CN114910150A - Calibration method and device of capacitive weight sensor, intelligent pad and storage medium - Google Patents
Calibration method and device of capacitive weight sensor, intelligent pad and storage medium Download PDFInfo
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47C—CHAIRS; SOFAS; BEDS
- A47C23/00—Spring mattresses with rigid frame or forming part of the bedstead, e.g. box springs; Divan bases; Slatted bed bases
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
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Abstract
The invention discloses a calibration method and a calibration device of a capacitive weight sensor, an intelligent pad and a storage medium, which are used for calibrating the weight of the capacitive weight sensor, wherein the capacitive weight sensor comprises a first polar plate, a second polar plate and a dielectric layer positioned between the first polar plate and the second polar plate, and a capacitor formed by the first polar plate and the second polar plate, and the calibration method of the capacitive weight sensor comprises the following steps: when standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor, capacitance values corresponding to the standard mass bodies with different masses one to one are obtained; determining calibration parameters of the capacitive weight sensor according to the mass and the capacitance of each standard mass body; based on the calibration parameters, a calibration model of the capacitive weight sensor is established, and an accurate calibration model of the capacitive weight sensor can be established according to the calibration parameters, so that a simpler calibration method of the capacitive weight sensor with higher accuracy is realized.
Description
Technical Field
The invention relates to the technical field of calibration of capacitive weight sensors, in particular to a calibration method and device of a capacitive weight sensor, an intelligent pad and a storage medium.
Background
Smart mats including sensors are nowadays favored and applied by various application scenarios, such as sensor mats applied in vehicle seats, which can automatically start a vehicle when sensing that a human body is sitting down; or to smart homes such as sofas and mattresses.
The detection function of present intelligent pad is comparatively single, can only judge whether to have placed the heavy object on the intelligent pad or whether the human body sits down, and intelligence output 0/1 signal promptly can not measure the object or human weight that the intelligence was stamped, can't satisfy present diversified demand to intelligent house.
Disclosure of Invention
The invention provides a calibration method and device of a capacitive weight sensor, an intelligent pad and a storage medium, which can calibrate the capacitive weight sensor more accurately.
According to an aspect of the present invention, a calibration method for a capacitive weight sensor is provided, where the calibration method is used to calibrate a weight of the capacitive weight sensor, the capacitive weight sensor includes a first polar plate, a second polar plate, and a dielectric layer located between the first polar plate and the second polar plate, and a capacitor formed by the first polar plate and the second polar plate, and the calibration method for the capacitive weight sensor includes:
when standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor, capacitance values corresponding to the standard mass bodies with different masses one to one are obtained;
determining calibration parameters of the capacitive weight sensor according to the mass of each standard mass body and each capacitance value;
and establishing a calibration model of the capacitive weight sensor based on the calibration parameters.
Optionally, obtaining capacitance values corresponding to standard masses of different masses one to one includes:
acquiring N capacitance measurement values corresponding to the same standard mass body;
calculating the capacitance average value of N capacitance measurement values corresponding to the same standard mass body;
and determining the capacitance average value corresponding to the standard mass body as the capacitance value corresponding to the standard mass body.
Optionally, after establishing the calibration model of the capacitive weight sensor based on the calibration parameters, the method further includes:
determining a capacitance standard deviation corresponding to the standard mass body according to the capacitance measured value and the capacitance average value corresponding to the same standard mass body;
and correcting the calibration model according to the capacitance standard deviation corresponding to each standard mass body.
Optionally, determining calibration parameters of the capacitive weight sensor according to the mass of each standard mass body and each capacitance value includes:
determining a plurality of slope values and a plurality of intercept values based on a linear relation according to the mass sum of each standard mass body and each capacitance value;
averaging the plurality of slope values to determine a slope average value, and averaging the plurality of intercept values to determine an intercept average value;
and determining the slope average value and the intercept average value as calibration parameters of the capacitive weight sensor.
Optionally, determining calibration parameters of the capacitive weight sensor according to the mass of each standard mass body and each capacitance value includes:
based on the initial value of linear fitting, performing linear fitting on the mass sum of each standard mass body and each capacitance value to determine a linear fitting result;
and determining calibration parameters of the capacitive weight sensor according to the linear fitting result.
Optionally, the linear fitting result includes a fitting parameter and a fitting degree;
according to the linear fitting result, determining calibration parameters of the capacitive weight sensor, including:
judging whether the fitting degree is greater than a preset fitting degree;
if so, determining the fitting parameters as calibration parameters of the capacitive weight sensor;
and if not, after the linear fitting initial value is adjusted, returning to the step of performing linear fitting on the mass sum of each standard mass body and each capacitance value based on the linear fitting initial value, determining a linear fitting result until the fitting times reach the preset times, and determining the fitting parameters of the last fitting as the calibration parameters of the capacitive weight sensor.
According to another aspect of the present invention, there is provided a calibration apparatus for a capacitive weight sensor, configured to perform weight calibration on the capacitive weight sensor, where the capacitive weight sensor includes a first electrode plate, a second electrode plate, and a dielectric layer located between the first electrode plate and the second electrode plate, and a capacitor formed by the first electrode plate and the second electrode plate, the calibration apparatus for the capacitive weight sensor includes:
the capacitance value acquisition module is used for acquiring capacitance values corresponding to the standard mass bodies with different masses one to one when the standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor;
the calibration parameter determining module is used for determining calibration parameters of the capacitive weight sensor according to the mass of each standard mass body and each capacitance value;
and the calibration model establishing module is used for establishing a calibration model of the capacitive weight sensor based on the calibration parameters.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the above-described method of calibration of a capacitive weight sensor.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the calibration method of the capacitive weight sensor described above when executed.
According to another aspect of the present invention, there is provided a smart mat including: at least one capacitive weight sensor;
the capacitive weight sensor comprises a first polar plate, a second polar plate and a dielectric layer positioned between the first polar plate and the second polar plate, wherein the first polar plate and the second polar plate form a capacitor;
the capacitive weight sensor is used for weight calibration by adopting the calibration method of the capacitive weight sensor.
According to the calibration method of the capacitive weight sensor provided by the embodiment of the invention, the standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor, and the capacitance values corresponding to the standard mass bodies with different masses one to one are respectively obtained at the same time, so that the calibration parameters of the more accurate capacitive weight sensor can be determined according to a plurality of groups of masses and the corresponding capacitance values, a calibration model of the more accurate capacitive weight sensor can be established according to the calibration parameters, a calibration method of the more simple capacitive weight sensor with higher accuracy is realized, and the weight of an object or a human body can be more accurately determined according to the capacitance value output by the current capacitive weight sensor in practical application.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a capacitive weight sensor according to an embodiment of the present invention;
FIG. 2 is a flow chart of a calibration method for a capacitive weight sensor according to an embodiment of the present invention;
FIG. 3 is a flow chart of another method for calibrating a capacitive weight sensor according to an embodiment of the present invention;
FIG. 4 is a flowchart of a calibration method for a capacitive weight sensor according to an embodiment of the present invention;
FIG. 5 is a flow chart of a calibration method for a capacitive weight sensor according to an embodiment of the present invention;
FIG. 6 is a schematic mechanical diagram of a calibration apparatus for a capacitive weight sensor according to an embodiment of the present invention;
FIG. 7 is a schematic mechanical diagram of another calibration apparatus for a capacitive weight sensor provided in an embodiment of the present invention;
FIG. 8 is a schematic mechanical diagram of a calibration apparatus for a capacitive weight sensor provided in an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the invention provides a calibration method of a capacitive weight sensor, which can be used for carrying out weight calibration on the capacitive weight sensor, and can be executed by the calibration device of the capacitive weight sensor provided by the embodiment of the invention, the calibration device of the capacitive weight sensor can be executed by software and/or hardware, and the calibration device of the capacitive weight sensor can be integrated in the intelligent pad provided by the embodiment of the invention.
Exemplarily, fig. 1 is a schematic structural diagram of a capacitive weight sensor according to an embodiment of the present invention, and as shown in fig. 1, the capacitive weight sensor 00 includes a first plate 10, a second plate 20, and a dielectric layer 30 located between the first plate 10 and the second plate 20, where the first plate 10 and the second plate 20 form a capacitor. The dielectric layer 30 is preferably an elastic flexible dielectric layer, when a heavy object is placed on the capacitive weight sensor 00, the thickness of the dielectric layer 30 is reduced, so that the distance between the first substrate 10 and the second substrate 20 is reduced, that is, the inter-plate distance of the capacitor is reduced, and it can be known according to the capacitance decision formula C ═ epsilon S/4 pi kd that when the inter-plate distance of the capacitor is reduced, the capacitance value is increased, and the inter-plate distance of the capacitor is reduced as the weight of the heavy object is heavier, accordingly, the weight of the heavy object can be determined according to the obtained capacitance value between the two inter-plate electrodes of the capacitor, and in order to make the weight of the heavy object determined according to the capacitance value more accurate, the embodiment of the invention provides a calibration method of the capacitive weight sensor.
Fig. 2 is a flowchart of a calibration method for a capacitive weight sensor according to an embodiment of the present invention, and as shown in fig. 2, the method includes:
and S110, acquiring capacitance values corresponding to the standard mass bodies with different masses one to one when the standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor.
Specifically, the standard mass body is an object with higher mass (Kg) precision, and the standard mass bodies with different masses may be sequentially and respectively placed on one plate surface of the capacitive weight sensor, for example, the standard mass bodies with the masses of M1, M2, M3, … …, and Mk (k is a natural number), the standard mass body with the mass of M1 may be placed on one plate surface of the capacitive weight sensor first to apply pressure to the capacitive weight sensor, and when the standard mass body with the mass of M1 is placed stably on the plate surface of the capacitive weight sensor, the capacitance value C1 corresponding to the standard mass body may be obtained, and then the standard mass body with the mass of M1 is taken down from the plate of the capacitive weight sensor, and the dielectric layer between the two plates of the capacitor is deformed at intervals, and then the standard mass body with the mass of M1 is taken down from the plate of the capacitive weight sensor. For example, a standard mass body with mass M2 may be placed on one plate surface of the capacitive weight sensor, and when the placement is stable, a capacitance value C2 corresponding to the standard mass body with mass M2 is obtained, and so on until k capacitance values C1, C2, C3, … …, Ck corresponding to k standard mass bodies with masses M1, M2, M3, … …, Mk are obtained.
And S120, determining calibration parameters of the capacitive weight sensor according to the mass and the capacitance of each standard mass body.
Specifically, referring to fig. 1, when a standard mass body applies pressure to the capacitive weight sensor 00, the thickness of the medium 30 is reduced, so that the distance between the first substrate 10 and the second substrate 20 is reduced, that is, the plate distance d of the capacitor is reduced, and the larger the mass of the standard mass body is, the smaller the plate distance d of the capacitor is, that is, the mass M of the standard mass body is inversely proportional to the plate distance d of the capacitor, and it can be known that the capacitance value C is inversely proportional to the plate distance d according to the capacitance decision formula C ∈ S/4 π kd (where C is the capacitance value, epsilon is the dielectric constant of the medium between the plates, S is the facing area of the two plates of the capacitor, k is the electrostatic force constant, and d is the distance between the plates), that when the plate distance d of the capacitor is reduced, the capacitance value is increased, and therefore, the pressure applied to the capacitive weight sensor is in direct proportional to the capacitance value C, namely, the mass M of the standard mass body is in a direct proportional relation with the capacitance value C, namely, the larger the mass M of the standard mass body is, the larger the pressure born by the capacitive weight sensor is, the smaller the plate spacing d is, and the larger the capacitance value C is; it can be assumed that the relation between the mass M of the standard mass body and the capacitance C is: m ═ aC + b, where a, c are the calibration parameters, and a is the slope and b is the intercept. Accordingly, a capacitance value may be determined from a plurality of sets of measured capacitance values and the mass of the corresponding proof mass.
And S130, establishing a calibration model of the capacitive weight sensor based on the calibration parameters.
Specifically, after the values of the standard parameters a and b are determined, M ═ aC + b may be used as a calibration model of the capacitive weight sensor, and in practical application, the pressure borne by the current capacitive weight sensor may be determined according to the obtained capacitance value, that is, the weight of the object or human body on the current capacitive weight sensor may be determined.
According to the calibration method of the capacitive weight sensor provided by the embodiment of the invention, the standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor, and the capacitance values corresponding to the standard mass bodies with different masses one to one are respectively obtained at the same time, so that the calibration parameters of the more accurate capacitive weight sensor can be determined according to a plurality of groups of masses and the corresponding capacitance values, a calibration model of the more accurate capacitive weight sensor can be established according to the calibration parameters, a calibration method of the more simple capacitive weight sensor with higher accuracy is realized, and the weight of an object or a human body can be more accurately determined according to the capacitance value output by the current capacitive weight sensor in practical application.
Optionally, fig. 3 is a flowchart of another calibration method for a capacitive weight sensor according to an embodiment of the present invention, and as shown in fig. 3, the method includes:
s210, when the standard mass bodies with different masses apply pressure to the capacitive weight sensor, capacitance values corresponding to the standard mass bodies with different masses one to one are obtained.
And S220, determining a plurality of slope values and a plurality of intercept values according to the mass sum of each standard mass body and each capacitance value and based on a linear relation.
S230, averaging the plurality of slope values to determine a slope average value, and averaging the plurality of intercept values to determine an intercept average value.
And S240, determining the slope average value and the intercept average value as calibration parameters of the capacitive weight sensor.
Specifically, a plurality of slope values a and a plurality of intercept values b may be determined based on the linear relationship M ═ aC + b according to the mass sum and each capacitance value of the acquired standard mass body; for example, a set of calibration parameters may be determined sequentially by using k standard mass bodies and two sets of data of k capacitance values C1, C2, C3, … …, and Ck obtained in one-to-one correspondence with k standard mass bodies, and then taking the mean value of each calibration parameter as the final calibration parameter, for example, the calibration parameters determined according to (M1, C1) and (M2, C2) are (a1, b1), the calibration parameters determined according to (M3, C3) and (M4, C4) are (a2, b2), and so on, the calibration parameters determined according to (Mk-1, Ck-1) and (Mk, Ck) are (ak/2, bk/2), and then the final calibration parameters may be determined:
or (M1, C1) may be sequentially combined with (M2, C2), (M3, C3) … …, (Mk, Ck) to respectively obtain corresponding calibration parameters, then (M2, C2) may be sequentially combined with (M3, C3) … …, (Mk, Ck) to respectively obtain corresponding calibration parameters, and so on, and finally the mean value of all calibration parameters is determined as the final calibration parameter of the capacitive weight sensor, so that the weight calibration accuracy of the capacitive weight sensor can be improved.
And S250, establishing a calibration model of the capacitive weight sensor based on the calibration parameters.
For example, the foregoing embodiment only illustrates that the calibration parameters of the capacitive weight sensor are determined in a manner that is easy to understand, and any other feasible manner may be adopted to determine the calibration parameters of the capacitive weight sensor, which is not limited in this embodiment of the present invention.
Optionally, fig. 4 is a flowchart of a calibration method for a capacitive weight sensor according to an embodiment of the present invention, and as shown in fig. 4, the method includes:
and S310, acquiring capacitance values corresponding to the standard mass bodies with different masses one to one when the standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor.
And S320, performing linear fitting on the mass sum of each standard mass body and each capacitance value based on the initial linear fitting value, and determining a linear fitting result.
And S330, determining calibration parameters of the capacitive weight sensor according to the linear fitting result.
Specifically, the calibration parameters of the capacitive weight sensor may be determined by linear fitting, for example, a least square method or a polynomial fitting method may be used to fit (M1, C1), (M2, C2), (M3, C3) … …, (Mk, Ck), when the linear fitting algorithm is used to perform the fitting, initial values of a slope a and an intercept b (i.e., calibration parameters) in the linear relationship M ═ aC + b may be preset, then the fitted calibration parameters may be obtained according to the linear fitting algorithm, in addition to the values of the calibration parameters, the fitting degree corresponding to the current calibration parameters may be determined, and it is assumed that the fitted straight line determined according to the linear fitting algorithm is the fitting straight line determined as the value of the current calibration parameters
Degree of fitting
Wherein i is more than or equal to 1 and less than or equal to k, and i is an integer,
the mass mean of k standard masses shows that the fitting degree r2 is larger, and the fitting straight line is
The higher the fitting accuracy of (1), and thus is in factAfter the linear fitting result is determined, the calibration parameters of the capacitive weight sensor with higher precision can be determined according to the fitting degree.
Exemplarily, after the linear fitting result is determined, whether the fitting degree is greater than a preset fitting degree can be judged, and if the fitting degree is determined to be greater than the preset fitting degree, the fitting parameters are determined as the calibration parameters of the capacitive weight sensor; and if the fitting degree is not greater than the preset fitting degree, returning to the step S220 after the linear fitting initial value is adjusted, namely performing linear fitting on the mass sum and each capacitance value of each standard mass body based on the linear fitting initial value, determining a linear fitting result, stopping fitting until the fitting times reach the preset times, and taking the fitting result of the last time as a final calibration parameter of the capacitive weight sensor, so that a fitting straight line with higher precision can be obtained. The preset fitting degree and the preset times can be set according to design requirements, for example, the preset fitting degree can be set to be 0.8, and the preset times can be set to be 10.
And S340, establishing a calibration model of the capacitive weight sensor based on the calibration parameters.
Optionally, fig. 5 is a flowchart of a calibration method for a capacitive weight sensor according to an embodiment of the present invention, and as shown in fig. 5, the method includes:
s410, when standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor, N capacitance measurement values corresponding to the same standard mass body are obtained.
And S420, calculating the capacitance average value of the N capacitance measured values corresponding to the same standard mass body.
S430, determine the average value of the capacitances corresponding to the proof masses as the capacitance corresponding to the proof masses.
Specifically, in order to improve the calibration accuracy of the capacitive weight sensor, the standard mass body with the same mass may be repeatedly placed on the capacitive weight sensor, that is, the standard mass body with the same mass is used to repeatedly apply pressure to the capacitive weight sensor, and a capacitance measurement value is obtained when pressure is applied each time, and the number of times of repetition may be set according to design requirements, for example, 30 times; for example, the standard mass body with the mass of M1 may be placed on the capacitive weight sensor 30 times, an average value of capacitance measurement values obtained 30 times is used as a capacitance value corresponding to the standard mass body with the mass of M1, and so on until the capacitance value corresponding to the standard mass body with the mass of Mk is determined, so that the capacitance value corresponding to each standard mass body may be determined, and the calibration accuracy of the capacitive weight sensor is further improved.
And S440, determining calibration parameters of the capacitive weight sensor according to the mass and the capacitance of each standard mass body.
S450, establishing a calibration model of the capacitive weight sensor based on the calibration parameters.
And S460, determining the capacitance standard deviation corresponding to the standard mass body according to the capacitance measured value and the capacitance average value corresponding to the same standard mass body.
And S470, correcting the calibration model according to the capacitance standard deviation corresponding to each standard mass body.
Specifically, in the actual operation process of the calibration process, when the standard mass body of the same mass is placed on the capacitive weight sensor, the position, the speed, the angle and the like of the standard mass body of the same mass are different, so that the thickness change of the elastic medium layer is also different, for example, when the same mass body is placed on the capacitive weight sensor, the thickness of the elastic medium layer is rapidly reduced and then recovered a little due to overlarge acceleration during placement, although the thickness of the elastic medium layer is still recovered to the thickness corresponding to the mass of the standard mass body after a long time, it is difficult to ensure that the obtained capacitance measurement value is the capacitance value corresponding to the complete recovery of the capacitance measurement value, so that an error is easily caused, and in order to eliminate the error, the calibration model of the weight sensor can be revised to have a certain fault-tolerant range, so that when the weight is determined according to the capacitance value in actual use, for example, when the capacitance value is applied to an intelligent pad, if the human body sits or lies on the intelligent cushion for multiple times within a certain time period, the weight determined according to the capacitance value obtained each time is within the error range, and the weight of the human body can be determined to be unchanged.
Illustratively, each proof mass may be calculatedThe capacitance standard deviation σ i (i is greater than or equal to 1 and less than or equal to k, and i is an integer) repeatedly placed on the capacitance weight sensor, and assuming that n times of capacitance measurement values are repeatedly obtained for each standard mass body, the capacitance standard deviation corresponding to the ith standard mass body is as follows:
wherein, σ i is the capacitance standard deviation corresponding to the standard mass body with the mass Mi, Ci, j is the capacitance measured value of the jth time of the standard mass body with the mass Mi,
the mass is the average value of the capacitance corresponding to the standard mass body of Mi, and then the average value of the capacitance standard deviation of k standard mass bodies can be used
Determined as the standard deviation σ from the calibration model, i.e.
If the calibration model of the capacitive weight sensor is determined to be M ═ aC + b, the calibration model of the capacitive weight sensor may be modified as follows: m ═ a (C ± σ) + b. In this way, when the weight is determined according to the capacitance value in actual use, for example, when the weight is applied to the intelligent cushion, if the weight determined according to the capacitance value obtained each time is within the error range when the human body sits or lies on the intelligent cushion for a plurality of times within a certain period of time (for example, a week), the weight of the human body can be determined to be unchanged.
Based on the same inventive concept, the embodiment of the present invention further provides a calibration apparatus for a capacitive weight sensor, the calibration apparatus for a capacitive weight sensor can calibrate the weight of the capacitive weight sensor accurately, the calibration apparatus for a capacitive weight sensor can be used to execute the embodiments of the present invention, and a calibration method for a capacitive weight sensor, the calibration apparatus for a capacitive weight sensor can be executed by software and/or hardware, and the calibration apparatus for a capacitive weight sensor can be integrated into the smart mat provided by the embodiments of the present invention. Referring to fig. 1, the capacitive weight sensor 00 includes a first plate 10, a second plate 20, and a dielectric layer 30 located between the first plate 10 and the second plate 20, wherein the first plate 10 and the second plate 20 form a capacitor; among them, the dielectric layer 30 is preferably a flexible dielectric layer having elasticity.
Fig. 6 is a schematic mechanism diagram of a calibration apparatus for a capacitive weight sensor according to an embodiment of the present invention, and as shown in fig. 6, the calibration apparatus for a capacitive weight sensor includes: a capacitance value obtaining module 100, configured to obtain capacitance values corresponding to the standard mass bodies of different masses one to one when the standard mass bodies of different masses respectively apply pressure to the capacitive weight sensor; a calibration parameter determining module 200, configured to determine a calibration parameter of the capacitive weight sensor according to the mass and each capacitance value of each standard mass body; and a calibration model establishing module 300, configured to establish a calibration model of the capacitive weight sensor based on the calibration parameters.
According to the calibration device for the capacitive weight sensor, provided by the embodiment of the invention, when the standard mass bodies with different masses apply pressure to the capacitive weight sensor, the capacitance values corresponding to the standard mass bodies with different masses one by one are respectively acquired through the capacitance value acquisition module, so that the calibration parameter of the capacitive weight sensor which is more accurate can be determined according to multiple groups of masses and corresponding capacitance values through the calibration parameter determination module, and the calibration model establishment module can establish the calibration model of the capacitive weight sensor which is more accurate according to the calibration parameter, so that calibration of the capacitive weight sensor which is simpler and higher in accuracy is realized, and the weight of an object or a human body can be more accurately determined according to the capacitance value output by the current capacitive weight sensor in practical application.
Optionally, fig. 7 is a schematic mechanism diagram of another calibration apparatus for a capacitive weight sensor according to an embodiment of the present invention, and as shown in fig. 7, the capacitance value obtaining module includes a capacitance measurement value obtaining unit 110, configured to obtain N capacitance measurement values corresponding to a same standard mass body; a capacitance average value calculating unit 120, configured to calculate a capacitance average value of N capacitance measurement values corresponding to the same standard mass body; a capacitance value determining unit 130, configured to determine an average value of the capacitances corresponding to the standard mass body as a capacitance value corresponding to the standard mass body.
Optionally, referring to fig. 7, the calibration apparatus for a capacitive weight sensor further includes a capacitance standard deviation determining module 400, configured to determine, according to the capacitance measured value and the capacitance average value corresponding to the same standard mass body, a capacitance standard deviation corresponding to the standard mass body; and the calibration model correction module 500 is used for correcting the calibration model according to the capacitance standard deviation corresponding to each standard mass body.
Optionally, fig. 8 is a schematic mechanical diagram of a calibration apparatus for a capacitive weight sensor according to an embodiment of the present invention, and as shown in fig. 8, the calibration parameter determining module 200 includes: a slope value and intercept value determination unit 210, configured to determine a plurality of slope values and a plurality of intercept values based on a linear relation according to the mass sum and each capacitance value of each standard mass body; an average determining unit 220, configured to average the plurality of slope values to determine a slope average, and average the plurality of intercept values to determine an intercept average; a first calibration parameter determining unit 230 for determining the slope average value and the intercept average value as calibration parameters of the capacitive weight sensor.
Optionally, with continued reference to fig. 8, the calibration parameter determining module 200 further includes: a linear fitting result determining unit 240, configured to perform linear fitting on the mass sum of each standard mass body and each capacitance value based on the initial value of linear fitting, and determine a linear fitting result; and the second calibration parameter determining unit 250 is used for determining the calibration parameters of the capacitive weight sensor according to the linear fitting result.
Optionally, with reference to fig. 8, the linear fitting result includes a fitting parameter and a fitting degree, and the second calibration parameter determining unit 250 includes a determining subunit 251, configured to determine whether the fitting degree is greater than a preset fitting degree; the calibration parameter determining subunit 252 is configured to determine the fitting parameter as a calibration parameter of the capacitive weight sensor when the determining subunit determines that the degree of fitting is greater than the preset degree of fitting; and when the judging subunit determines that the fitting degree is not greater than the preset fitting degree, adjusting the initial linear fitting value, returning to the step of performing linear fitting on the mass sum and the capacitance value of each standard mass body based on the initial linear fitting value, determining a linear fitting result until the fitting times reach the preset times, and determining the fitting parameters of the last fitting as the calibration parameters of the capacitive weight sensor.
FIG. 9 shows a schematic block diagram of an electronic device 01 that may be used to implement embodiments of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 9, the electronic device 01 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 01 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to the bus 14.
A plurality of components in the electronic device 01 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 01 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above.
In some embodiments, method XXX may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed on the electronic device 01 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the calibration method of the capacitive weight sensor described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the calibration method of the capacitive weight sensor by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
Based on the same inventive concept, the embodiment of the present invention further provides an intelligent mat, including: at least one capacitive weight sensor; referring to fig. 1, the capacitive weight sensor 00 includes a first plate 10, a second plate 20, and a dielectric layer 30 located between the first plate 10 and the second plate 20, the first plate 10 and the second plate 20 forming a capacitor; the capacitive weight sensor 00 performs weight calibration by using the calibration method of the capacitive weight sensor provided in any embodiment of the present invention, so that the technical characteristics of the calibration method of the capacitive weight sensor provided in any embodiment of the present invention are provided, the technical effect of the calibration method of the capacitive weight sensor provided in any embodiment of the present invention can be achieved, and the same points can be referred to the description of the calibration method of the capacitive weight sensor, and are not described herein again. Wherein the smart mat may be applied in a chair, a sofa or a mattress.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A calibration method of a capacitive weight sensor is used for carrying out weight calibration on the capacitive weight sensor, and is characterized in that the capacitive weight sensor comprises a first polar plate, a second polar plate and a dielectric layer positioned between the first polar plate and the second polar plate, and a capacitor formed by the first polar plate and the second polar plate, and the calibration method of the capacitive weight sensor comprises the following steps:
when standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor, capacitance values corresponding to the standard mass bodies with different masses one to one are obtained;
determining calibration parameters of the capacitive weight sensor according to the mass of each standard mass body and each capacitance value;
and establishing a calibration model of the capacitive weight sensor based on the calibration parameters.
2. The method for calibrating a capacitive weight sensor according to claim 1, wherein obtaining capacitance values corresponding to different masses of standard mass one-to-one comprises:
acquiring N capacitance measurement values corresponding to the same standard mass body;
calculating the capacitance average value of N capacitance measurement values corresponding to the same standard mass body;
and determining the capacitance average value corresponding to the standard mass body as the capacitance value corresponding to the standard mass body.
3. The method for calibrating a capacitive weight sensor according to claim 2, further comprising, after establishing a calibration model of the capacitive weight sensor based on the calibration parameters:
determining a capacitance standard deviation corresponding to the standard mass body according to the capacitance measured value and the capacitance average value corresponding to the same standard mass body;
and correcting the calibration model according to the capacitance standard deviation corresponding to each standard mass body.
4. The calibration method of the capacitive weight sensor according to claim 1, wherein determining calibration parameters of the capacitive weight sensor according to the mass sum of each standard mass body and each capacitance value comprises:
determining a plurality of slope values and a plurality of intercept values based on a linear relation according to the mass sum of each standard mass body and each capacitance value;
averaging the plurality of slope values to determine a slope average value, and averaging the plurality of intercept values to determine an intercept average value;
and determining the slope average value and the intercept average value as calibration parameters of the capacitive weight sensor.
5. The calibration method of the capacitive weight sensor according to claim 1, wherein determining calibration parameters of the capacitive weight sensor according to the mass sum of each standard mass body and each capacitance value comprises:
performing linear fitting on the mass sum of each standard mass body and each capacitance value based on the initial linear fitting value, and determining a linear fitting result;
and determining the calibration parameters of the capacitive weight sensor according to the linear fitting result.
6. The method for calibrating a capacitive weight sensor according to claim 5, wherein the linear fitting result comprises fitting parameters and a degree of fitting;
according to the linear fitting result, determining calibration parameters of the capacitive weight sensor, including:
judging whether the fitting degree is greater than a preset fitting degree;
if so, determining the fitting parameters as calibration parameters of the capacitive weight sensor;
and if not, after the linear fitting initial value is adjusted, returning to the step of performing linear fitting on the mass sum of each standard mass body and each capacitance value based on the linear fitting initial value, determining a linear fitting result until the fitting times reach the preset times, and determining the fitting parameters of the last fitting as the calibration parameters of the capacitive weight sensor.
7. The calibration device of the capacitive weight sensor is used for calibrating the weight of the capacitive weight sensor, and is characterized in that the capacitive weight sensor comprises a first polar plate, a second polar plate and a dielectric layer positioned between the first polar plate and the second polar plate, the first polar plate and the second polar plate form a capacitor, and the calibration device of the capacitive weight sensor comprises:
the capacitance value acquisition module is used for acquiring capacitance values corresponding to the standard mass bodies with different masses one to one when the standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor;
the calibration parameter determining module is used for determining calibration parameters of the capacitive weight sensor according to the mass of each standard mass body and each capacitance value;
and the calibration model establishing module is used for establishing a calibration model of the capacitive weight sensor based on the calibration parameters.
8. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a method of calibrating a capacitive weight sensor as claimed in any one of claims 1 to 6.
9. A computer readable storage medium, wherein the computer readable storage medium stores computer instructions for causing a processor to perform a calibration method of a capacitive weight sensor according to any one of claims 1 to 6.
10. A smart mat, comprising: at least one capacitive weight sensor;
the capacitive weight sensor comprises a first polar plate, a second polar plate and a dielectric layer positioned between the first polar plate and the second polar plate, wherein the first polar plate and the second polar plate form a capacitor;
the capacitive weight sensor is subjected to weight calibration by the method for calibrating the capacitive weight sensor according to any one of claims 1 to 6.
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