CN116805519A - Hypertension data transmission early warning system and method based on artificial intelligence - Google Patents
Hypertension data transmission early warning system and method based on artificial intelligence Download PDFInfo
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Abstract
The invention relates to the technical field of data transmission management, in particular to an artificial intelligence-based hypertension data transmission early warning system and method, comprising the following steps: the system comprises a transmission information acquisition module, a database, a reminding target screening module, a measurement reminding management module and a transmission data management module, wherein the transmission information acquisition module acquires historical uploading information of a user after logging in a health management platform, the historical uploading information is transmitted to the database, the database stores the historical uploading information, the reminding target screening module analyzes the historical uploading information, screens out target users needing to remind of adjusting the blood pressure measurement time, the measurement reminding management module adjusts the blood pressure measurement time of the target users and reminds the users to measure the blood pressure, the transmission data management module stores and manages the blood pressure data uploaded by the users, reminds the users to timely transmit the blood pressure data, and maximally guarantees the users to measure the blood pressure and upload the blood pressure in an optimal measurement time period, so that the referential property of the uploaded data is improved.
Description
Technical Field
The invention relates to the technical field of data transmission management, in particular to an artificial intelligence-based hypertension data transmission early warning system and method.
Background
The health management platform is a platform for managing and analyzing personal health data, acquires and integrates the personal health data through a big data technology, helps people to know own physical conditions and provide corresponding health suggestions, helps people to find potential health problems in time, blood pressure is one of the most main physiological parameters reflecting the health conditions of the human body, and a user can know the health data of the user in terms of blood pressure in time by uploading the blood pressure measurement data to the health management platform every day;
however, in terms of blood pressure data transmission management, some problems still exist in the existing blood pressure data transmission management method: the blood pressure measurement of each day has an optimal time period, the analysis result of the blood pressure data obtained by measuring the blood pressure in the optimal time period is most accurate, and because the time of measuring the blood pressure at ordinary times of a user is not fixed, the blood pressure measurement time of the user is not effectively managed in the prior art so as to ensure the blood pressure measurement of the user in the optimal measurement time period to the greatest extent and the blood pressure data transmission, and the referenceability of the blood pressure data obtained by a platform cannot be improved.
Therefore, an artificial intelligence-based hypertension data transmission early warning system and method are needed to solve the above problems.
Disclosure of Invention
The invention aims to provide an artificial intelligence-based hypertension data transmission early warning system and method, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an artificial intelligence based hypertension data transmission early warning system, the system comprising: the system comprises a transmission information acquisition module, a database, a reminding target screening module, a measurement reminding management module and a transmission data management module;
the output end of the transmission information acquisition module is connected with the input end of the database, the output end of the database is connected with the input ends of the reminding target screening module and the transmission data management module, the output end of the reminding target screening module is connected with the input end of the measurement reminding management module, and the output end of the measurement reminding management module is connected with the input end of the transmission data management module;
the transmission information acquisition module is used for acquiring historical uploading information of a user after logging in the health management platform and transmitting the historical uploading information to the database;
the database is used for storing historical uploading information;
the reminding target screening module is used for analyzing the historical uploading information and screening out target users needing to remind and adjust the blood pressure measurement time;
the measurement reminding management module is used for adjusting the blood pressure measurement time of the target user and reminding the user to measure the blood pressure;
the transmission data management module is used for carrying out storage management on blood pressure data uploaded by a user.
Further, the transmission information acquisition module comprises a user information acquisition unit and a measurement time acquisition unit;
the output ends of the user information acquisition unit and the measurement time acquisition unit are connected with the input end of the database;
the user information acquisition unit is used for acquiring the time information of getting up input by a user after logging in the health management platform in the past;
the measurement time acquisition unit is used for acquiring measurement blood pressure time information uploaded by a user in the past logging on the health management platform.
Further, the reminding target screening module comprises an optimal time setting unit, a measurement time comparison unit and a target screening unit;
the output ends of the optimal time setting unit and the database are connected with the input end of the measuring time comparison unit, and the output end of the measuring time comparison unit is connected with the input end of the target screening unit;
the optimal time setting unit is used for setting the optimal blood pressure measurement time period as follows: [ user getting-up time, user getting-up time +L ], wherein L represents an optimal interval duration from the user getting-up time;
the measurement time comparison unit is used for comparing the optimal blood pressure measurement time period with the blood pressure measurement time uploaded by the user in the past logging-in health management platform and judging whether the time for measuring the blood pressure of the user in the past is in the optimal blood pressure measurement time period;
the target screening unit is used for screening target users needing to remind and adjust the blood pressure measurement time according to the judging result.
Further, the measurement reminding management module comprises a reminding time planning unit, a blood pressure measurement reminding unit and a measurement data transmission unit;
the input end of the reminding time planning unit is connected with the output end of the target screening unit, the output end of the reminding time planning unit is connected with the input end of the blood pressure measurement reminding unit, and the output end of the blood pressure measurement reminding unit is connected with the input end of the measurement data transmission unit;
the reminding time planning unit is used for planning the optimal blood pressure measurement time for the target user;
the blood pressure measurement reminding unit is used for reminding a target user of measuring blood pressure at the planned time;
the measurement data transmission unit is used for transmitting blood pressure data to the health management platform after the user measures blood pressure, encrypting the transmitted data and user identity information by using an artificial intelligence algorithm, encrypting the user data by using the artificial intelligence algorithm, thereby being beneficial to improving the safety of the data on the platform and reducing the probability of user information leakage.
Further, the transmission data management module comprises a measurement time analysis unit and a data storage planning unit;
the input end of the measurement time analysis unit is connected with the output ends of the measurement data transmission unit and the database, and the output end of the measurement time analysis unit is connected with the input end of the data storage planning unit;
the measuring time analysis unit is used for analyzing the blood pressure measuring time of the target user after reminding the target user to measure the blood pressure and the past blood pressure measuring time of the non-target user and classifying all the users;
the data storage planning unit is used for storing the blood pressure data uploaded by the same user at the same position.
An artificial intelligence-based hypertension data transmission early warning method comprises the following steps:
s1: collecting historical uploading information of a user after logging in the health management platform;
s2: analyzing the historical uploading information, and screening out target users needing to remind and adjust the blood pressure measurement time;
s3: adjusting the blood pressure measurement time of a target user and reminding the user to measure the blood pressure;
s4: and storing and managing the blood pressure data uploaded by the user.
Further, in step S1: collecting the time of getting up input after a user logs in the health management platform for n times in the past and the time of measuring blood pressure uploaded correspondingly, and obtaining the time interval duration set of the time interval of getting up when the blood pressure uploaded randomly for n times by one user as K= { K 1 ，K 2 ，…，K n Collecting interval duration of time interval getting up time of measuring blood pressure uploaded by all users n times;
all the data collected above are collected after being authorized by the user.
Further, in step S2: setting the optimal blood pressure measurement time period as follows: [ user getting-up time, +L user getting-up time ]]Wherein L represents the optimal interval time from the user's time of getting up, and K is compared i And L: if K i The time of measuring the blood pressure of the user is within the optimal blood pressure measuring time period; if K i >L, the time of the corresponding time of measuring blood pressure of the user is not in the optimal blood pressure measurement time period, wherein K i Representing the time interval between the measured blood pressure and the time when the user gets up at the ith upload of a random userCounting the times of measuring blood pressure time in n times of random users in the optimal blood pressure measuring time period by the interval time length, and counting the times of measuring blood pressure time in n times of m users in the optimal blood pressure measuring time period to be W= { W 1 ，W 2 ，…，W m Setting the threshold of times asWherein W is j Representing the times that the blood pressure measuring time is not in the optimal blood pressure measuring time period in n times of a random user, and screening out the users, of which the times that the blood pressure measuring time is not in the optimal blood pressure measuring time period in n times exceeds a time threshold, as target users;
the blood pressure is not fixed and is changed along with the day-and-night change, the optimal time period for blood pressure measurement is generally within two hours after getting up, a period of time is left after getting up in the morning, people are in a quiet state, the blood pressure at the moment is relatively stable, the blood pressure data can be truly reflected, the measured blood pressure value is the most accurate, the optimal blood pressure measurement time period is set, the time for measuring the blood pressure of a user in the past is collected, a target user is screened by judging whether the blood pressure measurement time is within the optimal time period, and if the user frequently does not measure the blood pressure within the optimal time period, the user needs to be reminded of timely measuring the blood pressure.
Further, in step S3: screening out f target users, and taking the interval duration set of the rest time interval getting-up time of the blood pressure measurement time except the blood pressure measurement time which is not in the optimal blood pressure measurement time period of the target user as T= { T 1 ，T 2 ，…，T b Wherein b represents the number of times the blood pressure time is measured in the optimal blood pressure measurement period in n times before a target user, b>3, arranging interval duration according to the order from small to large, randomly dividing the arranged interval duration parameters into a groups, wherein all interval duration in the former group is smaller than that in the latter group, and after the interval duration parameters are obtained and grouped according to a random grouping mode, the average value set of the interval duration in each group in the a group is t= { t 1 ，t 2 ，…，t e ，…，t a Calculating the dispersion degree H of the group a parameters after grouping in a random grouping mode according to the following formula:
wherein t is e Representing the average value of interval duration in the e-th group in the a group after grouping according to a random grouping mode, calculating the dispersion degree of parameters of the a group after grouping according to different grouping modes, obtaining the result obtained after grouping according to the grouping mode with the maximum dispersion degree, and obtaining the interval duration set of the first group after grouping according to the grouping mode with the maximum dispersion degree as T ’ ＝{T 1 ’ ，T 2 ’ ，…，
T v ’ ，…，T c ’ And c parameters are added in the first group, and the blood pressure measurement time of the corresponding target user is adjusted to be: the time interval from the time of getting up is as long asMeasuring blood pressure at a time interval from getting up for +.>If the corresponding target user is detected not to upload the blood pressure data, sending an early warning signal to remind the corresponding target user to measure the blood pressure, wherein T is v ’ Representing a v interval duration in the first group, adjusting blood pressure measurement time for the remaining target users in the same manner;
after screening out the target user, the user needs to plan a proper time to remind the target user to measure blood pressure and upload blood pressure data, when planning time, the behavior habit of the target user is analyzed according to the time of measuring blood pressure in the optimal measurement time period in the past, the proper blood pressure measurement time is planned for the user according to the actual behavior habit data, meanwhile, on the basis of taking the actual behavior habit data of the target user as a time planning reference, the closer the time interval between the blood pressure measurement is, the more accurate the measurement result is judged, a group of blood pressure measurement time closest to the time interval is selected as planning reference data, and the planning time timely reminds a user of measuring the blood pressure, so that the influence of the adjusted blood pressure measurement time on the daily behaviors of the user is guaranteed to be reduced, and the referenceability of the blood pressure measurement result is improved.
Further, in step S4: after reminding a target user of measuring blood pressure according to the adjusted time, collecting interval duration of the time interval get-up time of the measured blood pressure uploaded by m users for g times, and calling an average interval duration set of the time interval get-up time of the measured blood pressure uploaded by each user for g times as R= { R 1 ，R 2 ，…，R m Randomly dividing m users into p classes, and obtaining a random classification result, wherein the average value set of average interval duration of the interval get-up time of the measured blood pressure uploaded by each user in the p classes is r= { r 1 ，r 2 ，…，r i ，…，r p Calculating the dispersion degree H of the blood pressure measurement time of the user in the random one classification result according to the following formula ’ ：
Wherein r is i The average value of average interval duration of the interval starting time of the measured blood pressure of the ith user in the p groups is represented in a random classification result, the classification result with the largest dispersion degree is obtained, and blood pressure data uploaded by the users in the same class in the classification result with the largest dispersion degree are stored in the same position;
after the blood pressure measurement data are uploaded to the health management platform by the users, the users with the closest blood pressure measurement time are classified into one type by analyzing the blood pressure measurement time of all the users, the more the blood pressure measurement time is close, the more meaningful the comparison analysis of the blood pressure data is, the more accurate the obtained result is, the blood pressure data of the same type of users are stored in the same position, the proper comparison data can be provided when the blood pressure data of the users are analyzed, and the accuracy of the blood pressure analysis result is improved.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, through setting the optimal blood pressure measurement time period, the time for measuring the blood pressure of the user in the past is collected, the target user is screened by judging whether the blood pressure measurement time is in the optimal time period, if the blood pressure of the user is frequently measured in the optimal time period, the user needs to be reminded of timely measuring the blood pressure, the user needs to be reminded of planning proper time to remind the user of carrying out blood pressure measurement and uploading blood pressure data after the target user is screened, when the time is planned, the behavior habit of the target user is analyzed according to the time for measuring the blood pressure of the target user in the optimal time period, the proper blood pressure measurement time is planned for the user according to the actual behavior habit data, meanwhile, on the basis that the actual behavior habit data of the target user is taken as a time planning reference, the closer the time interval between the blood pressure measurement times is, the more accurate measurement result is judged, the set of the blood pressure measurement time closest to the time is taken as planning reference data, the planning time is reminded of the user of measuring the blood pressure in time, the daily behavior influence of the adjusted blood pressure measurement time on the user is reduced, and the referee of the blood pressure measurement result obtained by a health management platform is improved;
after the user uploads the blood pressure measurement data to the health management platform, the users with the closest blood pressure measurement time are classified into one type by analyzing the blood pressure measurement time of all the users, and the blood pressure data of the same type of users are stored at the same position, so that the blood pressure data of the users can be conveniently and properly compared when being analyzed, the blood pressure data of the users can be better analyzed, and the accuracy of the blood pressure analysis result is improved.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of an artificial intelligence based hypertension data transmission early warning system;
fig. 2 is a flowchart of a hypertension data transmission early warning method based on artificial intelligence.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The invention is further described below with reference to fig. 1-2 and the specific embodiments.
Embodiment one:
as shown in fig. 1, this embodiment provides a hypertension data transmission early warning system based on artificial intelligence, the system includes: the system comprises a transmission information acquisition module, a database, a reminding target screening module, a measurement reminding management module and a transmission data management module;
the output end of the transmission information acquisition module is connected with the input end of the database, the output end of the database is connected with the input ends of the reminding target screening module and the transmission data management module, the output end of the reminding target screening module is connected with the input end of the measurement reminding management module, and the output end of the measurement reminding management module is connected with the input end of the transmission data management module;
the transmission information acquisition module is used for acquiring historical uploading information after a user logs in the health management platform and transmitting the historical uploading information to the database;
the database is used for storing historical uploading information;
the reminding target screening module is used for analyzing the historical uploading information and screening out target users needing to remind and adjust the blood pressure measurement time;
the measurement reminding management module is used for adjusting the blood pressure measurement time of the target user and reminding the user to measure the blood pressure;
the transmission data management module is used for carrying out storage management on blood pressure data uploaded by a user.
The transmission information acquisition module comprises a user information acquisition unit and a measurement time acquisition unit;
the output ends of the user information acquisition unit and the measurement time acquisition unit are connected with the input end of the database;
the user information acquisition unit is used for acquiring the time information of getting up input by a user after logging in the health management platform in the past;
the measurement time acquisition unit is used for acquiring measurement blood pressure time information uploaded by a user in the past logging in the health management platform.
The reminding target screening module comprises an optimal time setting unit, a measurement time comparison unit and a target screening unit;
the output ends of the optimal time setting unit and the database are connected with the input end of the measuring time comparison unit, and the output end of the measuring time comparison unit is connected with the input end of the target screening unit;
the optimal time setting unit is used for setting the optimal blood pressure measurement time period as follows: [ user getting-up time, user getting-up time +L ], wherein L represents an optimal interval duration from the user getting-up time;
the measurement time comparison unit is used for comparing the optimal blood pressure measurement time period with the blood pressure measurement time uploaded by the user in the past logging-in health management platform and judging whether the time for measuring the blood pressure of the user in the past is in the optimal blood pressure measurement time period;
the target screening unit is used for screening out target users needing to remind and adjust the blood pressure measurement time according to the judging result.
The measurement reminding management module comprises a reminding time planning unit, a blood pressure measurement reminding unit and a measurement data transmission unit;
the input end of the reminding time planning unit is connected with the output end of the target screening unit, the output end of the reminding time planning unit is connected with the input end of the blood pressure measurement reminding unit, and the output end of the blood pressure measurement reminding unit is connected with the input end of the measurement data transmission unit;
the reminding time planning unit is used for planning the optimal blood pressure measurement time for the target user;
the blood pressure measurement reminding unit is used for reminding a target user of measuring blood pressure at the planned time;
the measurement data transmission unit is used for transmitting blood pressure data to the health management platform after the user measures the blood pressure, and encrypting the transmitted data and the user identity information by using an artificial intelligence algorithm.
The transmission data management module comprises a measurement time analysis unit and a data storage planning unit;
the input end of the measurement time analysis unit is connected with the output ends of the measurement data transmission unit and the database, and the output end of the measurement time analysis unit is connected with the input end of the data storage planning unit;
the measuring time analysis unit is used for analyzing the blood pressure measuring time of the target user after reminding the target user to measure the blood pressure and the past blood pressure measuring time of the non-target user and classifying all the users;
the data storage planning unit is used for storing the blood pressure data uploaded by the same user at the same position.
Embodiment two:
as shown in fig. 2, the present embodiment provides an artificial intelligence based hypertension data transmission early warning method, which is implemented based on the data transmission early warning system in the embodiment, and specifically includes the following steps:
s1: collecting historical uploading information of a user after logging in the health management platform: collecting the time of getting up input after a user logs in the health management platform for n times in the past and the time of measuring blood pressure uploaded correspondingly, and obtaining the time interval duration set of the time interval of getting up when the blood pressure uploaded randomly for n times by one user as K= { K 1 ，K 2 ，…，K n Collecting interval duration of time interval getting up time of measuring blood pressure uploaded by all users n times;
s2: analyzing the historical uploading information, screening out target users needing to remind to adjust the blood pressure measurement time, and setting the optimal blood pressure measurement time period as follows: [ user getting-up time, +L user getting-up time ]]Wherein L represents the optimal interval time from the user's time of getting up, and K is compared i And L: if K i The time of measuring the blood pressure of the user is within the optimal blood pressure measuring time period; if K i >L, the time of the corresponding time of measuring blood pressure of the user is not in the optimal blood pressure measurement time period, wherein K i Representing the time interval between the time of getting up and the time of measuring the blood pressure uploaded by a user at the ith timeCounting the times of measuring blood pressure time in n times of random users in the optimal blood pressure measuring time period, and counting the times of measuring blood pressure time in n times of m users in the optimal blood pressure measuring time period to obtain a set of W= { W 1 ，W 2 ，…，W m Setting the threshold of times asWherein W is j Representing the times that the blood pressure measuring time is not in the optimal blood pressure measuring time period in n times of a random user, and screening out the users, of which the times that the blood pressure measuring time is not in the optimal blood pressure measuring time period in n times exceeds a time threshold, as target users;
for example: acquiring interval duration set of interval getting-up time of 10 times of blood pressure measurement uploaded by random user as K= { K 1 ，K 2 ，K 3 ，K 4 ，K 5 ，K 6 ，K 7 ，K 8 ，K 9 ，K 10 } = {10, 20, 70, 130, 50, 90, 150,8, 25, 245}, unit is: minute, set the optimal blood pressure measurement time period as: [ user get-up time, user get-up time +120 ]]The units of L are: minute, K 4 >L、K 7 >L、K 10 >L, counting the number of times that the blood pressure measurement time is not in the optimal blood pressure measurement time period among the corresponding users 10 times as 3, and counting the number of times that the blood pressure measurement time is not in the optimal blood pressure measurement time period among the n times of m=7 users as w= { W 1 ，W 2 ，W 3 ，W 4 ，W 5 ，W 6 ，W 7 Setting the threshold of times as = {3,1,2,0,3,0,3}Screening out the first, fifth and seventh users as target users;
s3: adjusting the blood pressure measurement time of the target user, reminding the user to measure the blood pressure, screening out f=3 total target users, and calling out the target users when the blood pressure is measured in a random time period which is not the optimal blood pressure measurement time periodIn addition, the interval duration set of the interval get-up time of the rest blood pressure measurement time is T= { T 1 ，T 2 ，T 3 ，T 4 ，T 5 ，T 6 ，T 7 The interval duration is arranged in order from small to large, the interval duration parameters after arrangement are randomly divided into 3 groups, all interval durations in the former group are smaller than those in the latter group, after the interval duration sets in each group are respectively {8, 10, 20}, {25} and {50, 70, 90}, after the interval duration sets are grouped according to a random grouping mode, and the interval duration average value set in each group in the 3 groups is t = { t } 1 ，t 2 ，t 3 } = {13, 25, 70}, according to the formulaCalculating the dispersion degree H apprxeq 24.5 of 3 groups of parameters after grouping in the grouping mode, wherein t e Representing the average value of interval duration in the e-th group in the a group after grouping according to a random grouping mode, calculating the dispersion degree of parameters of the a group after grouping according to different grouping modes, obtaining the result obtained after grouping according to the grouping mode with the maximum dispersion degree, and obtaining the interval duration set of the first group after grouping according to the grouping mode with the maximum dispersion degree as T ’ = {8, 10}, wherein there are c=2 parameters in the first group, and the blood pressure measurement time of the corresponding target user is adjusted as follows: the duration of the time interval from the getting-up is +.>Measuring blood pressure, and if the corresponding target user is detected not to upload blood pressure data at a time interval of 9 minutes from the time of getting up, sending an early warning signal to remind the corresponding target user to measure the blood pressure, wherein T is v ’ Representing a v interval duration in the first group, adjusting blood pressure measurement time for the remaining target users in the same manner;
s4: the method comprises the steps of storing and managing blood pressure data uploaded by users, collecting interval duration of time interval getting up of 7 times of blood pressure measurement uploaded by 7 users after reminding target users to measure blood pressure according to adjusted time, and calling the interval duration of time interval getting up of 7 times of blood pressure measurement uploaded by each userThe average interval duration set of the interval get-up time of the blood pressure measuring time is R= { R 1 ，R 2 ，R 3 ，R 4 ，R 5 ，R 6 ，
R 7 } = {8,9, 13, 24, 15, 18, 34}, in units of: the method comprises the steps of randomly classifying 7 users into 3 classes in minutes, and obtaining a random classification result, wherein the average value set of average interval duration of the interval get-up time of the measured blood pressure uploaded by each user in the 3 classes is r= { r 1 ，r 2 ，r 3 } = {8.5, 15.3, 29}, according to the formulaCalculating the dispersion degree H of the blood pressure measurement time of the user in the random classification result ’ Approximately 8.5, where r i And (3) representing the average value of average interval duration of the time interval of getting up of the blood pressure measurement time uploaded by the ith user in the p groups in the random classification result, obtaining the classification result with the largest dispersion degree, and storing the blood pressure data uploaded by the users in the same class in the classification result with the largest dispersion degree in the same position.
Finally, it should be noted that: the foregoing is merely a preferred example of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. An artificial intelligence-based hypertension data transmission early warning system is characterized in that: the system comprises: the system comprises a transmission information acquisition module, a database, a reminding target screening module, a measurement reminding management module and a transmission data management module;
the output end of the transmission information acquisition module is connected with the input end of the database, the output end of the database is connected with the input ends of the reminding target screening module and the transmission data management module, the output end of the reminding target screening module is connected with the input end of the measurement reminding management module, and the output end of the measurement reminding management module is connected with the input end of the transmission data management module;
the transmission information acquisition module is used for acquiring historical uploading information of a user after logging in the health management platform and transmitting the historical uploading information to the database;
the database is used for storing historical uploading information;
the reminding target screening module is used for analyzing the historical uploading information and screening out target users needing to remind and adjust the blood pressure measurement time;
the measurement reminding management module is used for adjusting the blood pressure measurement time of the target user and reminding the user to measure the blood pressure;
the transmission data management module is used for carrying out storage management on blood pressure data uploaded by a user.
2. The artificial intelligence-based hypertension data transmission early warning system according to claim 1, wherein: the transmission information acquisition module comprises a user information acquisition unit and a measurement time acquisition unit;
the output ends of the user information acquisition unit and the measurement time acquisition unit are connected with the input end of the database;
the user information acquisition unit is used for acquiring the time information of getting up input by a user after logging in the health management platform in the past;
the measurement time acquisition unit is used for acquiring measurement blood pressure time information uploaded by a user in the past logging on the health management platform.
3. The artificial intelligence-based hypertension data transmission early warning system according to claim 1, wherein: the reminding target screening module comprises an optimal time setting unit, a measurement time comparison unit and a target screening unit;
the output ends of the optimal time setting unit and the database are connected with the input end of the measuring time comparison unit, and the output end of the measuring time comparison unit is connected with the input end of the target screening unit;
the optimal time setting unit is used for setting the optimal blood pressure measurement time period as follows: [ user getting-up time, user getting-up time +L ], wherein L represents an optimal interval duration from the user getting-up time;
the measurement time comparison unit is used for comparing the optimal blood pressure measurement time period with the blood pressure measurement time uploaded by the user in the past logging-in health management platform and judging whether the time for measuring the blood pressure of the user in the past is in the optimal blood pressure measurement time period;
the target screening unit is used for screening target users needing to remind and adjust the blood pressure measurement time according to the judging result.
4. The artificial intelligence based hypertension data transmission early warning system according to claim 3, wherein: the measurement reminding management module comprises a reminding time planning unit, a blood pressure measurement reminding unit and a measurement data transmission unit;
the input end of the reminding time planning unit is connected with the output end of the target screening unit, the output end of the reminding time planning unit is connected with the input end of the blood pressure measurement reminding unit, and the output end of the blood pressure measurement reminding unit is connected with the input end of the measurement data transmission unit;
the reminding time planning unit is used for planning the optimal blood pressure measurement time for the target user;
the blood pressure measurement reminding unit is used for reminding a target user of measuring blood pressure at the planned time;
the measurement data transmission unit is used for transmitting blood pressure data to the health management platform after the user measures blood pressure, and encrypting the transmitted data and user identity information by using an artificial intelligence algorithm.
5. The artificial intelligence based hypertension data transmission early warning system according to claim 4, wherein: the transmission data management module comprises a measurement time analysis unit and a data storage planning unit;
the input end of the measurement time analysis unit is connected with the output ends of the measurement data transmission unit and the database, and the output end of the measurement time analysis unit is connected with the input end of the data storage planning unit;
the measuring time analysis unit is used for analyzing the blood pressure measuring time of the target user after reminding the target user to measure the blood pressure and the past blood pressure measuring time of the non-target user and classifying all the users;
the data storage planning unit is used for storing the blood pressure data uploaded by the same user at the same position.
6. An artificial intelligence-based hypertension data transmission early warning method is characterized in that: the method comprises the following steps:
s1: collecting historical uploading information of a user after logging in the health management platform;
s2: analyzing the historical uploading information, and screening out target users needing to remind and adjust the blood pressure measurement time;
s3: adjusting the blood pressure measurement time of a target user and reminding the user to measure the blood pressure;
s4: and storing and managing the blood pressure data uploaded by the user.
7. The artificial intelligence-based hypertension data transmission early warning method according to claim 6, wherein the method comprises the following steps of: in step S1: collecting the time of getting up input after a user logs in the health management platform for n times in the past and the time of measuring blood pressure uploaded correspondingly, and obtaining the time interval duration set of the time interval of getting up when the blood pressure uploaded randomly for n times by one user as K= { K 1 ，K 2 ，…，K n Collecting interval duration of time interval getting up time of measuring blood pressure uploaded by all users n times;
in step S2: setting the optimal blood pressure measurement time period as follows: [ user getting-up time, +L user getting-up time ]]Wherein L represents the time optimal from the user's time of getting upInterval length, compare K i And L: if K i The time of measuring the blood pressure of the user is within the optimal blood pressure measuring time period; if K i >L, the time of the corresponding time of measuring blood pressure of the user is not in the optimal blood pressure measurement time period, wherein K i Representing the interval duration of the interval starting time of the blood pressure measurement time interval uploaded by the ith time of one user, counting the times of measuring the blood pressure time in n times of the random time of the user which are not in the optimal blood pressure measurement time period, and counting the times of measuring the blood pressure time in n times of the m users which are not in the optimal blood pressure measurement time period to be W= { W 1 ，W 2 ，…，W m Setting the threshold of times asWherein W is j And (3) representing the times that the blood pressure measuring time is not in the optimal blood pressure measuring time period in the n times of random one user, and screening out the users, of which the times that the blood pressure measuring time is not in the optimal blood pressure measuring time period in the n times exceeds a time threshold, as target users.
8. The artificial intelligence-based hypertension data transmission early warning method according to claim 7, wherein the method comprises the following steps of: in step S3: screening out f target users, and taking the interval duration set of the rest time interval getting-up time of the blood pressure measurement time except the blood pressure measurement time which is not in the optimal blood pressure measurement time period of the target user as T= { T 1 ，T 2 ，…，T b Wherein b represents the number of times the blood pressure time is measured in the optimal blood pressure measurement period in n times before a target user, b>3, arranging interval duration according to the order from small to large, randomly dividing the arranged interval duration parameters into a groups, wherein all interval duration in the former group is smaller than that in the latter group, and after the interval duration parameters are obtained and grouped according to a random grouping mode, the average value set of the interval duration in each group in the a group is t= { t 1 ，t 2 ，…，t e ，…，t a After grouping in a random grouping manner, the group a parameters are calculated according to the following formulaDegree of dispersion H of the numbers:
wherein t is e Representing the average value of interval duration in the e-th group in the a group after grouping according to a random grouping mode, calculating the dispersion degree of parameters of the a group after grouping according to different grouping modes, obtaining the result obtained after grouping according to the grouping mode with the maximum dispersion degree, and obtaining the interval duration set of the first group after grouping according to the grouping mode with the maximum dispersion degree as T ’ ＝{T 1 ’ ，T 2 ’ ，…，T v ’ ，…，T c ’ And c parameters are added in the first group, and the blood pressure measurement time of the corresponding target user is adjusted to be: the time interval from the time of getting up is as long asMeasuring blood pressure at a time interval from getting up for +.>If the corresponding target user is detected not to upload the blood pressure data, sending an early warning signal to remind the corresponding target user to measure the blood pressure, wherein T is v ’ Representing the v-th interval duration in the first group.
9. The artificial intelligence-based hypertension data transmission early warning method according to claim 7, wherein the method comprises the following steps of: in step S4: after reminding a target user of measuring blood pressure according to the adjusted time, collecting interval duration of the time interval get-up time of the measured blood pressure uploaded by m users for g times, and calling an average interval duration set of the time interval get-up time of the measured blood pressure uploaded by each user for g times as R= { R 1 ，R 2 ，…，R m Randomly dividing m users into p classes, and obtaining a random classification result, wherein the time interval between the measured blood pressure uploaded by each user in the p classes is upThe mean set of the mean interval durations of time is r= { r 1 ，r 2 ，…，r i ，…，r p Calculating the dispersion degree H of the blood pressure measurement time of the user in the random one classification result according to the following formula ’ ：
Wherein r is i And (3) representing the average value of average interval duration of the time interval of getting up of the blood pressure measurement time uploaded by the ith user in the p groups in the random classification result, obtaining the classification result with the largest dispersion degree, and storing the blood pressure data uploaded by the users in the same class in the classification result with the largest dispersion degree in the same position.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070038128A1 (en) * | 2005-08-15 | 2007-02-15 | Omron Healthcare Co. Ltd. | Electronic blood pressure monitor, and blood pressure measurement data processing apparatus and method |
| CN204046649U (en) * | 2014-04-09 | 2014-12-24 | 北京华录亿动科技发展有限公司 | A kind of set top box remote blood pressure monitoring management service system |
| US20160089077A1 (en) * | 2014-09-26 | 2016-03-31 | Christine Tinio Geronimo-Button | Hypertension prevention app and web based tools |
| CN206044615U (en) * | 2016-07-06 | 2017-03-29 | 广东乐心医疗电子股份有限公司 | Blood pressure measuring system |
| CN107785083A (en) * | 2017-10-30 | 2018-03-09 | 上海斐讯数据通信技术有限公司 | A kind of user health collecting method and user health monitor system |
| JP2020146403A (en) * | 2019-03-15 | 2020-09-17 | オムロンヘルスケア株式会社 | Blood-pressure measurement device and blood-pressure measurement method |
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070038128A1 (en) * | 2005-08-15 | 2007-02-15 | Omron Healthcare Co. Ltd. | Electronic blood pressure monitor, and blood pressure measurement data processing apparatus and method |
| CN204046649U (en) * | 2014-04-09 | 2014-12-24 | 北京华录亿动科技发展有限公司 | A kind of set top box remote blood pressure monitoring management service system |
| US20160089077A1 (en) * | 2014-09-26 | 2016-03-31 | Christine Tinio Geronimo-Button | Hypertension prevention app and web based tools |
| CN206044615U (en) * | 2016-07-06 | 2017-03-29 | 广东乐心医疗电子股份有限公司 | Blood pressure measuring system |
| CN107785083A (en) * | 2017-10-30 | 2018-03-09 | 上海斐讯数据通信技术有限公司 | A kind of user health collecting method and user health monitor system |
| JP2020146403A (en) * | 2019-03-15 | 2020-09-17 | オムロンヘルスケア株式会社 | Blood-pressure measurement device and blood-pressure measurement method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117648063A (en) * | 2024-01-29 | 2024-03-05 | 南京功夫豆信息科技有限公司 | Intelligent operation management system and method based on big data analysis |
| CN117648063B (en) * | 2024-01-29 | 2024-04-05 | 南京功夫豆信息科技有限公司 | Intelligent operation management system and method based on big data analysis |
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