CN113951863A - Data correction method and device, electronic equipment and computer readable storage medium - Google Patents
Data correction method and device, electronic equipment and computer readable storage medium Download PDFInfo
- Publication number
- CN113951863A CN113951863A CN202111261279.5A CN202111261279A CN113951863A CN 113951863 A CN113951863 A CN 113951863A CN 202111261279 A CN202111261279 A CN 202111261279A CN 113951863 A CN113951863 A CN 113951863A
- Authority
- CN
- China
- Prior art keywords
- body fat
- fat rate
- value
- historical
- current
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012937 correction Methods 0.000 title claims abstract description 235
- 238000000034 method Methods 0.000 title claims abstract description 55
- 210000000577 adipose tissue Anatomy 0.000 claims abstract description 305
- 238000005259 measurement Methods 0.000 claims abstract description 228
- 230000037396 body weight Effects 0.000 claims description 55
- 238000004590 computer program Methods 0.000 claims description 8
- 150000002632 lipids Chemical class 0.000 claims description 6
- 239000002243 precursor Substances 0.000 claims description 5
- 230000007423 decrease Effects 0.000 claims 3
- 238000012545 processing Methods 0.000 description 8
- 238000009499 grossing Methods 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 5
- 238000012417 linear regression Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000035622 drinking Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/053—Measuring electrical impedance or conductance of a portion of the body
- A61B5/0537—Measuring body composition by impedance, e.g. tissue hydration or fat content
Abstract
The invention provides a data correction method, a data correction device, electronic equipment and a computer readable storage medium, wherein a current body fat rate measured value and a plurality of historical body fat rate correction values meeting preset conditions are obtained; weighting the current body fat rate measured value and the plurality of historical body fat rate correction values based on a preset weight coefficient to obtain a current body fat rate correction value corresponding to the current body fat rate measured value; wherein the weighting factor corresponding to each historical body fat rate correction value is associated with the time interval between the first measurement time and the current measurement time corresponding to the historical body fat rate correction value. According to the method, the weighting coefficient corresponding to each historical body fat rate correction value is determined according to the time interval between the first measurement time and the current measurement time corresponding to each historical body fat rate correction value, and the obtained current body fat rate measurement value and the plurality of historical body fat rate correction values are weighted based on different weighting coefficients, so that the accuracy of the obtained current body fat rate correction value can be improved.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a data correction method, apparatus, electronic device, and computer-readable storage medium.
Background
The human body composition scale can be used for measuring the body fat rate of a human body, in a mode of related technology, a multiple linear regression method is usually adopted for formula fitting to calculate the human body composition, parameters such as sex, age, height, weight, impedance and the like are usually required to be used as independent variables, the body fat rate measured by a dual-energy X-ray absorption method or an underwater weighing method is used as dependent variables for linear regression, and the accuracy of the body fat rate obtained by the multiple linear regression formula is poor due to the fact that the weight and the impedance of the same person are usually changed under different states; in another mode, a mode of assigning weights to the current body fat rate measurement value and the last body fat rate prediction value may be adopted to calculate the current body fat rate prediction value, so as to implement the correction of the current body fat rate measurement value, but this mode is easily affected by random factors, resulting in poor accuracy of the corrected body fat rate.
Disclosure of Invention
The invention aims to provide a data correction method, a data correction device, electronic equipment and a computer readable storage medium, so as to enhance the repeatability and stability of data measurement.
The invention provides a data correction method, which comprises the following steps: obtaining a current body fat rate measurement value and a plurality of historical body fat rate correction values meeting preset conditions; weighting the current body fat rate measured value and the plurality of historical body fat rate correction values based on a preset weight coefficient to obtain a current body fat rate correction value corresponding to the current body fat rate measured value; wherein the weighting factor corresponding to each historical body fat rate correction value is associated with the time interval between the first measurement time and the current measurement time corresponding to the historical body fat rate correction value.
Further, a plurality of historical body fat rate correction values satisfying the preset condition are obtained by: acquiring multiple groups of historical measurement data; wherein each set of historical measurement data comprises: historical measurement time, and a historical body fat rate correction value sample and a body weight value sample measured at the historical measurement time; for each set of historical measurement data, calculating a first time interval absolute value of a historical measurement time and a current measurement time in the set of historical measurement data, and calculating a first body weight difference value absolute value between a weight value sample and a weight value measured at the current measurement time; and if the absolute value of the first time interval meets a first preset threshold value and the absolute value of the first body weight difference value meets a second preset threshold value, determining a historical body fat rate correction value sample in the group of historical measurement data as the historical body fat rate correction value meeting a preset condition.
Further, the weighting factor includes: a plurality of first weight coefficients respectively corresponding to the plurality of groups of historical body fat rate correction values; the plurality of first weight coefficients are obtained by: for each historical body fat rate correction value, acquiring a second time interval absolute value between the first measurement time and the current measurement time corresponding to the historical body fat rate correction value and a second body weight difference absolute value between the body weight value measured at the first measurement time and the body weight value measured at the current measurement time; calculating the Euclidean distance between the current body fat rate measurement value and the historical body fat rate correction value based on the historical body fat rate correction value, the current body fat rate measurement value and a preset interval parameter; and calculating a first weight coefficient corresponding to the historical body fat rate correction value based on the second time interval absolute value, the second body weight difference absolute value and the Euclidean distance.
Further, the step of calculating a first weight coefficient corresponding to the historical body fat rate correction value based on the second time interval absolute value, the second body weight difference absolute value, and the euclidean distance includes: calculating a first adjustment coefficient corresponding to the historical body fat rate correction value based on a preset parameter, a second time interval absolute value, a second body weight difference absolute value and the Euclidean distance; the first adjusting coefficient is reduced along with the increase of the absolute value of the second time interval, reduced along with the increase of the absolute value of the second body weight difference and reduced along with the increase of the Euclidean distance; determining a second adjustment coefficient corresponding to the precursor lipid rate measurement value; summing the first adjustment coefficient and the second adjustment coefficient corresponding to each historical body fat rate correction value to obtain a summation result; and dividing the first adjustment coefficient corresponding to the historical body fat rate correction value by the summation result to obtain a first weight coefficient corresponding to the historical body fat rate correction value.
Further, the second adjustment factor corresponding to the current body fat rate measurement value is 1.
Further, the weighting factor includes: a second weight coefficient corresponding to the current body fat rate measurement; the second weight coefficient is obtained by: and dividing the second adjustment coefficient corresponding to the current body fat rate measurement value by the summation result to obtain a second weight coefficient corresponding to the current body fat rate measurement value.
Further, the data correction method further includes: and for each historical body fat rate correction value, determining a second time interval absolute value corresponding to the historical body fat rate correction value as an interval parameter corresponding to the historical body fat rate correction value.
The invention provides a data correction device, which comprises an acquisition module, a correction module and a correction module, wherein the acquisition module is used for acquiring a current body fat rate measured value and a plurality of historical body fat rate correction values meeting preset conditions; the correction module is used for weighting the current body fat rate measurement value and the plurality of historical body fat rate correction values based on a preset weight coefficient to obtain a current body fat rate correction value corresponding to the current body fat rate measurement value; wherein the weighting factor corresponding to each historical body fat rate correction value is associated with the time interval between the first measurement time and the current measurement time corresponding to the historical body fat rate correction value.
The electronic device provided by the invention comprises a memory and a processor, wherein a computer program capable of running on the processor is stored in the memory, and the data correction method is realized when the processor executes the computer program.
The invention provides a computer readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to execute the data correction method.
According to the data correction method, the data correction device, the electronic equipment and the computer readable storage medium, a current body fat rate measurement value and a plurality of historical body fat rate correction values meeting preset conditions are obtained; weighting the current body fat rate measured value and the plurality of historical body fat rate correction values based on a preset weight coefficient to obtain a current body fat rate correction value corresponding to the current body fat rate measured value; wherein the weighting factor corresponding to each historical body fat rate correction value is associated with the time interval between the first measurement time and the current measurement time corresponding to the historical body fat rate correction value. According to the method, the weighting coefficient corresponding to each historical body fat rate correction value is determined according to the time interval between the first measurement time and the current measurement time corresponding to each historical body fat rate correction value, and the obtained current body fat rate measurement value and the plurality of historical body fat rate correction values are weighted based on different weighting coefficients, so that the accuracy of the obtained current body fat rate correction value can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a data correction method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another data correction method according to an embodiment of the present invention;
FIG. 3 is a graph of body fat mass ratio measurements according to an embodiment of the present invention;
FIG. 4 is a graph of data from another body fat rate measurement provided in accordance with an embodiment of the present invention;
fig. 5 is a flowchart of a data correction method according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a data correction apparatus according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment 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 some, but not all, embodiments of the present invention. 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.
At present, most of traditional human body composition calculation methods adopt a multiple linear regression method for formula fitting, and the method does not consider the weight and impedance changes of the same person in different states, and the weightIs generally less than 2 kg; the change of impedance generally causes larger measurement errors due to the contact area between the skin and the electrode plate, the moisture degree of the skin, the water drinking amount, the system error of an intelligent human body composition scale and the like, the error can reach 500 ohms, the change of the body weight and the impedance enables the body fat rate obtained by a multiple linear regression formula to change by more than 2.5 percent, and the accuracy is poor; another approach is to use a simple exponential smoothing formula: PBFPt＝α*PBFRt+(1-α)*PBFPt-1α is a smoothing coefficient, PBFPtIs the predicted value at time t, PBFRtThe measured value at the current time t and the predicted value of the simple exponential smoothing formula at each time only adopt the current measured value and the result predicted last time, and are influenced by random factors to cause large fluctuation, so that the accuracy of the predicted value data is poor. Based on this, the embodiments of the present invention provide a data correction method, apparatus, electronic device and computer-readable storage medium, which may be applied to applications that require data correction.
In order to facilitate understanding of the embodiment, a detailed description is first given of a data correction method disclosed in the embodiment of the present invention; as shown in fig. 1, the method comprises the steps of:
step S102, obtaining a current body fat rate measurement value and a plurality of historical body fat rate correction values meeting preset conditions;
the current body fat rate measurement value can be an actual measurement value of the current body fat rate measured by measuring equipment such as an intelligent human body composition scale; the preset condition may be set according to an actual requirement, for example, the preset condition may be that a time interval between a measurement time corresponding to the historical body fat percentage and a measurement time corresponding to the current body fat percentage measurement value is smaller than a preset threshold value; in practical implementation, when the current body fat rate measurement value needs to be corrected, the current body fat rate measurement value and m historical body fat rate correction values of the same user are usually required to be read, n historical body fat rate correction values meeting preset conditions are selected from the m historical body fat rate correction values of the user, and the selected n historical body fat rate correction values meeting the preset conditions are used for participating in data correction of the current body fat rate measurement value.
Step S104, weighting the current body fat rate measurement value and the plurality of historical body fat rate correction values based on a preset weight coefficient to obtain a current body fat rate correction value corresponding to the current body fat rate measurement value; wherein the weighting factor corresponding to each historical body fat rate correction value is associated with the time interval between the first measurement time and the current measurement time corresponding to the historical body fat rate correction value.
After the current body fat rate measurement value and a plurality of historical body fat rate correction values meeting preset conditions are obtained, different weight coefficients can be set for the current body fat rate measurement value and the plurality of historical body fat rate correction values respectively, and the current body fat rate correction value corresponding to the current body fat rate measurement value is obtained through weighting processing; for the same user, considering that the body fat rate of the user usually changes along with the change of time, that is, the obtained current body fat rate measurement value and the plurality of historical body fat rate correction values may not be the same, in general, the historical body fat rate correction value corresponding to the time closer to the current measurement time is considered to be closer to the current real body fat rate, and it can also be understood that the closer the first measurement time corresponding to the historical body fat rate correction value and the current measurement time is, the closer the historical body fat rate correction value is to the current real body fat rate, therefore, different weight coefficients may be set for the plurality of historical body fat rate correction values; the units used by the time intervals can be set according to actual requirements, for example, the measured time intervals can be set in units of year, month, week, day, hour or minute, an improved simple exponential smoothing method is adopted, historical body fat rate correction values corresponding to different first measurement times are given different weights, the weight coefficient of the historical body fat rate correction value measured in the recent period is increased, after weighting processing, the current body fat rate correction value corresponding to the current body fat rate measurement value can be closer to the average value, and the accuracy of data measurement is enhanced.
The improved simple exponential smoothing formula is:
wherein PBFPIs a corrected value of the current body fat rate, PBFR is a measured value of the current body fat rate, PBFPtIs the t-th historical body fat rate correction value, omega0Is a weight coefficient, ω, of the current body fat rate measurementtIs the weight coefficient of the t-th historical body fat rate correction value.
According to the data correction method, a plurality of historical body fat rate correction values meeting preset conditions are obtained by obtaining a current body fat rate measurement value; weighting the current body fat rate measured value and the plurality of historical body fat rate correction values based on a preset weight coefficient to obtain a current body fat rate correction value corresponding to the current body fat rate measured value; wherein the weighting factor corresponding to each historical body fat rate correction value is associated with the time interval between the first measurement time and the current measurement time corresponding to the historical body fat rate correction value. According to the method, the weighting coefficient corresponding to each historical body fat rate correction value is determined according to the time interval between the first measurement time and the current measurement time corresponding to each historical body fat rate correction value, and the obtained current body fat rate measurement value and the plurality of historical body fat rate correction values are weighted based on different weighting coefficients, so that the accuracy of the obtained current body fat rate correction value can be improved.
The embodiment of the invention also provides another data correction method, which is realized on the basis of the method of the embodiment; as shown in fig. 2, the method comprises the steps of:
step S202, obtaining a current body fat rate measurement value and a plurality of historical body fat rate correction values meeting preset conditions;
in practical implementation, it is usually necessary to read the current body fat rate measurement value and m historical body fat rate correction values of the same user, select n historical body fat rate correction values satisfying the preset condition from the m historical body fat rate correction values of the user, and use the selected n historical body fat rate correction values satisfying the preset condition to participate in data correction of the current body fat rate measurement value.
Specifically, a plurality of historical body fat rate correction values satisfying the preset condition are obtained through the following steps one to three:
acquiring multiple groups of historical measurement data; wherein each set of historical measurement data comprises: historical measurement time, and a historical body fat rate correction value sample and a body weight value sample measured at the historical measurement time;
step two, calculating a first time interval absolute value of the historical measurement time and the current measurement time in each group of historical measurement data, and calculating a first body weight difference value absolute value between the body weight value sample and the body weight value measured at the current measurement time;
and step three, if the absolute value of the first time interval meets a first preset threshold value and the absolute value of the first body weight difference value meets a second preset threshold value, determining a historical body fat rate correction value sample in the group of historical measurement data as the historical body fat rate correction value meeting a preset condition.
In practical implementation, m groups of historical measurement data of the same user are read, each group of data comprises historical measurement time, body fat rate correction value samples and weight value samples, and each group of body fat rate correction value samples and each group of weight value samples are measured at corresponding historical measurement time.
The first time interval absolute value refers to an absolute value of a time interval between each historical measurement time and the current measurement time, the first body weight difference absolute value refers to an absolute value of a difference between a weight value sample measured at each historical measurement time and a weight value measured at the current measurement time, the first time interval absolute values corresponding to each group of historical measurement data are usually different, and the first body weight difference absolute values corresponding to each group of historical measurement data may be partially the same or different.
The first preset threshold and the second preset threshold are two values set in advance, and may be specifically set according to actual requirements, and for each group of historical measurement data, if an absolute value of a first time interval between a historical measurement time and a current measurement time in the group of historical measurement data is greater than the first preset threshold, or if an absolute value of a first body weight difference between a body weight value sample in the group of historical measurement data and a body weight value measured at the current measurement time is greater than the second preset threshold, it may be considered that a body composition of a human body has changed in different states of the same person, the group of historical measurement data and all data measured before the historical measurement time corresponding to the group of historical measurement data do not participate in correction, and if an absolute value of a first time interval between the historical measurement time and the current measurement time in the group of historical measurement data is less than the first preset threshold, and the body weight value sample in the group of historical measurement data and the current measurement data are not involved in correction When the absolute value of the first body weight difference value between the weight values measured in time is smaller than the second preset threshold value, the same person can be considered to be in different states, the human body components are not obviously changed, and the group of historical measurement data can participate in data correction of the current body fat rate measurement value. The method judges whether the historical data measured by the same person can participate in data correction according to the measurement time interval and the weight change, and can improve the accuracy of data correction.
For a further understanding of the above embodiments, reference is made to a graph of body fat mass ratio measurements shown in FIG. 3.
Three curves are shared in the graph, namely a curve obtained by sequentially connecting a plurality of historical body fat rate measurement values according to measurement time (corresponding to a solid line obtained by circular connection in the graph, namely an original body fat rate curve), a curve obtained by sequentially connecting a plurality of body fat rate correction value samples according to measurement time (corresponding to a solid line obtained by star connection in the graph, namely a smoothed body fat rate curve) and a curve obtained by sequentially connecting body weight value samples according to measurement time (corresponding to a dashed line obtained by square connection in the graph, namely a body weight curve), and a target position point is selected on the curves; and determining the measuring time corresponding to the target position point as the current measuring time. And if the time interval and the weight difference value both meet preset values, the group of historical measurement data can participate in data correction of the current body fat rate measured value, the body fat rate correction value obtained by the method has small curve fluctuation, and the accuracy of data correction is high.
Step S204, weighting the current body fat rate measurement value and the plurality of historical body fat rate correction values based on a preset weight coefficient to obtain a current body fat rate correction value corresponding to the current body fat rate measurement value; wherein the weighting factor corresponding to each historical body fat rate correction value is associated with the time interval between the first measurement time and the current measurement time corresponding to the historical body fat rate correction value.
The weight coefficients include: a plurality of first weight coefficients respectively corresponding to the plurality of groups of historical body fat rate correction values; the plurality of first weight coefficients are obtained through the following steps four to six:
step four, acquiring a second time interval absolute value between the first measurement time and the current measurement time corresponding to each historical body fat rate correction value and a second body weight difference absolute value between the body weight value measured at the first measurement time and the body weight value measured at the current measurement time;
calculating the Euclidean distance between the current body fat rate measurement value and the historical body fat rate correction value based on the historical body fat rate correction value, the current body fat rate measurement value and a preset interval parameter;
in practical implementation, the absolute value Δ T of the second time intervaltThe calculation formula is as follows: delta Tt＝T0-TtWherein T is0Indicating the current measurement time, TtIndicating the measurement time, T, corresponding to the correction value of the historical body fat rate0≥Tt,t＝0…n，ΔTtThe units can be years, months, weeks, days, hours or minutes, can be set according to actual needs, and are not limited to the above.
In practical implementation, the absolute value Δ Weight of the second body Weight difference istThe calculation formula is as follows: delta Weightt＝Weight0-WeighttWherein Weight0Weight value, Weight, representing the measurement at the current measurement timetAnd (3) a weight value measured at a measurement time corresponding to the t-th historical body fat rate correction value, wherein t is 0 … n.
In practical implementation, the Euclidean distance Δ d of the change in body fat percentage istThe calculation formula is as follows:
wherein d is0＝0，t＝0…n，dtIs the interval parameter, PBFR is the current body fat rate measurement, PBFPtThe t-th historical body fat rate correction value is obtained.
And for each historical body fat rate correction value, determining a second time interval absolute value corresponding to the historical body fat rate correction value as an interval parameter corresponding to the historical body fat rate correction value.
In practical implementation, the interval parameter d in the Euclidean distance formula of the body fat rate changetMay be calculated using linearly increasing intervals, such as 0 … n, or the time interval Δ T between the current measurement time and the measurement time corresponding to the historical body fat rate correction value may be calculatedtAs interval parameters corresponding to the historical body fat rate correction values.
And step six, calculating a first weight coefficient corresponding to the historical body fat rate correction value based on the second time interval absolute value, the second body weight difference absolute value and the Euclidean distance.
The sixth step can be specifically obtained by the following steps A to D:
step A, calculating a first adjustment coefficient corresponding to the historical body fat rate correction value based on a preset parameter, a second time interval absolute value, a second body weight difference absolute value and an Euclidean distance; the first adjusting coefficient is reduced along with the increase of the absolute value of the second time interval, reduced along with the increase of the absolute value of the second body weight difference and reduced along with the increase of the Euclidean distance;
b, determining a second adjustment coefficient corresponding to the current body fat rate measurement value;
in practical implementation, the PBF is adjustedcoeftThe calculation method of (c) is as follows:
wherein Δ TtThe time interval of the measurement time of the current measurement value of the body fat rate and the historical measurement time corresponding to the tth historical body fat rate correction value can be year, month, week, day, hour or minute; Δ dtThe Euclidean distance between the current body fat rate measured value and the historical body fat rate corrected value; delta WeighttThe difference between the current weight measurement and the historical weight measurement; a. b and c are set adjusting parameters, and when t is 1 … n, PBFcoeftThe first adjustment coefficient corresponding to the historical body fat rate correction value is shown by the formulat、Δdt、ΔWeighttThe larger, the PBFcoeftThe smaller t is 0, the PBFcoeftAnd a second adjustment coefficient corresponding to the current body fat rate measurement value.
The second adjustment factor corresponding to the measured precursor lipid rate is 1. Specifically, when t is 0, the formula
Δ T of (1)t、Δdt、ΔWeighttWhen all are 0, the calculated PBFcoeftIs 1.
Step C, summing the first adjustment coefficient and the second adjustment coefficient corresponding to each historical body fat rate correction value to obtain a summation result;
and D, dividing the first adjustment coefficient corresponding to the historical body fat rate correction value by the summation result to obtain a first weight coefficient corresponding to the historical body fat rate correction value.
In practical implementation, the weighting factor w is calculated as follows:
the denominator is a fixed value obtained by summing the first adjustment coefficient and the second adjustment coefficient corresponding to each of the historical body fat rate correction values, and when t is 1 … n, that is, for each of the historical command correction values, the first adjustment coefficient PBF corresponding to the historical command correction value is usedcoeftThe sum is divided by the value of the first weight coefficient omega to obtain a first weight coefficient omega corresponding to the historical body fat rate correction valuet。
The weight coefficients include: a second weight coefficient corresponding to the current body fat rate measurement; the second weight coefficient is obtained by: and dividing the second adjustment coefficient corresponding to the current body fat rate measurement value by the summation result to obtain a second weight coefficient corresponding to the current body fat rate measurement value.
In practical implementation, when t is 0, the second adjustment coefficient PBF corresponding to the current body fat rate measurement value is usedcoeftIs divided by the summation result to obtain a second weight coefficient omega corresponding to the current body fat rate measurement value0For example, if the second adjustment coefficient corresponding to the precursor lipid rate measurement value is 1, the summation result is divided by 1 to obtain the second weighting coefficient corresponding to the current body lipid rate measurement value.
According to the data correction method, for the same user, an improved simple exponential smoothing method is adopted, different weights are given to historical body fat rate correction values measured at different times, the weight of a recent measurement value is increased, the current body fat rate measurement value of the same user and multiple groups of historical measurement data are adopted to correct the current body fat rate measurement value, fluctuation caused by random factors can be reduced, and repeatability and stability of corrected data are improved.
For a further understanding of the above example, reference is made to another graph of body fat ratio measurement data shown in FIG. 4.
Three curves are shared in the graph, namely a curve obtained by sequentially connecting a plurality of historical body fat rate measurement values according to measurement time (corresponding to a solid line obtained by circular connection in the graph, namely an original body fat rate curve), a curve obtained by sequentially connecting a plurality of body fat rate correction value samples according to measurement time (corresponding to a solid line obtained by star connection in the graph, namely a smoothed body fat rate curve) and a curve obtained by sequentially connecting body weight value samples according to measurement time (corresponding to a dashed line obtained by square connection in the graph, namely a body weight curve), and a target position point is selected on the curves; and determining the measuring time corresponding to the target position point as the current measuring time. Wherein, the time interval of the measuring time corresponding to the target position point and the measuring time corresponding to the point before the target position point is calculated, the weight difference value of the weight value corresponding to the target position point and the weight value corresponding to the point before the target position point is calculated, if the time interval and the weight difference value both meet preset values, the group of historical measuring data can participate in the data correction of the current body fat rate measured value, the historical body fat rate correction value corresponding to the current measuring time is closer to the current real body fat rate according to the corrected body fat rate curve graph in the figure, and can also be understood as that the closer the measuring time corresponding to the historical body fat rate correction value and the current measuring time is, the closer the historical body fat rate correction value is to the current real body fat rate, after weighting processing, the current body fat rate correction value corresponding to the current body fat rate measured value is closer to the average value, the accuracy of data measurement is enhanced.
For a further understanding of the above embodiments, refer to a flow chart of a data correction method shown in fig. 5.
Reading m groups of data currently and historically measured by the same user, wherein the data comprises body fat percentage, weight value and measuring time; calculating the difference between the measurement time interval and the body weight; judging whether the difference value between the measurement time interval and the weight is within a threshold value range; when the measurement time interval is less than or equal to a set threshold and the difference value of the weight is less than or equal to the set threshold, storing the current body fat rate, the body fat rates of the n groups after screening and the corresponding difference values of the time interval and the weight, and entering a correction mode; calculating Euclidean distance of body fat rate change; calculating corresponding weight by adopting the time interval, the weight difference and the Euclidean distance; correcting the current body fat rate to obtain a corrected body fat rate, and displaying and exiting; and when the measurement time interval is greater than the set threshold or the weight is greater than the set threshold, the data and all measured data before the current measurement time do not participate in correction, the current measurement value is displayed, and the operation is exited.
An embodiment of the present invention further provides a data correction apparatus, as shown in fig. 6, the apparatus includes: an obtaining module 60, configured to obtain a plurality of historical body fat rate correction values that satisfy a preset condition when a current body fat rate measurement value is obtained; the correction module 61 is configured to perform weighting processing on the current body fat rate measurement value and the plurality of historical body fat rate correction values based on a preset weight coefficient to obtain a current body fat rate correction value corresponding to the current body fat rate measurement value; wherein the weighting factor corresponding to each historical body fat rate correction value is associated with the time interval between the first measurement time and the current measurement time corresponding to the historical body fat rate correction value.
The data correction device obtains a current body fat rate measurement value and a plurality of historical body fat rate correction values meeting preset conditions; weighting the current body fat rate measured value and the plurality of historical body fat rate correction values based on a preset weight coefficient to obtain a current body fat rate correction value corresponding to the current body fat rate measured value; wherein the weighting factor corresponding to each historical body fat rate correction value is associated with the time interval between the first measurement time and the current measurement time corresponding to the historical body fat rate correction value. The device determines a weight coefficient corresponding to each historical body fat rate correction value according to a time interval between first measurement time and current measurement time corresponding to each historical body fat rate correction value, and performs weighting processing on the obtained current body fat rate measurement value and the plurality of historical body fat rate correction values based on different weight coefficients, so that the accuracy of the obtained current body fat rate correction value can be improved.
Further, the apparatus further comprises a historical body fat rate correction value determination module, wherein the historical body fat rate correction value determination module is used for: acquiring multiple groups of historical measurement data; wherein each set of historical measurement data comprises: historical measurement time, and a historical body fat rate correction value sample and a body weight value sample measured at the historical measurement time; for each set of historical measurement data, calculating a first time interval absolute value of a historical measurement time and a current measurement time in the set of historical measurement data, and calculating a first body weight difference value absolute value between a weight value sample and a weight value measured at the current measurement time; and if the absolute value of the first time interval meets a first preset threshold value and the absolute value of the first body weight difference value meets a second preset threshold value, determining a historical body fat rate correction value sample in the group of historical measurement data as the historical body fat rate correction value meeting a preset condition.
Further, the weighting factor includes: a plurality of first weight coefficients respectively corresponding to the plurality of groups of historical body fat rate correction values; the apparatus also includes a first weight coefficient determination module to: for each historical body fat rate correction value, acquiring a second time interval absolute value between the first measurement time and the current measurement time corresponding to the historical body fat rate correction value and a second body weight difference absolute value between the body weight value measured at the first measurement time and the body weight value measured at the current measurement time; calculating the Euclidean distance between the current body fat rate measurement value and the historical body fat rate correction value based on the historical body fat rate correction value, the current body fat rate measurement value and a preset interval parameter; and calculating a first weight coefficient corresponding to the historical body fat rate correction value based on the second time interval absolute value, the second body weight difference absolute value and the Euclidean distance.
Further, the first weight coefficient determining module is further configured to: calculating a first adjustment coefficient corresponding to the historical body fat rate correction value based on a preset parameter, a second time interval absolute value, a second body weight difference absolute value and the Euclidean distance; the first adjusting coefficient is reduced along with the increase of the absolute value of the second time interval, reduced along with the increase of the absolute value of the second body weight difference and reduced along with the increase of the Euclidean distance; determining a second adjustment coefficient corresponding to the precursor lipid rate measurement value; summing the first adjustment coefficient and the second adjustment coefficient corresponding to each historical body fat rate correction value to obtain a summation result; and dividing the first adjustment coefficient corresponding to the historical body fat rate correction value by the summation result to obtain a first weight coefficient corresponding to the historical body fat rate correction value.
Further, the second adjustment factor corresponding to the current body fat rate measurement value is 1.
Further, the weighting factor includes: a second weight coefficient corresponding to the current body fat rate measurement; the apparatus also includes a second weight coefficient determination module to: and dividing the second adjustment coefficient corresponding to the current body fat rate measurement value by the summation result to obtain a second weight coefficient corresponding to the current body fat rate measurement value.
Further, the first weight coefficient determining module is further configured to: and for each historical body fat rate correction value, determining a second time interval absolute value corresponding to the historical body fat rate correction value as an interval parameter corresponding to the historical body fat rate correction value.
The data correction apparatus provided in the embodiments of the present invention has the same implementation principle and technical effect as those of the foregoing embodiments of the data correction method, and reference may be made to the corresponding contents in the foregoing embodiments of the video coding method for the data correction apparatus.
An embodiment of the present invention further provides an electronic device, as shown in fig. 7, the electronic device includes a processor 130 and a memory 131, the memory 131 stores machine executable instructions capable of being executed by the processor 130, and the processor 130 executes the machine executable instructions to implement the data correction method.
Further, the electronic device shown in fig. 7 further includes a bus 132 and a communication interface 133, and the processor 130, the communication interface 133, and the memory 131 are connected through the bus 132.
The Memory 131 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 133 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used. The bus 132 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 7, but this does not indicate only one bus or one type of bus.
The processor 130 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 130. The Processor 130 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 131, and the processor 130 reads the information in the memory 131 and completes the steps of the method of the foregoing embodiment in combination with the hardware thereof.
The embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to implement the data correction method.
The data correction method, the data correction device, the electronic device, and the computer-readable storage medium provided in the embodiments of the present invention include a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiments, and specific implementation may refer to the method embodiments, and will not be described herein again.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of data correction, the method comprising:
obtaining a current body fat rate measurement value and a plurality of historical body fat rate correction values meeting preset conditions;
weighting the current body fat rate measurement value and the plurality of historical body fat rate correction values based on a preset weight coefficient to obtain a current body fat rate correction value corresponding to the current body fat rate measurement value;
wherein the weighting factor corresponding to each historical body fat rate correction value is associated with the time interval between the first measurement time and the current measurement time corresponding to the historical body fat rate correction value.
2. The method according to claim 1, wherein the plurality of historical body fat rate correction values satisfying the preset condition are obtained by:
acquiring multiple groups of historical measurement data; wherein each set of the historical measurement data comprises: historical measurement time, and a historical body fat rate correction value sample and a body weight value sample measured at the historical measurement time;
for each set of the historical measurement data, calculating a first time interval absolute value of the historical measurement time and the current measurement time in the set of the historical measurement data, and calculating a first body weight difference value absolute value between the body weight value sample and the body weight value measured at the current measurement time;
and if the absolute value of the first time interval meets a first preset threshold value and the absolute value of the first body weight difference value meets a second preset threshold value, determining the historical body fat rate correction value sample in the group of historical measurement data as the historical body fat rate correction value meeting the preset condition.
3. The method of claim 1, wherein the weighting factors comprise: a plurality of first weight coefficients respectively corresponding to the plurality of groups of historical body fat rate correction values; a plurality of the first weight coefficients are obtained by:
for each historical body fat rate correction value, acquiring a second time interval absolute value between the first measurement time and the current measurement time corresponding to the historical body fat rate correction value and a second body weight difference absolute value between the body weight value measured at the first measurement time and the body weight value measured at the current measurement time;
calculating the Euclidean distance between the current body fat rate measured value and the historical body fat rate corrected value based on the historical body fat rate corrected value, the current body fat rate measured value and a preset interval parameter;
and calculating a first weight coefficient corresponding to the historical body fat rate correction value based on the second time interval absolute value, the second body weight difference absolute value and the Euclidean distance.
4. The method of claim 3, wherein the step of calculating the first weight coefficient corresponding to the historical body fat rate correction value based on the second time interval absolute value, the second body weight difference absolute value, and the Euclidean distance comprises:
calculating a first adjustment coefficient corresponding to the historical body fat rate correction value based on a preset parameter, the second time interval absolute value, the second body weight difference absolute value and the Euclidean distance; wherein the first adjustment coefficient decreases with an increase in the absolute value of the second time interval, decreases with an increase in the absolute value of the second body mass difference, and decreases with an increase in the euclidean distance;
determining a second adjustment coefficient corresponding to the current body fat rate measurement value;
summing the first adjustment coefficient and the second adjustment coefficient corresponding to each historical body fat rate correction value to obtain a summation result;
and dividing the first adjustment coefficient corresponding to the historical body fat rate correction value by the summation result to obtain a first weight coefficient corresponding to the historical body fat rate correction value.
5. The method of claim 4, wherein the second adjustment factor is 1 for the current precursor lipid rate measurement.
6. The method of claim 4, wherein the weighting factors comprise: a second weight coefficient corresponding to the current body fat rate measurement; the second weight coefficient is obtained by:
and dividing the second adjustment coefficient corresponding to the current body fat rate measurement value by the summation result to obtain a second weight coefficient corresponding to the current body fat rate measurement value.
7. The method of claim 3, further comprising:
and for each historical body fat rate correction value, determining the second time interval absolute value corresponding to the historical body fat rate correction value as the interval parameter corresponding to the historical body fat rate correction value.
8. A data correction apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring a current body fat rate measurement value and a plurality of historical body fat rate correction values meeting preset conditions;
the correction module is used for weighting the current body fat rate measurement value and the plurality of historical body fat rate correction values based on a preset weight coefficient to obtain a current body fat rate correction value corresponding to the current body fat rate measurement value;
wherein the weighting factor corresponding to each historical body fat rate correction value is associated with the time interval between the first measurement time and the current measurement time corresponding to the historical body fat rate correction value.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any of claims 1-7 when executing the computer program.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, performs the steps of the method of any of the preceding claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111261279.5A CN113951863A (en) | 2021-10-28 | 2021-10-28 | Data correction method and device, electronic equipment and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111261279.5A CN113951863A (en) | 2021-10-28 | 2021-10-28 | Data correction method and device, electronic equipment and computer readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113951863A true CN113951863A (en) | 2022-01-21 |
Family
ID=79467921
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111261279.5A Pending CN113951863A (en) | 2021-10-28 | 2021-10-28 | Data correction method and device, electronic equipment and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113951863A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020022773A1 (en) * | 1998-04-15 | 2002-02-21 | Darrel Drinan | Body attribute analyzer with trend display |
| US20020112898A1 (en) * | 2000-08-04 | 2002-08-22 | Tanita Corporation | Body weight managing apparatus |
| WO2006070126A1 (en) * | 2004-12-23 | 2006-07-06 | Seb S.A. | Apparatus for measuring body composition comprising an improved display device |
-
2021
- 2021-10-28 CN CN202111261279.5A patent/CN113951863A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020022773A1 (en) * | 1998-04-15 | 2002-02-21 | Darrel Drinan | Body attribute analyzer with trend display |
| US20020112898A1 (en) * | 2000-08-04 | 2002-08-22 | Tanita Corporation | Body weight managing apparatus |
| WO2006070126A1 (en) * | 2004-12-23 | 2006-07-06 | Seb S.A. | Apparatus for measuring body composition comprising an improved display device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109558295B (en) | Performance index abnormality detection method and device | |
| CN109543353B (en) | Three-dimensional water vapor inversion method, device, equipment and computer readable storage medium | |
| CN108989889B (en) | Video playing amount prediction method and device and electronic equipment | |
| CN112132485A (en) | Index data processing method and device, electronic equipment and storage medium | |
| US10346870B1 (en) | Dynamic promotion analytics | |
| CN111307751A (en) | Spectrogram baseline correction method, system and detection method in tea near infrared spectrum analysis | |
| CN115600516A (en) | Method and device for tracking position of oil mixing interface, electronic equipment and medium | |
| CN113951863A (en) | Data correction method and device, electronic equipment and computer readable storage medium | |
| CN108732991B (en) | Method and device for correcting motion axis | |
| CN111405370A (en) | Video playback method and device | |
| CN111839209B (en) | Display temperature control method, device, equipment and readable storage medium | |
| CN112182493B (en) | Analog quantity calibration method, analog quantity calibration device, electronic equipment and storage medium | |
| CN109725188B (en) | Current measuring method and device | |
| CN104407366A (en) | Pseudo-range smooth processing method | |
| CN113959511B (en) | Flow metering method, equipment, medium and product based on jet flow water meter | |
| CN109214447B (en) | Disk life prediction method and device | |
| CN108766527B (en) | Method and device for determining food calorie | |
| Goldstein | The construction of standards for measurements subject to growth | |
| US20230266158A1 (en) | Method and system for eccentric load error correction | |
| CN105160087B (en) | A kind of construction method of correlation rule optimal curve model | |
| CN110991745B (en) | Power load prediction method and device, readable medium and electronic equipment | |
| CN109347691B (en) | Data sampling method, device and equipment for Web service | |
| CN109981327B (en) | Method and system for predicting traffic | |
| CN111189471A (en) | Correction method, correction device and computer storage medium | |
| CN116994979B (en) | Square resistance measuring method, electronic device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |