CN113098971B - Electronic blood pressure counting data transmission monitoring system based on internet - Google Patents
Electronic blood pressure counting data transmission monitoring system based on internet Download PDFInfo
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Abstract
The invention discloses an electronic blood pressure counting data transmission monitoring system based on the internet, which relates to the technical field of data transmission monitoring and solves the problem that the measurement accuracy is reduced due to the fact that the use mode of an electronic sphygmomanometer of each user cannot be reasonably and accurately matched in the prior art; then sending the self-service measurement quantity set and the corresponding average use quantity difference Xi, and the auxiliary measurement quantity set and the corresponding average use quantity difference Wi to a data transmission platform; the using modes of the electronic sphygmomanometer in each area are analyzed, the stability of the using modes of each area is judged by comparing the average using quantity difference, the accuracy of mode matching is improved, and the working efficiency is improved.
Description
Technical Field
The invention relates to the technical field of data transmission supervision, in particular to an electronic blood pressure counting data transmission supervision system based on the Internet.
Background
The intelligent sphygmomanometer mainly uploads the measurement data of the traditional sphygmomanometer to a mobile phone and a cloud terminal by utilizing various communication means, so that a user of the sphygmomanometer, relatives and friends of the user or a doctor can see the measurement data of the user at any time and any place;
however, in the prior art, the usage modes of the electronic sphygmomanometer of each user cannot be reasonably and accurately matched, and the measurement accuracy is reduced.
Disclosure of Invention
The invention aims to provide an electronic blood pressure counting data transmission monitoring system based on the internet, wherein a hardware data analysis unit is used for analyzing the running data of an electronic sphygmomanometer in each area, each area is marked as h, h =1, 2, … …, g and g are positive integers, the electronic sphygmomanometer is divided into two using modes, namely self-service measurement and auxiliary measurement, the data counting time is set, then a self-service measurement quantity set is constructed, an auxiliary measurement quantity set is constructed, and then the corresponding repeated measurement quantity in the data counting time is obtained; acquiring self-service measurement average use quantity difference Xi of two adjacent days in the data statistics time through a formula, and acquiring auxiliary measurement average use quantity difference Wi of two adjacent days in the data statistics time through the formula; then sending the self-service measurement quantity set and the corresponding average use quantity difference Xi, and the auxiliary measurement quantity set and the corresponding average use quantity difference Wi to a data transmission platform; the using modes of the electronic sphygmomanometer in each area are analyzed, the stability of the using modes of each area is judged by comparing the average using quantity difference, the accuracy of mode matching is improved, and the working efficiency is improved;
the purpose of the invention can be realized by the following technical scheme:
an electronic blood pressure counting data transmission supervision system based on the internet comprises a hardware data analysis unit, a duration data monitoring unit, an analysis model construction unit, a data transmission platform, a numerical analysis unit, a registration login unit and a database;
the hardware data analysis unit is used for analyzing the operation data of the electronic sphygmomanometer in each area, and marks each area as h, h =1, 2, … …, g and g as positive integers, wherein the specific data analysis process is as follows:
step S1: the electronic sphygmomanometer is divided into two using modes, namely self-service measurement and auxiliary measurement, wherein the self-service measurement means that a user independently completes blood pressure measurement, and the auxiliary measurement means that the user needs a doctor to assist in completing blood pressure measurement;
step S2: setting data statistics time, and marking the number of days of the data statistics time as i, i =1, 2, … …, n, n as a positive integer, and then constructing a self-service measurement quantity set { Ah1, Ah2, …, Aho, …, Ahi }, wherein Ao is expressed in an h area, the number of times of self-service measurement on the day o, and constructing an auxiliary measurement quantity set { Bh1, Bh2, …, Bho, …, Bhi }, wherein Bo is expressed in the h area, the number of times of auxiliary measurement on the day o;
step S3: then acquiring the corresponding repeated measurement quantity within the data statistics time, marking the average repeated measurement quantity of self-service measurement within the data statistics time as CF, and marking the average repeated measurement quantity of auxiliary measurement within the data statistics time as CD;
step S4: by the formula
Obtaining the self-service measurement average use quantity difference Xi of two adjacent days in the data statistics time through a formula
Acquiring the average use quantity difference Wi of the auxiliary measurements of two adjacent days in the data statistics time;
step S5: and then sending the self-service measurement quantity set and the corresponding average use quantity difference Xi, and the auxiliary measurement quantity set and the corresponding average use quantity difference Wi to the data transmission platform.
Further, the time duration data monitoring unit is used for monitoring the time duration of the daily operation of the electronic sphygmomanometer in the statistical time in each area, and the specific data monitoring process is as follows:
step SS 1: acquiring the self-service measurement all-day running time length in the statistical time, marking the all-day running time length as P, wherein P =1, 2, … …, k and k are positive integers, respectively marking the self-service measurement and the auxiliary measurement as a and b, constructing a self-service measurement all-day running time length set { Pah1, Pah2, …, Paho, … and Pahi }, wherein Po is the running time length of the self-service measurement electronic sphygmomanometer on the day o, and constructing an auxiliary measurement all-day running time length set { Pbh1, Pbh2, …, Pbho, … and Pbhi };
step SS 2: comparing the self-service measurement operation time length and the auxiliary measurement operation time length with an operation time length threshold range, wherein different operation time length threshold ranges correspond to different operation time length grades, the different operation time length grades are 1, 2, … …, n, and weight assignment is carried out on the different operation time length grades, namely e1+ e2+ … + en =1, and e1 > e2 > … > en;
step SS 3: then acquiring a self-service measurement all-day operation time length weight set { ePah1, ePahe2, …, efPaho, … and enPahi }, wherein the efPaho is expressed in an h area, the operation time length of the self-service measurement electronic sphygmomanometer on the day o corresponds to the weight assignment of the f time grade, and acquiring an auxiliary measurement all-day operation time length weight set { ePah1, ePah 2, …, efPbho, … and enPbhi }, wherein the efPbho is expressed in the h area, and the operation time length of the auxiliary measurement electronic sphygmomanometer on the day o corresponds to the weight assignment of the f time grade;
step SS 4: performing subset average value calculation on the self-service measurement all-day running time length weight set and the auxiliary measurement all-day running time length weight set, marking the subset average value of the self-service measurement all-day running time length weight set as an average running time length grade weight coefficient of self-service measurement in each area, and setting a number QZah; marking the subset average value of the auxiliary measurement all-day running time length weight set as an average running time length grade weight coefficient of auxiliary measurement in each area, and setting a serial number QZbh; and then, the operation time length grade weight coefficients QZah and QZbh are sent to the data transmission platform together.
Further, the numerical analysis unit is configured to perform accuracy analysis on the measured values of the electronic sphygmomanometer in each region, where the specific accuracy analysis process is as follows:
step T1: acquiring the average difference value of self-service measurement and auxiliary measurement of the measured value of each regional electronic sphygmomanometer within data statistics time, and marking the average difference value of self-service measurement and auxiliary measurement of the measured value of each regional electronic sphygmomanometer as CZhai and CZhbi;
step T2: acquiring the error times of self-service measurement and auxiliary measurement of the measured values of the electronic blood pressure meters in each region, and respectively marking the error times of the self-service measurement and the auxiliary measurement of the measured values of the electronic blood pressure meters in each region as CShai and CShbi;
step T3: by the formula
Obtaining accuracy coefficients RThai of self-help measurement in each region, wherein s1 and s2 are proportionality coefficients, s1 is larger than s2 is larger than 0, beta 1 is an error correction factor, the value is 2.36, and the self-help measurement accuracy coefficients RThai are obtained through a formula
Acquiring an accuracy coefficient RThbi of auxiliary measurement in each region, wherein s3 and s4 are proportional coefficients, s3 is greater than s4 is greater than 0, and beta 2 is an error correction factor and is taken as 2.53;
step T4: and sending the self-service measurement accuracy coefficient RThai and the auxiliary measurement accuracy coefficient RThbi in each region to a data transmission platform.
Further, the data transmission platform marks the self-service measurement quantity set and the corresponding average use quantity difference Xi, the average running time length grade weight coefficient of auxiliary measurement in each area and the accuracy coefficient RThai of self-service measurement in each area as self-service measurement analysis model factors, sends the self-service measurement analysis model factors to the analysis model construction unit, marks the auxiliary measurement quantity set and the corresponding average use quantity difference Wi, the average running time length grade weight coefficient of auxiliary measurement in each area and the accuracy coefficient RThbi of auxiliary measurement as auxiliary measurement analysis model factors, and sends the auxiliary measurement analysis model factors to the analysis model construction unit.
Further, the analysis model construction unit is used for constructing analysis models for self-service measurement and auxiliary measurement, so that the use modes of all the areas are reasonably matched, and the specific construction and matching process is as follows:
step TT 1: a self-service measurement analysis model is constructed through the self-service measurement accuracy coefficient RThai, and the formula of the self-service measurement analysis model is
Wherein BNah is a self-service measurement analysis coefficient of each area;
step TT 2: constructing an auxiliary measurement analysis model through the accuracy coefficient RThbi of auxiliary measurement, wherein the formula of the auxiliary measurement analysis model is as follows
Wherein BNbh is an auxiliary measurement analysis coefficient of each region;
step TT 3: comparing the self-service measurement analysis coefficients and the auxiliary measurement analysis coefficients in each region:
if the self-service measurement analysis coefficient in the region is larger than the auxiliary measurement analysis coefficient, judging that the self-service measurement using mode is suitable in the corresponding region, marking the self-service measurement using mode as a self-service measurement region, and then sending the self-service measurement region to a mobile phone terminal of a manager;
if the self-service measurement analysis coefficient in the region is less than the auxiliary measurement analysis coefficient, judging that the corresponding region is suitable for an auxiliary measurement use mode, marking the auxiliary measurement use mode as an auxiliary measurement region, and then sending the auxiliary measurement region to a mobile phone terminal of a manager;
if the self-service measurement analysis coefficient = auxiliary measurement analysis coefficient in the region, acquiring the average age of the measurement user in the corresponding region, if the average age is less than or equal to 35, marking the corresponding region as a self-service measurement region, and if the average age is greater than 35, marking the corresponding region as an auxiliary measurement region.
Further, the registration login unit is used for the manager and the user to submit manager information and user information for registration through the mobile phone terminal, and data storage is carried out on the manager information and the user information which are successfully registered, the manager information comprises the name, the age, the time of entry and the mobile phone number of real name authentication of the user, and the user information comprises the name, the age, the time of entry and the mobile phone number of real name authentication of the user.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, a hardware data analysis unit is used for analyzing the running data of the electronic sphygmomanometer in each area, each area is marked as h, h =1, 2, … …, g and g are positive integers, the electronic sphygmomanometer is divided into two using modes, namely self-service measurement and auxiliary measurement, data statistics time is set, then a self-service measurement quantity set is constructed, an auxiliary measurement quantity set is constructed, and then the corresponding repeated measurement quantity within the data statistics time is obtained; acquiring self-service measurement average use quantity difference Xi of two adjacent days in the data statistics time through a formula, and acquiring auxiliary measurement average use quantity difference Wi of two adjacent days in the data statistics time through the formula; then sending the self-service measurement quantity set and the corresponding average use quantity difference Xi, and the auxiliary measurement quantity set and the corresponding average use quantity difference Wi to a data transmission platform; the using modes of the electronic sphygmomanometer in each area are analyzed, the stability of the using modes of each area is judged by comparing the average using quantity difference, the accuracy of mode matching is improved, and the working efficiency is improved;
2. according to the method, an analysis model is built for self-service measurement and auxiliary measurement through an analysis model building unit, so that the use modes of each region are reasonably matched, a self-service measurement analysis model is built through the accuracy coefficient RThai of the self-service measurement, an auxiliary measurement analysis model is built through the accuracy coefficient RThbi of the auxiliary measurement, and the self-service measurement analysis coefficient and the auxiliary measurement analysis coefficient in each region are compared; the measuring modes of all the areas are reasonably matched, so that the use quality of a user is enhanced, the working intensity of medical personnel is reduced, the labor cost is saved, and the working efficiency is improved.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an electronic blood pressure counting data transmission monitoring system based on the internet comprises a hardware data analysis unit, a duration data monitoring unit, an analysis model construction unit, a data transmission platform, a numerical analysis unit, a registration unit and a database;
the registration login unit is used for the manager and the user to submit manager information and user information for registration through the mobile phone terminal, and data storage is carried out on the manager information and the user information which are successfully registered, the manager information comprises the name, the age, the time of entry and the mobile phone number of real name authentication of the user, and the user information comprises the name, the age, the time of entry and the mobile phone number of real name authentication of the user;
the hardware data analysis unit is used for analyzing the operation data of the electronic sphygmomanometer in each area, and marks each area as h, h =1, 2, … …, g and g as positive integers, wherein the specific data analysis process is as follows:
step S1: the electronic sphygmomanometer is divided into two using modes, namely self-service measurement and auxiliary measurement, wherein the self-service measurement means that a user independently completes blood pressure measurement, and the auxiliary measurement means that the user needs a doctor to assist in completing blood pressure measurement;
step S2: setting data statistics time, and marking the number of days of the data statistics time as i, i =1, 2, … …, n, n as a positive integer, and then constructing a self-service measurement quantity set { Ah1, Ah2, …, Aho, …, Ahi }, wherein Ao is expressed in an h area, the number of times of self-service measurement on the day o, and constructing an auxiliary measurement quantity set { Bh1, Bh2, …, Bho, …, Bhi }, wherein Bo is expressed in the h area, the number of times of auxiliary measurement on the day o;
step S3: then acquiring the corresponding repeated measurement quantity within the data statistics time, marking the average repeated measurement quantity of self-service measurement within the data statistics time as CF, and marking the average repeated measurement quantity of auxiliary measurement within the data statistics time as CD;
step S4: by the formula
Obtaining the self-service measurement average use quantity difference Xi of two adjacent days in the data statistics time through a formula
Acquiring the average use quantity difference Wi of the auxiliary measurements of two adjacent days in the data statistics time;
step S5: then sending the self-service measurement quantity set and the corresponding average use quantity difference Xi, and the auxiliary measurement quantity set and the corresponding average use quantity difference Wi to a data transmission platform;
the time length data monitoring unit is used for monitoring long-term data of the electronic sphygmomanometer running every day in statistical time in each area, and the specific data monitoring process is as follows:
step SS 1: acquiring the self-service measurement all-day running time length in the statistical time, marking the all-day running time length as P, wherein P =1, 2, … …, k and k are positive integers, respectively marking the self-service measurement and the auxiliary measurement as a and b, constructing a self-service measurement all-day running time length set { Pah1, Pah2, …, Paho, … and Pahi }, wherein Po is the running time length of the self-service measurement electronic sphygmomanometer on the day o, and constructing an auxiliary measurement all-day running time length set { Pbh1, Pbh2, …, Pbho, … and Pbhi };
step SS 2: comparing the self-service measurement operation time length and the auxiliary measurement operation time length with an operation time length threshold range, wherein different operation time length threshold ranges correspond to different operation time length grades, the different operation time length grades are 1, 2, … …, n, and weight assignment is carried out on the different operation time length grades, namely e1+ e2+ … + en =1, and e1 > e2 > … > en;
step SS 3: then acquiring a self-service measurement all-day operation time length weight set { ePah1, ePahe2, …, efPaho, … and enPahi }, wherein the efPaho is expressed in an h area, the operation time length of the self-service measurement electronic sphygmomanometer on the day o corresponds to the weight assignment of the f time grade, and acquiring an auxiliary measurement all-day operation time length weight set { ePah1, ePah 2, …, efPbho, … and enPbhi }, wherein the efPbho is expressed in the h area, and the operation time length of the auxiliary measurement electronic sphygmomanometer on the day o corresponds to the weight assignment of the f time grade;
step SS 4: performing subset average value calculation on the self-service measurement all-day running time length weight set and the auxiliary measurement all-day running time length weight set, marking the subset average value of the self-service measurement all-day running time length weight set as an average running time length grade weight coefficient of self-service measurement in each area, and setting a number QZah; marking the subset average value of the auxiliary measurement all-day running time length weight set as an average running time length grade weight coefficient of auxiliary measurement in each area, and setting a serial number QZbh; then, the operation duration grade weight coefficients QZah and QZbh are sent to a data transmission platform together;
the numerical analysis unit is used for carrying out accuracy analysis on the measured values of the electronic sphygmomanometer in each region, and the specific accuracy analysis process is as follows:
step T1: acquiring the average difference value of self-service measurement and auxiliary measurement of the measured value of each regional electronic sphygmomanometer within data statistics time, and marking the average difference value of self-service measurement and auxiliary measurement of the measured value of each regional electronic sphygmomanometer as CZhai and CZhbi;
step T2: acquiring the error times of self-service measurement and auxiliary measurement of the measured values of the electronic blood pressure meters in each region, and respectively marking the error times of the self-service measurement and the auxiliary measurement of the measured values of the electronic blood pressure meters in each region as CShai and CShbi;
step T3: by the formula
Obtaining accuracy coefficients RThai of self-help measurement in each region, wherein s1 and s2 are proportionality coefficients, s1 is larger than s2 is larger than 0, beta 1 is an error correction factor, the value is 2.36, and the self-help measurement accuracy coefficients RThai are obtained through a formula
Obtaining accuracy coefficients of auxiliary measurements in various regionsRThbi, wherein s3 and s4 are proportional coefficients, s3 is greater than s4 is greater than 0, and beta 2 is an error correction factor and takes a value of 2.53;
step T4: sending the self-service measurement accuracy coefficient RThai and the auxiliary measurement accuracy coefficient RThbi in each area to a data transmission platform;
the data transmission platform marks the self-service measurement quantity set and the corresponding average use quantity difference Xi, the average running time length grade weight coefficient of auxiliary measurement in each area and the accuracy coefficient RThai of self-service measurement in each area as self-service measurement analysis model factors, sends the self-service measurement analysis model factors to an analysis model construction unit, marks the auxiliary measurement quantity set and the corresponding average use quantity difference Wi, the average running time length grade weight coefficient of auxiliary measurement in each area and the accuracy coefficient RThbi of auxiliary measurement as auxiliary measurement analysis model factors, and sends the auxiliary measurement analysis model factors to an analysis model construction unit;
the analysis model construction unit is used for constructing the analysis model for self-service measurement and auxiliary measurement, thereby reasonably matching the use modes of each region, enhancing the use quality of users, simultaneously reducing the working intensity of medical personnel, saving the labor cost, improving the working efficiency, and specifically constructing and matching the process as follows:
step TT 1: a self-service measurement analysis model is constructed through the self-service measurement accuracy coefficient RThai, and the formula of the self-service measurement analysis model is
Wherein BNah is a self-service measurement analysis coefficient of each area;
step TT 2: constructing an auxiliary measurement analysis model through the accuracy coefficient RThbi of auxiliary measurement, wherein the formula of the auxiliary measurement analysis model is as follows
Wherein BNbh is an auxiliary measurement analysis coefficient of each region;
step TT 3: comparing the self-service measurement analysis coefficients and the auxiliary measurement analysis coefficients in each region:
if the self-service measurement analysis coefficient in the region is larger than the auxiliary measurement analysis coefficient, judging that the self-service measurement using mode is suitable in the corresponding region, marking the self-service measurement using mode as a self-service measurement region, and then sending the self-service measurement region to a mobile phone terminal of a manager;
if the self-service measurement analysis coefficient in the region is less than the auxiliary measurement analysis coefficient, judging that the corresponding region is suitable for an auxiliary measurement use mode, marking the auxiliary measurement use mode as an auxiliary measurement region, and then sending the auxiliary measurement region to a mobile phone terminal of a manager;
if the self-service measurement analysis coefficient = auxiliary measurement analysis coefficient in the region, acquiring the average age of the measurement user in the corresponding region, if the average age is less than or equal to 35, marking the corresponding region as a self-service measurement region, and if the average age is greater than 35, marking the corresponding region as an auxiliary measurement region.
When the self-service measurement analysis system works, a data transmission platform marks a self-service measurement quantity set and a corresponding average use quantity difference Xi, an average running time grade weight coefficient of auxiliary measurement in each area and an accuracy coefficient RThai of the self-service measurement in each area as self-service measurement analysis model factors, sends the self-service measurement analysis model factors to an analysis model construction unit, marks an auxiliary measurement quantity set and a corresponding average use quantity difference Wi, an average running time grade weight coefficient of the auxiliary measurement in each area and an accuracy coefficient RThbi of the auxiliary measurement as auxiliary measurement analysis model factors, and sends the auxiliary measurement analysis model factors to an analysis model construction unit; the analysis model construction unit is used for constructing analysis models for self-service measurement and auxiliary measurement, so that the use modes of all the regions are reasonably matched; the use quality of the user is enhanced, the working intensity of medical staff is reduced, the labor cost is saved, and the working efficiency is improved.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula which obtains the latest real situation by acquiring a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (5)
1. An electronic blood pressure counting data transmission monitoring system based on the internet is characterized by comprising a hardware data analysis unit, a time length data monitoring unit, an analysis model construction unit, a data transmission platform and a numerical analysis unit;
the hardware data analysis unit is used for analyzing the operation data of the electronic sphygmomanometer in each area, and marks each area as h, h =1, 2, … …, g and g as positive integers, wherein the specific data analysis process is as follows:
step S1: the electronic sphygmomanometer is divided into two using modes, namely self-service measurement and auxiliary measurement, wherein the self-service measurement means that a user independently completes blood pressure measurement, and the auxiliary measurement means that the user needs a doctor to assist in completing blood pressure measurement;
step S2: setting data statistics time, marking the number of days of the data statistics time as i, i =1, 2, … …, n, n is a positive integer, then constructing a self-help measurement quantity set { Ah1, Ah2, …, Aho, …, Ahi }, and constructing an auxiliary measurement quantity set { Bh1, Bh2, …, Bho, …, Bhi };
step S3: then acquiring the corresponding repeated measurement quantity within the data statistics time, marking the average repeated measurement quantity of self-service measurement within the data statistics time as CF, and marking the average repeated measurement quantity of auxiliary measurement within the data statistics time as CD;
step S4: by the formula
Obtaining the self-service measurement average use quantity difference Xi of two adjacent days in the data statistics time through a formula
Acquiring the average use quantity difference Wi of the auxiliary measurements of two adjacent days in the data statistics time;
step S5: and then sending the self-service measurement quantity set and the corresponding average use quantity difference Xi, and the auxiliary measurement quantity set and the corresponding average use quantity difference Wi to the data transmission platform.
2. The system according to claim 1, wherein the time duration data monitoring unit is configured to monitor the daily running time of the electronic sphygmomanometer within the statistical time in each area, and the specific data monitoring process is as follows:
step SS 1: constructing a self-service measurement all-day running time set { Pah1, Pah2, …, Paho, … and Pahi }, and constructing an auxiliary measurement all-day running time set { Pbh1, Pbh2, …, Pbho, … and Pbhi };
step SS 2: comparing the self-service measurement operation time length and the auxiliary measurement operation time length with an operation time length threshold range, wherein different operation time length threshold ranges correspond to different operation time length grades, the different operation time length grades are 1, 2, … …, n, and weight assignment is carried out on the different operation time length grades, namely e1+ e2+ … + en =1, and e1 > e2 > … > en;
step SS 3: then acquiring self-service measurement all-day running time length weight sets { ePah1, ePahe2, …, efPaho, … and enPahi }, and acquiring auxiliary measurement all-day running time length weight sets { ePbh1, ePbh2, …, efPbho, … and enPbhi };
step SS 4: acquiring the average running time length grade weight coefficient QZah of self-service measurement in each area; the average running time length grade weight coefficient QZbh of auxiliary measurement in each area is added; and then, the operation time length grade weight coefficients QZah and QZbh are sent to the data transmission platform together.
3. The system according to claim 1, wherein the numerical analysis unit is configured to perform accuracy analysis on the electronic blood pressure measurement values in each region, and the accuracy analysis process includes:
step T1: obtaining the average difference value of self-service measurement and auxiliary measurement of the measured value of each regional electronic sphygmomanometer and the error times of self-service measurement and auxiliary measurement of the measured value of each regional electronic sphygmomanometer in the data statistics time through a formula
Acquiring accuracy coefficients RThai of self-service measurement in each region, wherein s1 and s2 are proportional coefficients, s1 is greater than s2 is greater than 0, and beta 1 is an error correction factor and has a value of 2.36; by the formula
Acquiring an accuracy coefficient RThbi of auxiliary measurement in each region, wherein s3 and s4 are proportional coefficients, s3 is greater than s4 is greater than 0, and beta 2 is an error correction factor and is taken as 2.53; and sending the self-service measurement accuracy coefficient RThai and the auxiliary measurement accuracy coefficient RThbi in each region to a data transmission platform.
4. The system according to claim 1, wherein the data transmission platform marks the self-service measurement quantity set and the corresponding average usage quantity difference Xi, the average running time duration grade weight coefficient of the auxiliary measurement in each region, and the accuracy coefficient RThai of the self-service measurement in each region as self-service measurement analysis model factors, sends the self-service measurement analysis model factors to the analysis model construction unit, marks the auxiliary measurement quantity set and the corresponding average usage quantity difference Wi, the average running time duration grade weight coefficient of the auxiliary measurement in each region, and the accuracy coefficient RThbi of the auxiliary measurement as auxiliary measurement analysis model factors, and sends the auxiliary measurement analysis model factors to the analysis model construction unit.
5. The system according to claim 1, wherein the analysis model construction unit is configured to construct an analysis model for self-service measurement and auxiliary measurement, so as to reasonably match usage modes for each region, and the specific construction and matching process includes: and constructing a self-service measurement analysis model through the self-service measurement accuracy coefficient RThai, constructing an auxiliary measurement analysis model through the auxiliary measurement accuracy coefficient RThbi, and comparing the self-service measurement analysis coefficient and the auxiliary measurement analysis coefficient in each area.
Priority Applications (1)
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