvious abnormal situations, but for some initially non-obvious abnormal situations of indicators, it often has no reference significance and cannot give users relatively accurate warning guidance. Summary of the Invention

[0004] The purpose of the present invention is to at least solve one of the deficiencies of the prior art, and provide a warning method for human exhaled gas integrating multiple sensors.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] Specifically, a warning method for human exhaled gas integrating multiple sensors is proposed, including the following:

[0007] Determine all types of human exhaled gas, and determine the types of sensors to be arranged according to all types of human exhaled gas;

[0008] Predetermine the weight value of each type of human exhaled gas;

[0009] Determine the standard value of each type of human exhaled gas by analyzing healthy user samples;

[0010] Obtain the human exhaled gas of the target user, and obtain the real-time value of each type of human exhaled gas therein through the arranged multiple sensors;

[0011] Calculate the offset coefficient between the real-time value and the standard value of each type of human exhaled gas of the target user to obtain the offset coefficient of each type of human exhaled gas of the target user;

[0012] Calculate the abnormal risk value according to the offset coefficient of each type of human exhaled gas of the target user and the weight value of this type;

[0013] Determine whether the abnormal risk value is greater than the preset risk threshold. If it is greater than the preset risk threshold, it is determined that there is an abnormal risk. If it is not greater than the preset risk threshold, it is determined that there is no abnormal risk.

[0014] Further, specifically, all types of human exhaled gases include carbon dioxide concentration, oxygen concentration, nitrogen, volatile organic compounds, ammonia, ethanol, and exhaled nitric oxide.

[0015] Further, specifically, pre-determine the weight value of each type of human exhaled gas, including,

[0016] If there are m types of human exhaled gases;

[0017] Standardize the m types of human exhaled gases to obtain the standardized m types of human exhaled gases;

[0018] Calculate the information entropy of each type of human exhaled gas. The calculation formula is as follows,

[0019]

[0020] Among them, H j represents the information entropy of the jth type of human exhaled gas, and p ij is the proportion of the ith type of human exhaled gas after standardization in the jth type of human exhaled gas, and k is a preset constant;

[0021] Calculate its corresponding weight according to the information entropy of each type of human exhaled gas. The calculation formula is as follows,

[0022]

[0023] Among them, w j represents the weight of the jth type of human exhaled gas.

[0024] Further, specifically, determine the standard value of each type of human exhaled gas by analyzing the healthy user sample, including,

[0025] Sample multiple healthy users and obtain the real-time value of each type of human exhaled gas of the sampled healthy users;

[0026] For any type of human exhaled gas, calculate the average value of the real-time values of all sampled healthy users, and finally obtain the average value of the real-time values of all healthy users of all types of human exhaled gases, and use the average value of the real-time values of all healthy users as the characterization value of this type of human exhaled gas;

[0027] Standardize the characterization values of all human exhaled gases to obtain the standard value of each type of human exhaled gas.

[0028] Further, specifically, calculate the offset coefficient between the real-time value and the standard value of each type of human exhaled gas of the target user to obtain the offset coefficient of each type of human exhaled gas of the target user, including,

[0029] For any human exhaled gas, standardize the real-time value corresponding to the human exhaled gas of the target user to obtain a first value;

[0030] Subtract the standard value of the human exhaled gas from the first value and take the absolute value to obtain the offset coefficient of the human exhaled gas of the target user;

[0031] Calculate the offset coefficient of each type of human exhaled gas of the target user in the above manner.

[0032] Further, specifically, calculate the abnormal risk value according to the offset coefficient of each type of human exhaled gas of the target user and the weight value of this type, including,

[0033] If the offset coefficient of the j-th data type of the target user is Q j , then the calculation of the influenza risk value FX of the target user is as follows,

[0034]

[0035] The present invention also proposes an early warning device for human exhaled gas integrating multiple sensors, including the following:

[0036] An exhalation type determination module, configured to determine all types of human exhaled gas and determine the types of sensors to be arranged according to all types of human exhaled gas;

[0037] A weight value determination module, configured to pre-determine the weight value of each type of human exhaled gas;

[0038] A standard value determination module, configured to determine the standard value of each type of human exhaled gas by analyzing healthy user samples;

[0039] A data acquisition module, configured to acquire the human exhaled gas of the target user and acquire the real-time value of each type of human exhaled gas therein through the arranged multiple sensors;

[0040] An offset coefficient calculation module, configured to calculate the offset coefficient between the real-time value and the standard value of each type of human exhaled gas of the target user to obtain the offset coefficient of each type of human exhaled gas of the target user;

[0041] An abnormal risk value calculation module, configured to calculate the abnormal risk value according to the offset coefficient of each type of human exhaled gas of the target user and the weight value of this type;

[0042] The pre - abnormal warning module is used to determine whether the abnormal risk value is greater than the preset risk threshold. If it is greater than the preset risk threshold, it is determined that there is an abnormal risk. If it is not greater than the preset risk threshold, it is determined that there is no abnormal risk.

[0043] The beneficial effects of the present invention are as follows:

[0044] The present invention provides a warning method and device for human exhaled gas integrating multiple sensors. By obtaining the real - time values of each type of human exhaled gas of the target user, calculating the offset coefficient between each type of human exhaled gas of the target user and the standard value of a healthy user, and according to the pre - determined weight coefficient of each human exhaled gas, the abnormal risk value of the target user is finally calculated. Then, by comparing the abnormal risk value of the target user with the risk threshold, it is intelligently judged whether the user has an abnormal exhalation risk. The present invention can select users who obviously have an abnormal exhalation risk and play a certain preventive role in the health of users. Brief Description of the Drawings

[0045] By describing the embodiments shown in the accompanying drawings in detail, the above - mentioned and other features of the present disclosure will become more obvious. The same reference numerals in the drawings of the present disclosure represent the same or similar elements. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings:

[0046] Figure 1 It shows the flowchart of the warning method for human exhaled gas integrating multiple sensors of the present invention. Detailed Embodiments

[0047] The following will clearly and completely describe the concept, specific structure and technical effects generated by the present invention in combination with the embodiments and the drawings to fully understand the purpose, solution and effects of the present invention. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The same reference numerals used throughout the drawings indicate the same or similar parts.

[0048] Embodiment 1. Refer to Figure 1 , the present invention provides a warning method for human exhaled gas integrating multiple sensors, including the following:

[0049] Step 110: Determine all types of human exhaled gas and determine the types of sensors to be arranged according to all types of human exhaled gas;

[0050] Step 120: Pre - determine the weight values of each type of human exhaled gas;

[0051] Step 130: Determine the standard value of each type of human exhaled gas by analyzing a sample of healthy users;

[0052] Step 140: Obtain the human exhaled gas of the target user, and obtain the real-time value of each type of human exhaled gas therein through the arranged multi-sensors;

[0053] Step 150: Calculate the offset coefficient between the real-time value and the standard value of each type of human exhaled gas of the target user to obtain the offset coefficient of each type of human exhaled gas of the target user;

[0054] Step 160: Calculate the abnormal risk value according to the offset coefficient of each type of human exhaled gas of the target user and the weight value of this type;

[0055] Step 170: Determine whether the abnormal risk value is greater than a preset risk threshold. If it is greater than the preset risk threshold, it is determined that there is an abnormal risk. If it is not greater than the preset risk threshold, it is determined that there is no abnormal risk.

[0056] In this Embodiment 1, by obtaining the real-time value of each type of human exhaled gas of the target user, calculating the offset coefficient between each type of human exhaled gas of the target user and the standard value of healthy users, and according to the pre-determined weight coefficient of each type of human exhaled gas, finally calculate the abnormal risk value of the target user, and then compare the abnormal risk value of the target user with the risk threshold to intelligently determine whether the user has an abnormal exhalation risk. The present invention can select users who obviously have an abnormal exhalation risk and can play a certain preventive role in the health of users.

[0057] As a preferred embodiment of the present invention, specifically, all types of human exhaled gas include carbon dioxide concentration, oxygen concentration, nitrogen, volatile organic compounds, ammonia, ethanol, and exhaled nitric oxide.

[0058] As a preferred embodiment of the present invention, specifically, determining the weight value of each type of human exhaled gas includes,

[0059] If there are m types of human exhaled gas in total;

[0060] Standardize the m types of human exhaled gas to obtain m types of standardized human exhaled gas;

[0061] Calculate the information entropy of each type of human exhaled gas, and its calculation formula is as follows,

[0062]

[0063] where, H j represents the information entropy of the jth type of human exhaled gas, p ijis the proportion of the i-th standardized human exhaled gas in the j-th human exhaled gas, and k is a preset constant;

[0064] Calculate the corresponding weight according to the information entropy of each human exhaled gas, and its calculation formula is as follows,

[0065]

[0066] where, w j represents the weight of the j-th human exhaled gas.

[0067] As a preferred embodiment of the present invention, specifically, determine the standard value of each type of human exhaled gas by analyzing the healthy user samples, including,

[0068] Sample multiple healthy users to obtain the real-time values of each human exhaled gas of the sampled healthy users;

[0069] For any type of human exhaled gas, calculate the average value of the real-time values of all sampled healthy users, and finally obtain the average value of the real-time values of all healthy users of all types of human exhaled gas, and use the average value of the real-time values of all healthy users as the characterization value of this type of human exhaled gas;

[0070] Standardize the characterization values of all human exhaled gases to obtain the standard value of each human exhaled gas.

[0071] As a preferred embodiment of the present invention, specifically, calculate the offset coefficient between the real-time value and the standard value of each type of human exhaled gas of the target user to obtain the offset coefficient of each type of human exhaled gas of the target user, including,

[0072] For any human exhaled gas, standardize the real-time value corresponding to this human exhaled gas of the target user to obtain a first value;

[0073] Subtract the standard value of this human exhaled gas from the first value and take the absolute value to obtain the offset coefficient of this human exhaled gas of the target user;

[0074] Calculate the offset coefficient of each human exhaled gas of the target user in the above manner.

[0075] As a preferred embodiment of the present invention, specifically, calculate the abnormal risk value according to the offset coefficient of each type of human exhaled gas of the target user and the weight value of this type, including,

[0076] If the offset coefficient of the j-th data type of the target user is Q j , then the calculation of the influenza risk value FX of the target user is as follows,

[0077]

[0078] Embodiment 2, the present invention also proposes a warning device for human exhaled gas integrating multiple sensors, including the following:

[0079] An exhalation type determination module, configured to determine all types of human exhaled gas and determine the types of sensors to be arranged according to all types of human exhaled gas;

[0080] A weight value determination module, configured to pre-determine the weight values of each type of human exhaled gas;

[0081] A standard value determination module, configured to determine the standard values of each type of human exhaled gas by analyzing healthy user samples;

[0082] A data acquisition module, configured to acquire the human exhaled gas of a target user and obtain the real-time values of each type of human exhaled gas therein through the arranged multiple sensors;

[0083] An offset coefficient calculation module, configured to calculate the offset coefficient between the real-time value and the standard value of each type of human exhaled gas of the target user, and obtain the offset coefficients of each type of human exhaled gas of the target user;

[0084] An abnormal risk value calculation module, configured to calculate the abnormal risk value according to the offset coefficient and the weight value of each type of human exhaled gas of the target user;

[0085] A pre-warning module, configured to determine whether the abnormal risk value is greater than a preset risk threshold. If it is greater than the preset risk threshold, it is determined that there is an abnormal risk. If it is not greater than the preset risk threshold, it is determined that there is no abnormal risk.

[0086] Although the description of the present invention has been quite detailed and especially describes several of the described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but rather should be regarded as providing a broad interpretation of these claims in light of the prior art by reference to the appended claims, thereby effectively covering the intended scope of the present invention. In addition, the present invention is described above with embodiments foreseeable by the inventor for the purpose of providing a useful description, and those non-substantive modifications to the present invention that are not currently foreseeable may still represent equivalent modifications of the present invention.

[0087] As described above, it is only a preferred embodiment of the present invention. The present invention is not limited to the above-described embodiments. As long as it achieves the technical effects of the present invention by the same means, it should fall within the protection scope of the present invention. Within the protection scope of the present invention, its technical solutions and / or embodiments can have various different modifications and changes.

Claims

1. A warning method for human exhaled gas integrating multiple sensors, characterized in that, including the following: Determine all types of human exhaled gases, and determine the types of sensors to be deployed according to all types of human exhaled gases; Pre-determine the weight values of each type of human exhaled gas; Determine the standard values of each type of human exhaled gas by analyzing healthy user samples; Obtain the human exhaled gases of the target user, and obtain the real-time values of each type of human exhaled gas therein through the deployed multi-sensors; Calculate the offset coefficient between the real-time value and the standard value of each type of human exhaled gas of the target user to obtain the offset coefficient of each type of human exhaled gas of the target user; Calculate the abnormal risk value according to the offset coefficient of each type of human exhaled gas of the target user and the weight value of this type; Judge whether the abnormal risk value is greater than the preset risk threshold. If it is greater than the preset risk threshold, it is judged that there is an abnormal risk. If it is not greater than the preset risk threshold, it is judged that there is no abnormal risk.

2. The early warning method for human exhaled gas integrating multiple sensors according to claim 1, wherein, Specifically, all types of human exhaled gases include carbon dioxide concentration, oxygen concentration, nitrogen, volatile organic compounds, ammonia, ethanol, and exhaled nitric oxide.

3. The early warning method for human exhaled gas integrating multiple sensors according to claim 1, wherein, Specifically, pre-determine the weight values of each type of human exhaled gas, including, If there are m types of human exhaled gases; Standardize the m types of human exhaled gases to obtain the standardized m types of human exhaled gases; Calculate the information entropy of each type of human exhaled gas, and its calculation formula is as follows, Among them, H j represents the information entropy of the j-th type of human exhaled gas, and p ij is the proportion of the i-th type of human exhaled gas after standardization on the j-th type of human exhaled gas, and k is a preset constant; Calculate its corresponding weight according to the information entropy of each type of human exhaled gas, and its calculation formula is as follows, where, w j represents the weight of the j-th type of human exhaled gas.

4. The early warning method for human exhaled gas integrating multiple sensors according to claim 1, characterized in that, Specifically, determine the standard values of each type of human exhaled gas by analyzing healthy user samples, including, Sample multiple healthy users to obtain the real-time values of each type of human exhaled gas of the sampled healthy users; For any type of human exhaled gas, calculate the average value of the real-time values of all sampled healthy users, and finally obtain the average value of the real-time values of all healthy users of all types of human exhaled gases, and use the average value of the real-time values of all healthy users as the representative value of this type of human exhaled gas; Standardize the representative values of all human exhaled gases to obtain the standard values of each type of human exhaled gas.

5. The early warning method for human exhaled gas integrating multiple sensors according to claim 1, characterized in that, Specifically, calculate the offset coefficient between the real-time value and the standard value of each type of human exhaled gas of the target user to obtain the offset coefficient of each type of human exhaled gas of the target user, including, For any human exhaled gas, standardize the real-time value corresponding to this human exhaled gas of the target user to obtain a first value; Subtract the standard value of this human exhaled gas from the first value and take the absolute value to obtain the offset coefficient of this human exhaled gas of the target user; Calculate the offset coefficient of each human exhaled gas of the target user in the above way.

6. The early warning method for human exhaled gas integrating multiple sensors according to claim 1, characterized in that, Specifically, calculate the abnormal risk value according to the offset coefficient of each type of human exhaled gas of the target user and the weight value of this type, including, If the offset coefficient of the j-th data type of the target user is Q j , then the calculation of the influenza risk value FX of the target user is as follows 7. An early warning device for human exhaled gas integrating multiple sensors, characterized in that, including the following: An exhalation type determination module for determining all types of human exhaled gases and determining the types of sensors to be deployed according to all types of human exhaled gases; A weight value determination module for pre-determining the weight values of each type of human exhaled gas; A standard value determination module for determining the standard value of each type of human exhaled gas by analyzing a sample of healthy users; A data acquisition module for acquiring the human exhaled gas of a target user and obtaining the real-time value of each type of human exhaled gas therein through the deployed multi-sensors; An offset coefficient calculation module for calculating the offset coefficient between the real-time value and the standard value of each type of human exhaled gas of the target user to obtain the offset coefficient of each type of human exhaled gas of the target user; An abnormal risk value calculation module for calculating the abnormal risk value according to the offset coefficient of each type of human exhaled gas of the target user and the weight value of this type; A pre-abnormal warning module for determining whether the abnormal risk value is greater than a preset risk threshold. If it is greater than the preset risk threshold, it is determined that there is an abnormal risk. If it is not greater than the preset risk threshold, it is determined that there is no abnormal risk.

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