CN115349824A - Health early warning method and device, computer equipment and storage medium - Google Patents
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Abstract
The embodiment of the application is applicable to the technical field of computers, and provides a health early warning method, a health early warning device, computer equipment and a storage medium, wherein the method comprises the following steps: collecting a plurality of kinds of health data of a user, and respectively determining the signal to noise ratio of the health data; acquiring environment detection data, and determining confidence degrees of various health data according to the environment detection data and the signal-to-noise ratio; determining data to be processed in various health data according to the confidence coefficient; acquiring historical data corresponding to the data to be processed, and distinguishing various data to be processed into valid data and invalid data according to the historical data; weighting the historical data and the effective data according to the confidence coefficient to obtain comprehensive data; and carrying out health early warning on the user based on the comprehensive data. By adopting the method, the accuracy of the health data early warning and intervention can be improved, and the early warning and intervention on the risk events related to long-term health, production and life safety can be conveniently carried out in time.
Description
Technical Field
The embodiment of the application belongs to the technical field of computers, and particularly relates to a health early warning method, a health early warning device, computer equipment and a storage medium.
Background
The RPPG technology, that is, the remote photoplethysmography technology, can perform data analysis on the periodic variation of skin color captured by a camera sensor through an algorithm, so as to obtain various physiological index data related to the cardiac cycle, such as the physiological index data of heartbeat, respiration, cardiovascular characteristics, and the like. In the prior art, because signals captured by a camera sensor are easily interfered by noise such as light change, object movement and the like, the physiological index data obtained by current measurement is directly used for health early warning, so that the accuracy of the health early warning is lower.
Disclosure of Invention
In view of this, embodiments of the present application provide a health early warning method and apparatus, a computer device, and a storage medium, so as to improve accuracy of early warning a health risk event according to health data.
A first aspect of an embodiment of the present application provides a health early warning method, including:
collecting a plurality of kinds of health data of a user, and respectively determining the signal to noise ratio of the health data;
acquiring environment detection data, and determining confidence degrees of various health data according to the environment detection data and the signal-to-noise ratio;
determining data to be processed in various health data according to the confidence coefficient;
acquiring historical data corresponding to the data to be processed, and distinguishing various data to be processed into valid data and invalid data according to the historical data;
weighting the historical data and the effective data according to the confidence coefficient to obtain comprehensive data;
and carrying out health early warning on the user based on the comprehensive data.
A second aspect of the embodiments of the present application provides a health-warning apparatus, including:
the health data acquisition module is used for acquiring various health data of a user and respectively determining the signal to noise ratio of the various health data;
the confidence coefficient determining module is used for acquiring environment detection data and determining the confidence coefficient of various health data according to the environment detection data and the signal to noise ratio;
the to-be-processed data determining module is used for determining to-be-processed data in various health data according to the confidence coefficient;
the effective data determining module is used for acquiring historical data corresponding to the data to be processed and distinguishing various data to be processed into effective data and invalid data according to the historical data;
the data weighting module is used for weighting the historical data and the effective data according to the confidence coefficient to obtain comprehensive data;
and the health early warning module is used for carrying out health early warning on the user based on the comprehensive data.
A third aspect of embodiments of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the health-warning method according to the first aspect.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the health-warning method according to the first aspect is implemented.
A fifth aspect of the embodiments of the present application provides a computer program product, which when running on a computer, causes the computer to execute the health-warning method according to the first aspect.
Compared with the prior art, the embodiment of the application has the following advantages:
according to the embodiment of the application, various health data of a user are collected through a non-contact sensing technology, and the collected various health data are divided into valid data and invalid data according to historical health data. And determining confidence degrees of various effective data through the environment detection data, and performing weighted calculation on the various effective data and the historical health data according to the corresponding confidence degrees to obtain comprehensive data. And based on the obtained comprehensive data, health early warning can be performed on the user by generating a personal risk report and/or sending risk warning information. According to the embodiment of the application, various health data obtained through non-contact sensing calculation can be filtered and corrected according to historical health data and environment detection data, so that the accuracy of the health data for risk judgment can be further improved. The embodiment of the application can generate a detailed personal risk report through various effective data, so that a user and/or an organization manager can conveniently alarm and intervene on health risk events related to production and life safety.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the embodiments or the description of the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a schematic diagram of a health warning method provided in an embodiment of the present application;
fig. 2 is a schematic diagram of an implementation manner of S104 in a health warning method according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of a system architecture to which a health warning method according to an embodiment of the present disclosure is applied;
FIG. 4 is a schematic diagram of a health data display mode provided by an embodiment of the present application;
FIG. 5 is a schematic diagram of a personal risk report display mode provided by an embodiment of the present application;
fig. 6 is a schematic diagram of another health warning method provided in the embodiment of the present application
Fig. 7 is a schematic diagram of a health-warning apparatus according to an embodiment of the present disclosure;
fig. 8 is a schematic diagram of a computer device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
The technical solution of the present application is explained below by specific examples.
Referring to fig. 1, a schematic diagram of a health early warning method provided in an embodiment of the present application is shown, which may specifically include the following steps:
s101, collecting multiple kinds of health data of a user, and respectively determining signal-to-noise ratios of the health data.
In the embodiment of the present application, the manner of acquiring the health data is not limited, and various health data of the user may be acquired through a non-contact sensing technology or a wearable sensing technology, for example. The non-contact sensing technology can be used for capturing images through a camera, acquiring health data which cannot be subjectively controlled by people such as heart rate, blood oxygen, respiration and blood pressure through artificial intelligence spectrum and non-contact sensing, and performing multi-mode fusion calculation on the physiological indexes through the artificial intelligence technology to obtain basic disease indexes and health indexes of a user, such as physiological index data of blood sugar, atrial fibrillation, long-term pressure, cardiovascular risk and the like. The system shown in fig. 2 can be arranged in various places to acquire various health data through a non-contact sensing technology, the system can include an integrated module arranged in scenes such as logistics, vehicles, activity rooms and the like, an APK on a mobile phone and portable equipment of an enterprise health cabin, real-time video streams of users can be acquired through cameras of the equipment, and the video streams are processed by a computing engine, so that various health data are obtained, and the computing engine can be arranged at the front end and can also be arranged at the cloud end.
In an embodiment of the present application, the health data may include various physiological data, chronic disease index, sub-health index, psychological index, emotional index and the like of the user. The physiological data may include heart rate, blood oxygen, respiration, etc., the chronic disease index may include index data such as blood pressure, blood sugar, angiosclerosis, atrial fibrillation, depression, etc., the sub-health index may include data such as cardiovascular age, recovery ability, long-term stress, exercise ability, etc., and the psychological index may include indexes such as tension, fatigue, drowsiness, etc.
In the embodiment of the application, the signal to noise ratio of various health data can be determined by performing Fourier transform on the acquired data. For example, the acquired heartbeat curve may be subjected to noise reduction processing by band-pass filtering, the denoised heartbeat curve may be subjected to fourier transform to obtain a frequency spectrum of the heartbeat curve, a frequency spectrum area corresponding to a peak of the frequency spectrum of the heartbeat curve is a signal intensity, a remaining frequency spectrum area is a noise intensity, and a signal-to-noise ratio may be equal to the signal intensity divided by the noise intensity.
In the embodiment of the present application, the signal-to-noise ratio of the health data corresponding to the current cardiac cycle can be obtained by analyzing the shape of the acquired cardiac waveform, such as SQI analysis, and by performing SQI analysis on the periodic waveform of the cardiac signal.
S102, obtaining environment detection data, and determining confidence degrees of various health data according to the environment detection data and the signal-to-noise ratio.
In embodiments of the present application, the environmental detection data may include individual status information, ambient light data, and measurement device status information. The individual state information may be obtained through technologies such as face tracking, feature point tracking, or machine vision, and the specific obtaining manner of the individual state information is not limited in the embodiment of the present application. Ambient light data can be obtained by detecting the intensity of light changes in the background area during user measurements. By acquiring the measurement value of the accelerometer of the measuring device, whether the measuring device is in a vibration or moving state in the measuring process can be judged. The confidence degrees of the current multiple effective data can be adjusted through the environment detection data and the signal to noise ratio, so that the accuracy of the current multiple effective data is further enhanced. The acquired various environment detection data such as the individual state information, the environment light data, the measuring equipment state information and the like are subjected to Fourier transform, and the size of the environment noise can be determined. In embodiments of the present application, the confidence levels of the various health data may be determined by the ambient noise and the signal-to-noise ratio, which may be a function of the ambient noise and the signal-to-noise ratio. Whether the environmental noise is greater than a preset environmental noise threshold value or not can be judged firstly, if the environmental noise is less than or equal to the preset environmental noise threshold value, the fact that an individual and a measuring device are in a static state in the measuring process and the environmental light data display that the light of the measuring environment is always within a preset optimal environmental light range in the measuring process can be meant, the fact that the environmental noise does not affect the accuracy of various measured health data in the measuring process can be considered, therefore, the confidence degree of the various health data is only related to the corresponding signal-to-noise ratio, at the moment, the confidence degree can be a function of the signal-to-noise ratio, the confidence degree of the various health data is positively related to the signal-to-noise ratio, and the greater the signal-to-noise ratio is, the greater the confidence degree of the corresponding health data is. If the ambient noise is greater than the preset ambient noise threshold, it means that the individual and/or the measuring device is in a motion state during the measurement process, or the ambient light data indicates that the light of the measurement environment is not within the preset optimal ambient light range during the measurement process. Then, the confidence of the various health data measured in the environment may be affected by the environmental noise, and the confidence may be a function of the signal-to-noise ratio and the environmental noise, where the confidence of the various health data in the function is positively correlated with the signal-to-noise ratio, and the confidence of the various health data is negatively correlated with the environmental noise. Namely, the larger the signal-to-noise ratio is, the higher the confidence of the corresponding health data is, and the larger the environmental noise is, the lower the confidence of various health data is.
S103, determining data to be processed in the health data according to the confidence coefficient.
In the embodiment of the application, the data to be processed can be determined by judging whether the confidence of various health data exceeds the corresponding confidence threshold. If the confidence of certain health data is smaller than a preset confidence threshold, the health data can be considered to be interfered more in the measurement process, and the accuracy of the health data is insufficient, so that the health early warning of the user cannot be performed through the health data. If the confidence of certain health data is greater than or equal to the preset confidence threshold, the health data is considered to be less interfered in the measurement process, the accuracy of the health data is high, and therefore, the health data can be used for carrying out health early warning on a user, and the health data with the confidence greater than or equal to the preset confidence threshold can be marked as data to be processed.
And S104, acquiring historical data corresponding to the data to be processed, and distinguishing various data to be processed into valid data and invalid data according to the historical data.
In the embodiment of the application, the acquired health data can be filtered through historical data, that is, whether the current health data is valid data is determined through the past data. For example, a record of one item of blood pressure in recent historical data of a certain user is hypotension, and blood pressure data in various health data obtained through a non-contact sensing technology at present is hypertension, and because the difference between the current blood pressure data and the recent historical blood pressure data is too large, the current blood pressure data can be marked as invalid data through the recent historical blood pressure data, the current blood pressure data is only stored, and the current blood pressure data is not used for risk early warning at this time.
In a possible implementation manner of the embodiment of the present application, as shown in fig. 3, in S104, the distinguishing of the multiple types of data to be processed into valid data and invalid data according to the historical data may specifically include the following steps S1041 to S1044.
S1041, determining a difference value between each health data and the corresponding historical data.
In the embodiment of the application, a plurality of historical data can be selected, and the effectiveness of the current various health data can be determined by determining the difference value between the current various health data and the historical data. For example, the historical data of the previous three times is selected, the variance between the historical data of the three times and the various kinds of current health data is obtained, and the variance is used as the difference value between the historical data and the various kinds of current health data. For example, the historical data of the previous three times comprise blood pressure data, the historical blood pressure data of the three times are respectively 120mmHg, 122mmHg and 121mmHg, and the difference value between the current blood pressure data and the historical blood pressure data is 158.1875 when the blood pressure data is 150mmHg in the current multiple health data.
S1042, judging whether the difference value is larger than a set threshold value.
In the embodiment of the application, each health data has a set threshold of the difference value, and the difference value of the current health data is compared with the set threshold of the difference value, so that whether the current health data is valid data or not is determined.
If the difference value between the health data and the historical data is larger than the set threshold value, executing S1043; if the difference between the health data and the historical data is less than or equal to the set threshold, S1044 is executed.
And S1043, marking the health data as invalid data, and storing the invalid data.
In the embodiment of the present application, if a difference value between a certain kind of current health data and the corresponding historical data is greater than a set threshold value of the difference value of the certain kind of current health data, the certain kind of current health data may be considered to be too different from the historical data, and the accuracy of the current measurement of the certain kind of health data is uncertain, so that the certain kind of current health data may be marked as invalid data, and only the certain kind of current health data may be stored, without using the certain kind of current health data for the health early warning. For example, the historical data of the previous three times include blood pressure data, the historical blood pressure data of the three times are respectively 120mmHg, 122mmHg and 121mmHg, the blood pressure data of the current multiple health data is 150mmHg, the preset blood pressure data difference value threshold is 150, the difference value between the current blood pressure data and the historical blood pressure data is 158.1875, and if the difference value is greater than the preset blood pressure data difference value threshold, the current blood pressure data can be marked as invalid data, and the current blood pressure data is stored.
S1044, marking the various health data as the valid data.
In the embodiment of the present application, if a difference value between a certain health data and a corresponding historical data in the current multiple health data is less than or equal to a set threshold value of the difference value between the certain health data and the corresponding historical data, it can be considered that the current health data is not much different from the historical data, and the current health data has a certain accuracy, so that the multiple health data can be marked as valid data and used for further risk analysis.
For example, the average value of the measured heart rates in the current multiple health data is 85 times/minute, the average values of the heart rates are 90 times/minute, 85 times/minute and 80 times/minute, respectively, and the preset threshold value of the heart rate data difference is 25, so that the variance of the current heart rate average value from the previous three historical heart rate average values is 16.6, which is smaller than the preset threshold value of the heart rate data difference 25, and therefore, the current measured heart rate average value can be considered to have a certain accuracy, and the current multiple health data can be marked as valid data.
And S105, weighting the historical data and the effective data according to the confidence coefficient to obtain comprehensive data.
Weighting the multiple effective data and the corresponding historical data according to the confidence coefficient of the current effective data determined by the environment detection data and the corresponding confidence coefficient of the obtained historical data, so as to obtain the comprehensive data corresponding to the multiple health data.
And S106, carrying out health early warning on the user based on the comprehensive data.
In the embodiment of the application, the risk judgment can be performed on various real-time indexes of the user through a real-time index template arranged in the system by using various comprehensive data obtained by weighting the effective data and the historical data according to the corresponding confidence coefficients, so that the health early warning can be performed on the user. As shown in fig. 4, the historical data may be presented in the form of a coordinate axis to display historical trends of various health data of the user. A graphical representation of the historical trend of the health data, blood pressure, is shown in fig. 4. In fig. 4, the abscissa represents time, which may represent the corresponding acquisition time of each blood pressure data, and the ordinate represents the blood pressure value. The broken line in fig. 4 represents the trend of the blood pressure change of the user for a certain period of time, and the broken line represents the alarm threshold value of the blood pressure data. A health early warning map as shown in fig. 5 may also be generated, and the health early warning map may include user information, currently measured various health data, and physical and mental health indicators, and may intuitively perform health early warning for the user. In fig. 5, the leftmost side shows the user image of the user, the middle shows the specific values of the various physiological indexes of the user, and the rightmost side shows the physical and mental health indexes of the user. Fig. 5 shows that the systolic blood pressure of the user is 120mmHg, the diastolic blood pressure is 80mmHg, the heart rate is 80bpm, the respiratory rate is 21 times/min, the blood oxygen amount is 98%, and the blood sugar is 98mm/l, and also shows the respiratory waveform of the user, the cardiovascular risk of the user is 80%, the diabetes risk is 20%, the long-term stress index is 80%, the functional age is 80%, and the functional activity is 80%.
In the embodiment of the application, the validity of the current various health data is determined through the plurality of historical data, and the confidence degrees of the current various health data are determined according to the environment detection data, so that the accuracy of the various health data obtained by the non-contact sensing technology can be effectively improved. And risk judgment is carried out according to various health data, and real-time detection, early warning and intervention can be carried out on the health condition of the user.
Referring to fig. 6, a schematic diagram of another health early warning method provided in the embodiment of the present application is shown, which specifically includes the following steps:
s601, collecting multiple kinds of health data of a user, and respectively determining signal to noise ratios of the health data.
S602, obtaining environment detection data, and determining confidence degrees of various health data according to the environment detection data and the signal to noise ratio.
And S603, determining data to be processed in various health data according to the confidence coefficient.
S604, obtaining historical data corresponding to the data to be processed, and dividing various data to be processed into valid data and invalid data according to the historical data.
S605, weighting the historical data and the effective data according to the confidence coefficient to obtain comprehensive data.
Since S601-S605 in this embodiment are similar to S101-S105 in the previous embodiment, they can refer to each other, and are not described again in this embodiment.
And S606, performing single data risk judgment on the single comprehensive data according to preset indexes.
In the embodiment of the application, single data risk judgment can be performed by using single comprehensive data according to preset indexes of various health data, the single data risk judgment can include physiological index risk judgment, emotion risk judgment and the like, and each single data risk judgment can include instant risk judgment and long-term risk judgment. Physiological index risk determinations may include, for example, risk of arrhythmia, tachycardia, respiratory health, etc., and emotional risk determinations may include risk of stress, risk of drowsiness, etc. Immediate risk determinations may include, for example, stress risks, drowsiness risks, tachycardia, bradycardia, hypoxia risks, etc., and long-term risk determinations may include, for example, respiratory health risks, etc. For example, when the heart rate maximum value comprehensive data is found to be 120 times/minute when the user is at rest and exceeds the preset rest heart rate maximum value index for 100 times/minute, the user can be judged to have the risk of heart rate overspeed according to the heart rate maximum value comprehensive data.
S607, calculating the multiple comprehensive data according to a preset formula to obtain linkage data, and performing multi-data risk judgment on the linkage data according to the preset index.
In the embodiment of the application, a plurality of kinds of associated comprehensive data can be calculated through a preset formula so as to determine certain linkage data. And according to the relation of the linkage data in a preset index, the multi-data risk judgment of the user can be carried out. The multiple data risk determination may include, for example, long-term index risk determination, comprehensive risk determination, chronic risk determination, etc., and each multiple data risk determination may include immediate risk determination and long-term risk determination. Long-term index risk assessment may include, for example, cardiovascular health risk, cardiovascular age risk, and the like, and chronic risk assessment may include risk of hypertension, risk of hypotension, and the like. For example, whether the user has frequent exercise can be judged through the age data, the gender data and the heart rate comprehensive data, and whether the user has cardiovascular health risks can also be judged through the blood pressure comprehensive data, the blood sugar comprehensive data and the heart rate variability comprehensive data.
And S608, generating a personal risk report according to the single data risk judgment result and the multiple data risk judgment result.
In the embodiment of the application, the personal risk report can be generated according to the single data risk judgment result and the multiple data risk judgment result, and the generated personal risk report can be visually displayed to the user through the visualization panel shown in fig. 5. The processed effective data and the corresponding confidence degrees thereof can be stored together with the personal risk report, and the stored effective data and the corresponding confidence degrees thereof can be used as historical data for determining the effectiveness of various subsequently obtained health data and generating a new personal risk report.
In the embodiment of the application, a plurality of characteristic crowds can be divided according to a plurality of characteristics of users, such as age, gender, industry, occupation, working time and the like. The various health data and individual risk reports stored by any characteristic population can be subjected to aggregate analysis, so that a population risk report is obtained. For example, by performing aggregate analysis of various health data and individual risk reports of women aged 30 or older in a company, a group risk report can be generated by obtaining data such as the proportion of hypertension, the proportion of hyperglycemia, and the proportion of high tension risk of women aged 30 or older in the company.
And S609, carrying out health early warning on the user according to the personal risk report.
In the embodiment of the application, after the personal risk report is generated, the health early warning can be performed on the user in a mode of sending risk warning information to the user. The probability of occurrence of the plurality of risk events may be determined based on the stored historical data and the personal risk report and a determination may be made as to whether the probability of occurrence of the plurality of risk events exceeds a corresponding alarm threshold. When the occurrence probability of any risk event is found to exceed the alarm threshold value, risk alarm information can be immediately sent to a user. For example, if a respiratory system health risk occurs in a personal risk report of a user, and 70% of the previous five measurements have a respiratory rate greater than 25, which exceeds an alarm threshold of the respiratory rate by 60%, the health of the user can be warned by sending a respiratory rate alarm message to the user.
In the embodiment of the application, corresponding group risk reports are generated for various groups according to historical data, so that corresponding risk health early warning can be performed for different groups, an objective health reference report is provided for an organization manager, and the organization manager can conveniently perform alarming and intervention on health risk events related to production and life safety.
It should be noted that, the sequence numbers of the steps in the foregoing embodiments do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the inherent logic of the process, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Referring to fig. 7, a schematic diagram of a health early warning apparatus provided in an embodiment of the present application is shown, and specifically may include a health data acquisition module 701, a confidence determination module 702, a to-be-processed data determination module 703, an effective data determination module 704, a data weighting module 705, and a health early warning module 706, where:
the health data acquisition module 701 is used for acquiring various health data of a user and respectively determining the signal to noise ratio of the various health data;
a confidence level determining module 702, configured to obtain environment detection data, and determine confidence levels of various health data according to the environment detection data and the signal-to-noise ratio;
a to-be-processed data determining module 703, configured to determine to-be-processed data in the various health data according to the confidence;
the valid data determining module 704 is configured to obtain historical data corresponding to the to-be-processed data, and divide the various to-be-processed data into valid data and invalid data according to the historical data;
a data weighting module 705, configured to weight the historical data and the valid data according to the confidence to obtain comprehensive data;
and a health early warning module 706 configured to perform health early warning on the user based on the comprehensive data.
In a possible implementation manner of the embodiment of the present application, the health data preprocessing module 702 may be specifically configured to: determining a difference value between each type of health data and corresponding historical data; judging whether the difference value is larger than a set threshold value or not; if the difference value between the health data and the historical data is larger than a set threshold value, marking the health data as invalid data and storing the invalid data; and if the difference value between the health data and the historical data is less than or equal to a set threshold, marking the various health data as valid data.
In a possible implementation manner of the embodiment of the present application, the confidence determining module 703 may be specifically configured to: determining environmental noise according to the environmental detection data; determining confidence degrees of various health data according to the environmental noise; if the environmental noise is smaller than or equal to a preset environmental noise threshold value, the confidence degree is positively correlated with the signal-to-noise ratio; if the environmental noise is greater than the environmental noise threshold, the confidence level is positively correlated with the signal-to-noise ratio and the confidence level is negatively correlated with the environmental noise.
In a possible implementation manner of the embodiment of the present application, the health early warning module 706 may be specifically configured to: performing single data risk judgment on the single comprehensive data according to a preset index; calculating various comprehensive data according to a preset formula to obtain linkage data, and performing multi-data risk judgment on the linkage data according to preset indexes; generating a personal risk report according to the single data risk judgment result and the multiple data risk judgment result; and carrying out health early warning on the user according to the personal risk report.
In a possible implementation manner of the embodiment of the present application, the health early warning module 706 may further be configured to: determining the occurrence probability of a plurality of risk events according to the historical health data and the personal risk report, and judging whether the occurrence probability of each risk event is greater than the corresponding alarm threshold value; and if the occurrence probability of any risk event is greater than the corresponding alarm threshold value, sending risk alarm information to the user.
In a possible implementation manner of the embodiment of the present application, the apparatus may further include a data storage module, where the data storage module may be specifically configured to: and taking the valid data and the confidence coefficient of the valid data as historical health data, and storing the historical data and the personal risk report.
In a possible implementation manner of the embodiment of the present application, the health early warning module 706 may further be configured to: dividing a plurality of users into a plurality of characteristic crowds according to different characteristics; and carrying out aggregate analysis on the health data and the individual risk reports of any characteristic population to generate a population risk report.
For the apparatus embodiment, since it is substantially similar to the method embodiment, it is described relatively simply, and reference may be made to the description of the method embodiment section for relevant points.
Referring to fig. 8, a schematic diagram of a computer device provided in an embodiment of the present application is shown. As shown in fig. 8, a computer apparatus 800 in the embodiment of the present application includes: a processor 810, a memory 820, and a computer program 821 stored in the memory 820 and operable on the processor 810. The processor 810, when executing the computer program 821, implements the steps in the various embodiments of the health-warning method described above, such as the steps S101 to S105 shown in fig. 1. Alternatively, the processor 810, when executing the computer program 821, implements the functions of the modules/units in the device embodiments, such as the functions of the modules 701 to 705 shown in fig. 7.
Illustratively, the computer program 821 may be partitioned into one or more modules/units, which are stored in the memory 820 and executed by the processor 810 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing certain functions, which may be used to describe the execution of the computer program 821 in the computer device 800. For example, the computer program 821 may be divided into a health data acquisition module, a health data preprocessing module, a confidence determination module, a data weighting module, and a health pre-warning module, each of which has the following specific functions:
the health data acquisition module is used for acquiring various health data of a user and respectively determining the signal to noise ratio of the various health data;
the confidence coefficient determining module is used for acquiring environment detection data and determining the confidence coefficient of the effective data according to the environment detection data and the signal-to-noise ratio;
the to-be-processed data determining module is used for determining the to-be-processed data in the health data according to the confidence coefficient;
the effective data determining module is used for acquiring historical data corresponding to the data to be processed and distinguishing various data to be processed into effective data and invalid data according to the historical data;
the data weighting module is used for weighting the historical health data and the effective data according to the confidence coefficient to obtain comprehensive data;
and the health early warning module is used for carrying out health early warning on the user based on the comprehensive data.
The computer device 800 may be a computer device for implementing the foregoing method embodiments, and the computer device may be a desktop computer, a cloud server, or other computing devices. The computer device 800 may include, but is not limited to, a processor 810, a memory 820. Those skilled in the art will appreciate that fig. 8 is only one example of a computer device 800 and is not intended to limit the computer device 800 and that the computer device 800 may include more or less components than those shown, or some of the components may be combined, or different components, e.g., the computer device 800 may also include input and output devices, network access devices, buses, etc.
The Processor 810 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 820 may be an internal storage unit of the computer device 800, such as a hard disk or a memory of the computer device 800. The memory 820 may also be an external storage device of the computer device 800, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the computer device 800. Further, the memory 820 may also include both internal storage units and external storage devices of the computer device 800. The memory 820 is used for storing the computer program 821 and other programs and data required by the computer apparatus 800. The memory 820 may also be used to temporarily store data that has been output or is to be output.
The embodiment of the present application further discloses a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the health early warning method according to the foregoing embodiments is implemented.
The embodiment of the application also discloses a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the health early warning method according to the foregoing embodiments.
The embodiment of the application also discloses a computer program product, and when the computer program product runs on a computer, the computer is enabled to execute the health early warning method in the foregoing embodiments.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A health warning method, comprising:
collecting a plurality of kinds of health data of a user, and respectively determining the signal to noise ratio of the health data;
acquiring environment detection data, and determining confidence degrees of various health data according to the environment detection data and the signal-to-noise ratio;
determining data to be processed in various health data according to the confidence coefficient;
acquiring historical data corresponding to the data to be processed, and distinguishing various data to be processed into valid data and invalid data according to the historical data;
weighting the historical data and the effective data according to the confidence coefficient to obtain comprehensive data;
and carrying out health early warning on the user based on the comprehensive data.
2. The method of claim 1, wherein weighting the historical data and the valid data according to the confidence level to obtain integrated data comprises:
determining a difference value between each health data and the corresponding historical data;
judging whether the difference value is larger than a set threshold value or not;
if the difference value between the health data and the historical data is larger than the set threshold value, marking the health data as invalid data and storing the invalid data;
if the difference value between the health data and the historical data is smaller than or equal to the set threshold value, marking various health data as valid data.
3. The method of claim 1, wherein the environmental detection data comprises individual status information, measurement device status information, and ambient light data, and wherein determining the confidence level for the plurality of types of health data based on the environmental detection data and the signal-to-noise ratio of the plurality of types of health data comprises:
determining environmental noise according to the environmental detection data;
determining confidence degrees of various health data according to the environmental noise; if the environmental noise is smaller than or equal to a preset environmental noise threshold value, the confidence degree is positively correlated with the signal-to-noise ratio;
if the environmental noise is greater than the environmental noise threshold, the confidence level is positively correlated with the signal-to-noise ratio and the confidence level is negatively correlated with the environmental noise.
4. The method according to any one of claims 1 to 3, wherein the category of the integrated data includes a plurality of categories, each category of the integrated data corresponds to each category of the health data one to one, and the performing the health warning on the user based on the integrated data includes:
performing single data risk judgment on the single comprehensive data according to a preset index;
calculating various comprehensive data according to a preset formula to obtain linkage data, and performing multi-data risk judgment on the linkage data according to the preset index;
generating a personal risk report according to the single data risk judgment result and the multiple data risk judgment result;
and carrying out health early warning on the user according to the personal risk report.
5. The method of claim 4, wherein the pre-warning the user of health based on the personal risk report comprises:
determining the occurrence probability of a plurality of risk events according to the historical data and the personal risk report, and judging whether the occurrence probability of each risk event is greater than a corresponding alarm threshold value;
and if the occurrence probability of any risk event is greater than the corresponding alarm threshold, sending risk alarm information to the user.
6. The method of claim 4, further comprising, after generating the individual risk report based on the single data risk assessment result and the multiple data risk assessment results:
and taking the effective data and the confidence coefficient of the effective data as the historical data, and storing the historical data and the personal risk report.
7. The method of claim 6, wherein after storing the historical data and the personal risk report, further comprising:
dividing a plurality of users into a plurality of characteristic crowds according to different characteristics;
and performing aggregate analysis on the health data and the individual risk reports of any characteristic population to generate a population risk report.
8. A health-warning device, comprising:
the health data acquisition module is used for acquiring various health data of a user and respectively determining the signal to noise ratio of the various health data;
the confidence coefficient determining module is used for acquiring environment detection data and determining the confidence coefficient of various health data according to the environment detection data and the signal to noise ratio;
the to-be-processed data determining module is used for determining the to-be-processed data in the health data according to the confidence coefficient;
the effective data determining module is used for acquiring historical data corresponding to the data to be processed and distinguishing various data to be processed into effective data and invalid data according to the historical data;
the data weighting module is used for weighting the historical data and the effective data according to the confidence coefficient to obtain comprehensive data;
and the health early warning module is used for carrying out health early warning on the user based on the comprehensive data.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the health alert method as recited in any one of claims 1 to 7 when the computer program is executed by the processor.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of health pre-warning according to any one of claims 1 to 7.
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| CN117042031A (en) * | 2023-10-08 | 2023-11-10 | 深圳曼瑞德科技有限公司 | Mobile communication terminal and remote blood oxygen monitoring system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117042031A (en) * | 2023-10-08 | 2023-11-10 | 深圳曼瑞德科技有限公司 | Mobile communication terminal and remote blood oxygen monitoring system |
| CN117042031B (en) * | 2023-10-08 | 2023-12-26 | 深圳曼瑞德科技有限公司 | Mobile communication terminal and remote blood oxygen monitoring system |
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