Human respiratory system risk control early warning method, device and storage medium

Abstract

The present invention discloses a human respiratory system risk control and early warning method, device and storage medium, the method comprising: collecting multiple vital sign data of a user to be detected; determining the high-level duration corresponding to the preset vital sign high-level state of the multiple vital sign data; determining the target respiratory frequency of the user to be detected according to the high-level duration; and conducting a human respiratory system risk control and early warning for the user to be detected according to the high-level duration and the target respiratory frequency. Compared with the existing respiratory system monitoring technology which mainly relies on thermometers, oximeters or imaging technology to diagnose whether the user's individual lungs are infected, the present invention collects multiple vital sign data and target respiratory frequency of the user to be detected in real time through a terminal, realizes accurate acquisition of individual multiple vital sign data and target respiratory frequency, and then conducts a human respiratory system risk control and early warning for the user according to the high-level duration and the target respiratory frequency, thereby facilitating the user to observe physical abnormalities in a timely manner.

Description

Technical Field

[0001] The present invention relates to the field of respiratory system monitoring technology, and in particular to a human respiratory system risk control and early warning method, device and storage medium. Background Art

[0002] With the dramatic changes in the global environment and climate, human respiratory system abnormalities and lung infections occur frequently. Existing respiratory system monitoring technologies mainly rely on thermometers, blood oximeters or imaging technology to diagnose whether a user's individual lungs are infected. The diagnostic data is single-point data, and imaging diagnosis needs to be checked by professionals at a specific location. The diagnostic data is single-point data at a certain moment, and it is impossible to observe the user's respiratory system abnormalities and development trends in a timely manner.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present invention is to provide a human respiratory system risk management and early warning method, device and storage medium, aiming to solve the technical problem of how to accurately obtain individual vital signs data and target respiratory rate while facilitating users to observe abnormal physical conditions in a timely manner.

[0005] To achieve the above-mentioned purpose, the present invention provides a human respiratory system risk control and early warning method, the human respiratory system risk control and early warning method comprising:

[0006] Collecting multiple vital signs data of the user to be detected, wherein the multiple vital signs data include: heart rate, blood oxygen, and body temperature;

[0007] Determine the duration of the high state of the multiple vital signs data corresponding to the preset vital sign high state;

[0008] Determine the target respiratory frequency of the user to be detected according to the high-level duration, wherein the target respiratory frequency is an abnormal respiratory frequency;

[0009] The human respiratory system risk control warning is performed on the user to be detected according to the high-level duration and the target respiratory frequency.

[0010] Optionally, the step of determining a high-level duration corresponding to a preset high-level state of the multiple vital signs data comprises:

[0011] Determining the identity information of the user to be detected;

[0012] Determine a corresponding preset high-level state condition according to the identity information;

[0013] Extracting a plurality of to-be-monitored vital sign data from the plurality of vital sign data according to the preset high-level state condition;

[0014] Determine the high-level duration corresponding to the preset high-level state of the plurality of the to-be-monitored vital sign data.

[0015] Optionally, the step of determining the target respiratory rate of the user to be detected according to the high-level duration includes:

[0016] When the duration of the high level is longer than the preset duration, generating a vital sign curve graph according to the plurality of vital sign data to be monitored;

[0017] The target respiratory rate of the user to be detected is determined according to the trend of the vital signs curve graph.

[0018] Optionally, the step of determining the target respiratory rate of the user to be detected according to the trend of the vital signs curve graph includes:

[0019] When the trend of the vital sign curve graph is in a flat trend or an upward trend, determining a plurality of respiratory rates to be processed according to the plurality of vital sign data to be monitored;

[0020] Determine a corresponding normal breathing frequency according to the identity information;

[0021] Determining whether there is an abnormal respiratory frequency among the plurality of respiratory frequencies to be processed according to the normal respiratory frequency;

[0022] If there is an abnormal respiratory frequency among the multiple respiratory frequencies to be processed, selecting multiple abnormal respiratory frequencies to be processed from the multiple respiratory frequencies to be processed;

[0023] The target breathing frequency of the user to be detected is determined according to the multiple abnormal breathing frequencies to be processed.

[0024] Optionally, after the step of judging whether there is an abnormal respiratory frequency among the plurality of respiratory frequencies to be processed according to the normal respiratory frequency, the method further comprises:

[0025] If there is no abnormal respiratory frequency among the plurality of respiratory frequencies to be processed, determining a health strategy according to the vital sign curve graph and the identity information of the user to be detected;

[0026] Perform emergency treatment on the user to be detected according to the health strategy.

[0027] Optionally, the step of determining the target respiratory rate of the user to be detected according to the trend of the vital signs curve graph further includes:

[0028] When the trend of the vital sign curve graph is in a downward trend, determining a plurality of respiratory rates to be processed according to the plurality of vital sign data to be monitored;

[0029] Selecting the lowest respiratory rate from the plurality of respiratory rates to be processed;

[0030] The lowest breathing frequency is used as the target breathing frequency of the user to be detected.

[0031] Optionally, the step of performing a human respiratory system risk control warning on the user to be detected according to the high-level duration and the target respiratory frequency includes:

[0032] Determining abnormal body state information according to the high-position duration and the target respiratory rate;

[0033] Analyze the multiple vital signs data according to the abnormal body state information to obtain a vital signs abnormality report;

[0034] Based on the abnormal vital sign report, a human respiratory system risk control warning is performed on the user to be detected.

[0035] In addition, to achieve the above-mentioned purpose, the present invention also proposes a human respiratory system risk control and early warning device, the human respiratory system risk control and early warning device comprising:

[0036] A collection module, used to collect multiple vital signs data of the user to be detected, wherein the multiple vital signs data include: heart rate, blood oxygen, and body temperature;

[0037] A determination module, used to determine a high-level duration corresponding to the high-level state of the multiple vital signs data;

[0038] The determination module is further used to determine the target respiratory frequency of the user to be detected according to the high-level duration;

[0039] A control and early warning module is used to carry out a human respiratory system risk control and early warning for the user to be detected according to the high-level duration and the target respiratory frequency.

[0040] In addition, to achieve the above-mentioned purpose, the present invention also proposes a human respiratory system risk control and early warning device, which includes: a memory, a processor, and a human respiratory system risk control and early warning program stored in the memory and executable on the processor, and the human respiratory system risk control and early warning program is configured to implement the steps of the human respiratory system risk control and early warning method as described above.

[0041] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a human respiratory system risk control and early warning program is stored. When the human respiratory system risk control and early warning program is executed by a processor, the steps of the human respiratory system risk control and early warning method as described above are implemented.

[0042] The present invention first collects multiple vital sign data of the user to be detected, wherein the multiple vital sign data include: heart rate, blood oxygen, body temperature, and then determines the high-level duration corresponding to the preset vital sign high-level state of the multiple vital sign data, and determines the target respiratory frequency of the user to be detected according to the high-level duration, and then performs human respiratory system risk control warning for the user to be detected according to the high-level duration and the target respiratory frequency. Compared with the existing respiratory system monitoring technology, which mainly relies on thermometers, oximeters or imaging technology to diagnose whether the user's individual lungs are infected, the diagnostic data is single-point data, and the imaging diagnosis needs to be checked by professionals at a specific location. The diagnostic data is a single-point data at a certain moment, and it is impossible to observe the abnormal conditions and development trends of the user's respiratory system in time, and thus it is impossible to take corresponding treatment measures. In the present invention, the multiple vital sign data of the user to be detected are collected in real time by the terminal and the target respiratory frequency is determined, so that the changes and development trends of the target user's respiratory system are accurately monitored, which is convenient for the user to observe the abnormal conditions of the body in time, thereby improving the user's experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a structural diagram of a human respiratory system risk control and early warning device in a hardware operating environment involved in an embodiment of the present invention;

[0044] Figure 2 This is a flow chart of the first embodiment of the human respiratory system risk control and early warning method of the present invention;

[0045] Figure 3 It is a flow chart of the second embodiment of the human respiratory system risk control and early warning method of the present invention;

[0046] Figure 4 It is a flow chart of the third embodiment of the human respiratory system risk control and early warning method of the present invention;

[0047] Figure 5 This is a structural block diagram of the first embodiment of the human respiratory system risk management and warning device of the present invention.

[0048] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0049] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0050] Reference Figure 1 , Figure 1 It is a schematic diagram of the structure of the human respiratory system risk control and early warning device in the hardware operating environment involved in the embodiment of the present invention.

[0051] like Figure 1 As shown, the human respiratory system risk control and early warning device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (RandomAccess Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0052] Those skilled in the art will understand that Figure 1 The structure shown in does not constitute a limitation on the human respiratory system risk management and warning device, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0053] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a human respiratory system risk management and control early warning program.

[0054] exist Figure 1 In the human respiratory system risk control and early warning device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the human respiratory system risk control and early warning device of the present invention can be set in the human respiratory system risk control and early warning device, and the human respiratory system risk control and early warning device calls the human respiratory system risk control and early warning program stored in the memory 1005 through the processor 1001, and executes the human respiratory system risk control and early warning method provided in the embodiment of the present invention.

[0055] The embodiment of the present invention provides a human respiratory system risk control early warning method, referring to Figure 2 , Figure 2 It is a flow chart of the first embodiment of the human respiratory system risk control and early warning method of the present invention.

[0056] In this embodiment, the human respiratory system risk management and early warning method includes the following steps:

[0057] Step S10: Collect multiple vital signs data of the user to be detected, wherein the multiple vital signs data include: heart rate, blood oxygen, and body temperature.

[0058] It is easy to understand that the executor of this embodiment can be a human respiratory system risk control and early warning device with functions such as data processing, network communication and program running, such as a smart wearable watch, smart wearable clothing or smart home, etc. This embodiment does not limit this. This embodiment is illustrated by taking a smart wearable watch as an example, wherein a medical-grade heart rate sensor is stored in the human respiratory system risk control and early warning device. The human respiratory system risk control and early warning device can also be understood as a smart wearable watch with a built-in human respiratory system risk control and early warning function. The smart wearable watch acquisition terminal can collect and monitor multiple vital signs data of the human body in real time, and provide convenient health management data reference for patients with respiratory diseases, etc. This embodiment does not limit this.

[0059] It should also be noted that the user to be tested can be a user wearing a smart wearable watch, and the multiple vital signs data can be heart rate data, blood oxygen data, body temperature data, etc. collected in real time using medical-grade sensors.

[0060] Step S20: determining a high-level duration corresponding to a preset high-level state of the multiple vital signs data.

[0061] It should be understood that the preset high vital sign state is a state in which the individual's multiple vital sign data is higher than the normal multiple vital sign data, and the high state duration can be the continuous duration in which the individual's multiple vital sign data is higher than the normal multiple vital sign data. The high state duration can be 36 hours, 24 hours, 48 ​​hours, etc.

[0062] In the specific implementation, since the normal multiple vital signs data corresponding to users of different age groups are different, in order to accurately monitor the users to be detected, the processing method for determining the high-level duration corresponding to the preset vital sign high-level state of the multiple vital signs data can be to determine the identity information of the user to be detected, and then determine the corresponding preset high-level state conditions based on the identity information, and extract multiple vital signs data to be monitored from the multiple vital signs data based on the preset high-level state conditions, and determine the high-level duration corresponding to the preset vital signs high-level state of the multiple vital signs data to be monitored.

[0063] It should be noted that the identity information of the user to be detected may be the age, historical health information, etc. of the user to be detected, and the preset high-level state condition may be abnormal multi-vital sign data corresponding to the user to be detected, which is different from normal multi-vital sign data. Assuming that the respiratory rate of a newborn is 40 to 45 times / minute; the respiratory rate of a child aged 1 month to 1 year is about 30 times / minute; the respiratory rate of a child aged 1 to 3 years is about 24 times / minute; the respiratory rate of a child aged 4 to 7 years is 20 to 25 times / minute; the respiratory rate of a child aged 8 to 14 years is about 20 times / minute, then the respiratory rate of the preset high-level state condition corresponding to the newborn is 45 times / minute or more, the respiratory rate of the preset high-level state condition of a child aged 1 month to 1 year is 30 times / minute or more; the respiratory rate of the preset high-level state condition of a child aged 1 to 3 years is 24 times / minute or more, etc.

[0064] In this embodiment, it is assumed that the identity information of the user to be detected is a healthy adult, and multiple vital sign data corresponding to the healthy adult are collected through the smart wearable watch. If the heart rate data is 75, 80, 90, 110, and 110 per minute within 1-5 minutes, respectively, the blood oxygen data is 98, 96, 94, 93, and 92 per minute within 1-5 minutes, respectively, and the body temperature data is 37.5° within 5 minutes, then the preset high-level state condition corresponding to the adult is that the heart rate data is above 90, the blood oxygen data is below 94, and the body temperature data is above 37.3°. Then, according to the preset high-level state condition, multiple vital sign data to be monitored are extracted from the multiple vital sign data, wherein the multiple vital sign data to be monitored include heart rate data of 110 at the 4th minute and 110 at the 5th minute, blood oxygen data of 93 at the 4th minute and 92 at the 5th minute, body temperature data of 37.5° within 5 minutes, and the high-level duration corresponding to the vital sign data to be monitored is 2 minutes (i.e., the 4th minute and the 5th minute), etc.

[0065] Step S30: determining the target respiratory rate of the user to be detected according to the duration of the high level.

[0066] It should be noted that when the high-level duration is detected to reach the preset duration, in order to accurately observe the user's physical condition, a vital sign curve chart can be generated based on multiple vital sign data to be monitored, and then the target respiratory rate of the user to be detected can be determined based on the trend of the vital sign curve chart. The preset duration can be customized by the user and can be 36h, 24h, 48h, etc.

[0067] It should be understood that since the multiple vital signs data to be monitored include heart rate data, blood oxygen data, and body temperature data, it is necessary to generate a heart rate curve, a blood oxygen curve, and a body temperature curve according to the multiple vital signs data to be monitored, and then generate a vital signs curve chart according to the heart rate curve, blood oxygen curve, and body temperature curve.

[0068] In this embodiment, the trends of the heart rate curve, blood oxygen curve and body temperature curve can be observed according to the vital signs curve graph, including an upward trend, a downward trend or a flat trend, etc. When the heart rate curve in the vital signs graph is in a flat trend, the blood oxygen curve is in a flat trend and the body temperature curve is in a flat trend, it is determined that the vital signs curve graph is in a flat trend; when the heart rate curve in the vital signs graph is in an upward trend, the blood oxygen curve is in a flat trend or a downward trend, and the body temperature curve is in a flat trend or an upward trend, it is determined that the vital signs curve graph is in an upward trend; when the heart rate curve in the vital signs graph is in a downward trend, the blood oxygen curve is in a downward trend and the body temperature curve is in a downward trend, it is determined that the vital signs curve graph is in a downward trend, etc.

[0069] It should also be understood that when the trend of the vital signs curve graph is in a flat trend or an upward trend, the target breathing frequency of the user to be detected can be determined based on the multiple respiratory frequencies to be processed corresponding to the time period of the vital signs data to be monitored, and the user's respiratory system can be judged whether it is abnormal based on the target breathing frequency; when the vital signs curve graph is in a downward trend, the lowest breathing frequency can be used as the target breathing frequency of the user to be detected, and the target breathing frequency can be compared with the bradyrespiration frequency range. When the target breathing frequency is in the bradyrespiration frequency range for a period of time, it is determined that the user's respiratory system is abnormal at this time, and the user's physical condition is further detected to determine the type of abnormality in the user's respiratory system.

[0070] Step S40: providing a human respiratory system risk control warning to the user to be detected according to the high-level duration and the target respiratory frequency.

[0071] It should also be noted that in order to accurately carry out risk control and early warning of the human respiratory system, the abnormal body state information can be determined based on the duration of high pressure and the target respiratory rate. Then, the multiple vital signs data can be analyzed based on the abnormal body state information to obtain an abnormal vital signs report and carry out risk control and early warning of the human respiratory system.

[0072] In the specific implementation, if high heart rate, low blood oxygen, high body temperature persists for more than 36 hours, and the respiratory rate exceeds the normal value, it is judged as an abnormal respiratory infection, that is, abnormal body status information. Users monitor and manage according to the abnormal body status information and multiple vital signs data to provide a reference for doctors' treatment. Afterwards, they can analyze the multiple vital signs data according to the abnormal body status information, obtain a vital signs abnormality report, and take corresponding measures according to the vital signs abnormality report to seek medical treatment in time, prevent chronic obstructive pulmonary disease, and reduce the occurrence of bronchitis and pneumonia. At the same time, it provides convenient health management data reference for patients with respiratory diseases.

[0073] In this embodiment, the smart wearable watch collection terminal can also collect human vital signs data, heart rate data, blood oxygen data, and body temperature data when the user is sleeping at night. When the user's respiratory rate is monitored to be in the bradyrespiratory range for a period of time, it is determined that the user's respiratory system is abnormal and an early warning can be issued. For example, the range of bradyrespiratory range is less than or equal to 12 times / minute, and the duration can be 1min, 3min, etc. This embodiment does not limit this. In a specific implementation, a control and early warning module can be set in the smart wearable watch. The control and early warning module can be connected to the user's mobile phone or establish a connection with the user's guardian (doctor). The control and early warning module can store the contact information of the user or the user's guardian (doctor). When the user's respiratory system is abnormal, the control and early warning module can call the user's mobile phone or the user's guardian (doctor) mobile phone to remind and wake up the user in time. By issuing an early warning of the user's respiratory frequency, the user's safety risk events can be avoided.

[0074] In this embodiment, the multiple vital signs data of the user to be detected are first collected, and then the high-level duration corresponding to the preset vital sign high-level state of the multiple vital signs data is determined, and the target respiratory frequency of the user to be detected is determined according to the high-level duration, and then the human respiratory system risk control warning is given to the user to be detected according to the high-level duration and the target respiratory frequency. Compared with the existing respiratory system monitoring technology, which mainly relies on thermometers, oximeters or imaging technology to diagnose whether the user's individual lungs are infected, the diagnostic data is single-point data, and the imaging diagnosis needs to be checked by professionals at a specific location. The diagnostic data is single-point data at a certain moment, and it is impossible to observe the abnormal conditions and development trends of the user's respiratory system in time, and thus it is impossible to take corresponding treatment measures. In this embodiment, the multiple vital signs data of the user to be detected are collected in real time by the terminal and the target respiratory frequency is determined, so that the changes and development trends of the target user's respiratory system can be accurately monitored, which is convenient for the user to observe the abnormal conditions of the body in time, thereby improving the user's experience.

[0075] refer to Figure 3 , Figure 3 This is a flow chart of the second embodiment of the human respiratory system risk control and early warning method of the present invention.

[0076] Based on the above first embodiment, in this embodiment, the step S20 further includes:

[0077] Step S201: Determine the identity information of the user to be detected.

[0078] It should also be noted that when the user to be detected uses the smart wearable watch to monitor multiple vital signs data, the identity information of the user to be detected needs to be set in advance in the smart wearable watch, so that the smart wearable watch can manage the multiple vital signs data according to the identity information of the user to be detected. For example, the age, historical health status and other information of the user to be detected can determine different preset high-level state conditions according to the user's identity information. For example, if the user suffers from heart disease, the preset high-level state condition can be set to be 10% higher than the normal heart rate value or 10% lower than the normal heart rate value, the blood oxygen data is lower than the normal blood oxygen value, the body temperature data is higher than the normal body temperature value, etc. If the user's historical health information is good, the preset high-level state condition can be set to be higher than the normal heart rate value, the blood oxygen data is lower than the normal blood oxygen value, the body temperature data is higher than the normal body temperature value, etc., and the specific preset high-level state condition can be set according to the identity information filled in by the user, and this embodiment does not limit this.

[0079] Step S202: determining a corresponding preset high-level status condition according to the identity information.

[0080] It should be understood that the preset high-level state condition can be abnormal multiple vital signs data corresponding to the user to be detected, and the abnormal multiple vital signs data is different from the normal multiple vital signs data. The preset high-level state condition can be understood as the heart rate is higher than the preset heart rate value, the blood oxygen is lower than the preset blood oxygen value, and the body temperature is higher than the preset body temperature value. The preset heart rate value, the preset blood oxygen value, the preset body temperature value, and the preset respiratory rate value can be set and modified according to the user's identity information, and this embodiment does not limit this. For example, the preset high-level state condition for a healthy adult user can be understood as a heart rate data of more than 90, a blood oxygen data of less than 94, a body temperature data of more than 37.3°, etc.

[0081] Assuming that the normal respiratory rate of a newborn is 40 to 45 times / minute; the normal respiratory rate of a child aged 1 month to 1 year is about 30 times / minute; the normal respiratory rate of a child aged 1 to 3 years is about 24 times / minute; the normal respiratory rate of a child aged 4 to 7 years is 20 to 25 times / minute; the normal respiratory rate of a child aged 8 to 14 years is about 20 times / minute, then the corresponding preset high-state condition for a newborn is a respiratory rate of more than 45 times / minute, the preset high-state condition for a child aged 1 month to 1 year is a respiratory rate of more than 30 times / minute; the preset high-state condition for a child aged 1 to 3 years is a respiratory rate of more than 25 times / minute, etc.

[0082] Step S203: extracting a plurality of to-be-monitored vital sign data from the plurality of vital sign data according to the preset high-level state condition.

[0083] In this embodiment, it is assumed that the identity information of the user to be detected is a healthy adult, and multiple vital signs data corresponding to the adult are collected through the smart wearable watch. If the heart rate data is 75, 80, 90, 110, and 110 per minute within 1-5 minutes, the blood oxygen data is 98, 96, 94, 93, and 92 per minute within 1-5 minutes, and the body temperature data is 37.5° within 5 minutes, then the preset high-level state condition corresponding to the adult is that the heart rate data is above 90, the blood oxygen data is below 94, and the body temperature data is above 37.3°. Then, according to the preset high-level state condition, multiple vital signs data to be monitored are extracted from the multiple vital signs data, wherein the multiple vital signs data to be monitored include heart rate data of 110 and 110, blood oxygen data of 93 and 92, and body temperature data of 37.5°.

[0084] Step S204: determining a high-level duration corresponding to a preset high-level state of a plurality of the to-be-monitored vital sign data.

[0085] It should also be noted that since the multiple vital signs data to be monitored include heart rate data, blood oxygen data, and body temperature data, the time corresponding to the time when the heart rate data, blood oxygen data, and body temperature data all meet the preset high-level state conditions can be counted, and then the high-level duration corresponding to the heart rate data, blood oxygen data, and body temperature data being in the preset vital sign high-level state needs to be counted.

[0086] In a specific implementation, assuming that the central frequency data of multiple vital signs to be monitored is 110 at the 4th minute and 110 at the 5th minute, the blood oxygen data is 94 at the 4th minute and 93 at the 5th minute, and the body temperature data is 37.5° within 5 minutes, then the high-level duration corresponding to the vital signs data to be monitored is 2 minutes (i.e., the 4th minute and the 5th minute).

[0087] In this embodiment, the identity information of the user to be detected is first determined, and then the corresponding preset high-level state condition is determined based on the identity information, and multiple vital sign data to be monitored are extracted from the multiple vital sign data based on the preset high-level state condition, and then the high-level duration corresponding to the preset vital sign high-level state of the multiple vital sign data to be monitored is determined. The existing respiratory system monitoring technology mainly relies on thermometers, blood oximeters or imaging technology to diagnose whether the user's individual lungs are infected. The diagnostic data is single-point data. At this time, the multiple vital sign data obtained are data at a certain moment, and the problem of discontinuity of the multiple vital sign data will occur, so that the physical abnormality and development trend of the user to be detected cannot be accurately analyzed. In this embodiment, not only can the multiple vital sign data to be monitored be accurately determined based on the identity information of the user to be detected, but also the high-level duration corresponding to the preset vital sign high-level state of the multiple vital sign data to be monitored can be determined, so that it is convenient for the user to accurately analyze the physical abnormality based on the multiple vital sign data.

[0088] refer to Figure 4 , Figure 4 It is a flow chart of the third embodiment of the human respiratory system risk control and early warning method of the present invention.

[0089] Based on the above second embodiment, in this embodiment, the step S30 further includes:

[0090] Step S301: when the duration of the high level is greater than a preset duration, a vital sign curve chart is generated according to the plurality of vital sign data to be monitored.

[0091] It should be understood that since the multiple vital signs data to be monitored include heart rate data, blood oxygen data, and body temperature data, it is necessary to generate a heart rate curve, a blood oxygen curve, and a body temperature curve according to the multiple vital signs data to be monitored, and then generate a vital signs curve chart according to the heart rate curve, blood oxygen curve, and body temperature curve.

[0092] Step S302: determining the target respiratory rate of the user to be detected according to the trend of the vital signs curve graph.

[0093] In this embodiment, the trends of the heart rate curve, blood oxygen curve and body temperature curve can be observed according to the vital signs curve graph, including an upward trend, a downward trend or a flat trend, etc. When the heart rate curve in the vital signs graph is in a flat trend, the blood oxygen curve is in a flat trend and the body temperature curve is in a flat trend, it is determined that the vital signs curve graph is in a flat trend; when the heart rate curve in the vital signs graph is in an upward trend, the blood oxygen curve is in a flat trend or a downward trend, and the body temperature curve is in a flat trend or an upward trend, it is determined that the vital signs curve graph is in an upward trend; when the heart rate curve in the vital signs graph is in a downward trend, the blood oxygen curve is in a downward trend and the body temperature curve is in a downward trend, it is determined that the vital signs curve graph is in a downward trend, etc.

[0094] It should also be noted that when the trend of the vital signs curve chart is in a flat trend or an upward trend, it is determined that the high heart rate, low blood oxygen, and high body temperature to be detected continue for more than the preset duration of time at a high level, and it is further necessary to determine whether the user has a respiratory infection. The judgment method can be to determine multiple respiratory frequencies to be processed based on multiple vital signs data to be monitored, and determine the corresponding normal respiratory frequency based on the identity information, and determine whether there is an abnormal respiratory frequency among the multiple respiratory frequencies to be processed based on the normal respiratory frequency. If there is an abnormal respiratory frequency among the multiple respiratory frequencies to be processed, it means that the user to be detected may have a respiratory infection. If there is no abnormal respiratory frequency among the multiple respiratory frequencies to be processed, it means that the user to be detected does not have a respiratory infection, and it is necessary to determine the health strategy based on the vital signs curve chart and the identity information of the user to be detected, and then perform emergency treatment on the user to be detected according to the health strategy.

[0095] It should be understood that the health strategy may be a disease treatment measure formulated by a doctor based on the vital signs curve and the identity information of the user to be tested, and the user to be tested may perform emergency treatment according to the health strategy in an emergency.

[0096] It should also be noted that when the trend of the vital signs curve graph is on a downward trend, it means that the user may have respiratory system abnormalities at this time. Then, multiple time periods corresponding to the vital signs data to be monitored can be collected to determine the corresponding multiple respiratory frequencies to be processed, wherein the time for collecting the respiratory frequencies to be processed is consistent with the time for collecting the vital signs data to be monitored. Then, the lowest respiratory frequency is selected from the multiple respiratory frequencies to be processed, and the lowest respiratory frequency is used as the target respiratory frequency of the user to be detected. The target respiratory frequency is compared with the bradyrespiratory frequency range. When the target respiratory frequency is in the bradyrespiratory frequency range for a period of time, it is determined that the user's respiratory system is abnormal at this time. The user's physical condition can be further detected to determine the type of abnormality of the user's respiratory system.

[0097] In this embodiment, when the high duration is greater than the preset duration, a vital sign graph is generated based on multiple vital sign data to be monitored, and then the target respiratory rate of the user to be detected is determined based on the trend of the vital sign graph. Compared with the prior art, which only relies on thermometers, blood oximeters or imaging technology to diagnose whether the user's individual lungs are infected, the diagnostic data is single-point data. In this embodiment, when the high duration is greater than the preset duration, it can be determined that the user has a high heart rate, low blood oxygen and high body temperature, and then the target respiratory rate of the user to be detected can be determined based on the trend of the vital sign graph, thereby achieving accurate monitoring of changes and development trends in the target user's respiratory system, making it convenient for users to observe physical abnormalities in a timely manner.

[0098] In addition, an embodiment of the present invention also proposes a storage medium, on which a human respiratory system risk control and early warning program is stored. When the human respiratory system risk control and early warning program is executed by a processor, the steps of the human respiratory system risk control and early warning method as described above are implemented.

[0099] Reference Figure 5 , Figure 5 This is a structural block diagram of the first embodiment of the human respiratory system risk management and warning device of the present invention.

[0100] like Figure 5 As shown, the human respiratory system risk control and early warning device proposed in the embodiment of the present invention includes:

[0101] The collection module 5001 is used to collect multiple vital sign data of the user to be detected.

[0102] It is easy to understand that the executor of this embodiment can be a human respiratory system risk control and early warning device with functions such as data processing, network communication and program running, such as a smart wearable watch, smart wearable clothing or smart home, etc. This embodiment does not limit this. This embodiment is illustrated by taking a smart wearable watch as an example, wherein a medical-grade heart rate sensor is stored in the human respiratory system risk control and early warning device. The human respiratory system risk control and early warning device can also be understood as a smart wearable watch with a built-in human respiratory system risk control and early warning function. The smart wearable watch acquisition terminal can collect and monitor multiple vital signs data of the human body in real time, and provide convenient health management data reference for patients with respiratory diseases, etc. This embodiment does not limit this.

[0103] It should also be noted that the user to be tested can be a user wearing a smart wearable watch, and the multiple vital signs data can be heart rate data, blood oxygen data, body temperature data, etc. collected in real time using medical-grade sensors.

[0104] The determination module 5002 is used to determine the high-level duration corresponding to the preset high-level state of the multiple vital signs data.

[0105] It should be understood that the preset high vital sign state is a state in which the individual's multiple vital sign data is higher than the normal multiple vital sign data, and the high state duration can be the continuous duration in which the combined multiple vital sign data is higher than the normal multiple vital sign data. The high state duration can be 36 hours, or 24 hours, 48 ​​hours, etc.

[0106] In the specific implementation, since the normal multiple vital signs data corresponding to users of different age groups are different, in order to accurately monitor the users to be detected, the processing method for determining the high-level duration corresponding to the preset vital sign high-level state of the multiple vital signs data can be to determine the identity information of the user to be detected, and then determine the corresponding preset high-level state conditions based on the identity information, and extract multiple vital signs data to be monitored from the multiple vital signs data based on the preset high-level state conditions, and determine the high-level duration corresponding to the preset vital signs high-level state of the multiple vital signs data to be monitored.

[0107] It should be noted that the identity information of the user to be detected may be the age, historical health information, etc. of the user to be detected, and the preset high-level state condition may be abnormal multi-vital sign data corresponding to the user to be detected, which is different from normal multi-vital sign data. Assuming that the respiratory rate of a newborn is 40 to 45 times / minute; the respiratory rate of a child aged 1 month to 1 year is about 30 times / minute; the respiratory rate of a child aged 1 to 3 years is about 24 times / minute; the respiratory rate of a child aged 4 to 7 years is 20 to 25 times / minute; the respiratory rate of a child aged 8 to 14 years is about 20 times / minute, then the respiratory rate of the preset high-level state condition corresponding to the newborn is 45 times / minute or more, the respiratory rate of the preset high-level state condition of a child aged 1 month to 1 year is 30 times / minute or more; the respiratory rate of the preset high-level state condition of a child aged 1 to 3 years is 24 times / minute or more, etc.

[0108] In this embodiment, it is assumed that the identity information of the user to be detected is a healthy adult, and multiple vital sign data corresponding to the healthy adult are collected through the smart wearable watch. If the heart rate data is 75, 80, 90, 110, and 110 per minute within 1-5 minutes, respectively, the blood oxygen data is 98, 96, 94, 93, and 92 per minute within 1-5 minutes, respectively, and the body temperature data is 37.5° within 5 minutes, then the preset high-level state condition corresponding to the adult is that the heart rate data is above 90, the blood oxygen data is below 94, and the body temperature data is above 37.3°. Then, according to the preset high-level state condition, multiple vital sign data to be monitored are extracted from the multiple vital sign data, wherein the multiple vital sign data to be monitored include heart rate data of 110 at the 4th minute and 110 at the 5th minute, blood oxygen data of 93 at the 4th minute and 92 at the 5th minute, body temperature data of 37.5° within 5 minutes, and the high-level duration corresponding to the vital sign data to be monitored is 2 minutes (i.e., the 4th minute and the 5th minute), etc.

[0109] The determination module 5002 is further configured to determine the target respiratory frequency of the user to be detected according to the high-level duration.

[0110] It should be noted that when the high-level duration is detected to reach the preset duration, in order to accurately observe the user's physical condition, a vital sign curve chart can be generated based on multiple vital sign data to be monitored, and then the target respiratory rate of the user to be detected can be determined based on the trend of the vital sign curve chart. The preset duration can be customized by the user and can be 36h, 24h, 48h, etc.

[0111] It should be understood that since the multiple vital signs data to be monitored include heart rate data, blood oxygen data, and body temperature data, it is necessary to generate a heart rate curve, a blood oxygen curve, and a body temperature curve according to the multiple vital signs data to be monitored, and then generate a vital signs curve chart according to the heart rate curve, blood oxygen curve, and body temperature curve.

[0112] In this embodiment, the trends of the heart rate curve, blood oxygen curve and body temperature curve can be observed according to the vital signs curve graph, including an upward trend, a downward trend or a flat trend, etc. When the heart rate curve in the vital signs graph is in a flat trend, the blood oxygen curve is in a flat trend and the body temperature curve is in a flat trend, it is determined that the vital signs curve graph is in a flat trend; when the heart rate curve in the vital signs graph is in an upward trend, the blood oxygen curve is in a flat trend or a downward trend, and the body temperature curve is in a flat trend or an upward trend, it is determined that the vital signs curve graph is in an upward trend; when the heart rate curve in the vital signs graph is in a downward trend, the blood oxygen curve is in a downward trend and the body temperature curve is in a downward trend, it is determined that the vital signs curve graph is in a downward trend, etc.

[0113] It should also be understood that when the trend of the vital signs curve graph is in a flat trend or an upward trend, the target breathing frequency of the user to be detected can be determined based on the multiple respiratory frequencies to be processed corresponding to the time period of the vital signs data to be monitored, and the user's respiratory system can be judged whether it is abnormal based on the target breathing frequency; when the vital signs curve graph is in a downward trend, the lowest breathing frequency can be used as the target breathing frequency of the user to be detected, and the target breathing frequency can be compared with the bradyrespiration frequency range. When the target breathing frequency is in the bradyrespiration frequency range for a period of time, it is determined that the user's respiratory system is abnormal at this time, and the user's physical condition can be further detected to determine the type of abnormality in the user's respiratory system.

[0114] The control and early warning module 5003 is used to perform a human respiratory system risk control and early warning on the user to be detected according to the high-level duration and the target respiratory frequency.

[0115] It should also be noted that in order to accurately carry out risk control and early warning of the human respiratory system, the abnormal body state information can be determined based on the duration of high pressure and the target respiratory rate. Then, the multiple vital signs data can be analyzed based on the abnormal body state information to obtain an abnormal vital signs report and carry out risk control and early warning of the human respiratory system.

[0116] In the specific implementation, if high heart rate, low blood oxygen, high body temperature persists for more than 36 hours, and the respiratory rate exceeds the normal value, it is judged as an abnormal respiratory infection, that is, abnormal body status information. Users monitor and manage according to the abnormal body status information and multiple vital signs data to provide a reference for doctors' treatment. Afterwards, they can analyze the multiple vital signs data according to the abnormal body status information, obtain a vital signs abnormality report, and take corresponding measures according to the vital signs abnormality report to seek medical treatment in time, prevent chronic obstructive pulmonary disease, and reduce the occurrence of bronchitis and pneumonia. At the same time, it provides convenient health management data reference for patients with respiratory diseases.

[0117] In a specific implementation, the control and warning module 5003 can be connected to the user's mobile phone or establish a connection with the user's guardian (doctor), and the control and warning module can store the contact information of the user or the user's guardian (doctor). When the user's respiratory rate is monitored to be in the bradyrespiratory rate range for a period of time, it is determined that the user's respiratory system is abnormal and an early warning can be issued. The control and warning module can call the user's mobile phone or the user's guardian (doctor) mobile phone to remind the user and wake up the user in time to avoid the occurrence of health and safety risk events for the user.

[0118] In this embodiment, the multiple vital signs data of the user to be detected are first collected, and then the high-level duration corresponding to the preset vital sign high-level state of the multiple vital signs data is determined, and the target respiratory frequency of the user to be detected is determined according to the high-level duration, and then the human respiratory system risk control warning is given to the user to be detected according to the high-level duration and the target respiratory frequency. Compared with the existing respiratory system monitoring technology, which mainly relies on thermometers, oximeters or imaging technology to diagnose whether the user's individual lungs are infected, the diagnostic data is single-point data, and the imaging diagnosis needs to be checked by professionals at a specific location. The diagnostic data is single-point data at a certain moment, and it is impossible to observe the abnormal conditions and development trends of the user's respiratory system in time, and thus it is impossible to take corresponding treatment measures. In this embodiment, the multiple vital signs data and target respiratory frequency of the user to be detected are collected in real time by the terminal, so as to achieve accurate monitoring of the changes and development trends of the target user's respiratory system, which is convenient for the user to observe the abnormal conditions of the body in time, thereby improving the user's experience.

[0119] Other embodiments or specific implementations of the human respiratory system risk control and early warning device of the present invention can refer to the above-mentioned method embodiments, which will not be repeated here.

[0120] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0121] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0122] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0123] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A human respiratory system risk management and early warning method, characterized in that: The human respiratory system risk control and early warning method comprises the following steps: Collecting multiple vital sign data of the user to be tested, wherein the multiple vital sign data include: heart rate, blood oxygen, and body temperature; Determining a duration of time during which the heart rate, blood oxygen, and body temperature in the multiple vital sign data are at a high level corresponding to a preset high vital sign state, wherein the preset high vital sign state is a state in which the individual's multiple vital sign data is higher than normal multiple vital sign data; Determining a target respiratory frequency of the user to be detected according to the duration of the high level, wherein the target respiratory frequency is an abnormal respiratory frequency; Performing a human respiratory system risk control warning on the user to be detected based on the high-pressure duration and the target respiratory rate; The step of determining the duration of time that the heart rate, blood oxygen, and body temperature in the multi-vital sign data are in a high state corresponding to a preset high state of vital signs includes: Determining the identity information of the user to be detected; Determining a corresponding preset high-level state condition according to the identity information; Extracting a plurality of to-be-monitored vital sign data from the heart rate, blood oxygen and body temperature in the plurality of vital sign data according to the preset high-level state condition; Determining a high-level duration corresponding to a preset high-level state of a plurality of the to-be-monitored vital sign data; The step of determining the target respiratory rate of the user to be detected according to the high-level duration includes: When the duration of the high level is longer than the preset duration, generating a vital sign curve graph according to the plurality of vital sign data to be monitored; The target respiratory rate of the user to be detected is determined according to the trend of the vital signs curve graph.

2. The method according to claim 1, wherein The step of determining the target respiratory rate of the user to be detected according to the trend of the vital signs curve graph includes: When the trend of the vital sign curve graph is in a flat trend or an upward trend, determining a plurality of respiratory rates to be processed according to the plurality of vital sign data to be monitored; determining a corresponding normal respiratory rate according to the identity information; Determining whether there is an abnormal respiratory frequency among the plurality of respiratory frequencies to be processed according to the normal respiratory frequency; If there is an abnormal respiratory frequency among the plurality of respiratory frequencies to be processed, selecting a plurality of abnormal respiratory frequencies to be processed from the plurality of respiratory frequencies to be processed; The target respiratory frequency of the user to be detected is determined according to the multiple abnormal respiratory frequencies to be processed.

3. The method according to claim 2, wherein After the step of determining whether there is an abnormal respiratory frequency among the plurality of respiratory frequencies to be processed according to the normal respiratory frequency, the method further includes: If there is no abnormal respiratory frequency among the plurality of respiratory frequencies to be processed, determining a health strategy according to the vital sign curve graph and the identity information of the user to be detected; Perform emergency treatment on the user to be detected according to the health policy.

4. The method according to claim 1, wherein The step of determining the target respiratory rate of the user to be detected according to the trend of the vital signs curve graph further includes: When the trend of the vital sign curve graph is in a downward trend, determining a plurality of respiratory rates to be processed according to the plurality of vital sign data to be monitored; Selecting the lowest respiratory rate from the plurality of respiratory rates to be processed; The lowest respiratory rate is used as the target respiratory rate of the user to be detected.

5. The method according to any one of claims 1 to 4, characterized in that The step of performing a human respiratory system risk control warning on the user to be detected based on the high-level duration and the target respiratory rate includes: Determining abnormal body state information according to the high-pressure duration and the target respiratory rate; Analyzing the multiple vital signs data according to the abnormal body state information to obtain a vital signs abnormality report; Based on the abnormal vital sign report, a human respiratory system risk control warning is performed on the user to be detected.

6. A human respiratory system risk control and early warning device, characterized in that: The human respiratory system risk control and early warning device includes: An acquisition module is used to acquire multiple vital sign data of the user to be detected, wherein the multiple vital sign data include: heart rate, blood oxygen, and body temperature; a determination module, configured to determine a duration of time during which the heart rate, blood oxygen, and body temperature in the multi-vital sign data are at a high level corresponding to a preset high vital sign state, wherein the preset high vital sign state is a state in which the multi-vital sign data of an individual is higher than normal multi-vital sign data; The determining module is further configured to determine a target respiratory frequency of the user to be detected based on the duration of the high level, wherein the target respiratory frequency is an abnormal respiratory frequency; A control and warning module is used to perform a human respiratory system risk control and warning on the user to be detected based on the high-level duration and the target respiratory rate; The determination module is further configured to determine the identity information of the user to be detected; determine a corresponding preset high-level state condition based on the identity information; extract a plurality of to-be-monitored vital sign data from the heart rate, blood oxygen, and body temperature in the multiple vital sign data based on the preset high-level state condition; and determine a high-level duration corresponding to the preset high-level state of the plurality of to-be-monitored vital sign data. The determination module is further configured to generate a vital sign curve chart based on the plurality of vital sign data to be monitored when the high level duration is greater than a preset duration; and determine the target respiratory rate of the user to be detected based on the trend of the vital sign curve chart.

7. A human respiratory system risk control and early warning device, characterized in that: The device includes: a memory, a processor, and a human respiratory system risk control and early warning program stored in the memory and executable on the processor, wherein the human respiratory system risk control and early warning program is configured to implement the steps of the human respiratory system risk control and early warning method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that: The storage medium stores a human respiratory system risk control and early warning program, which, when executed by the processor, implements the steps of the human respiratory system risk control and early warning method according to any one of claims 1 to 5.

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