CN114910150B - 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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- CN114910150B CN114910150B CN202210420456.8A CN202210420456A CN114910150B CN 114910150 B CN114910150 B CN 114910150B CN 202210420456 A CN202210420456 A CN 202210420456A CN 114910150 B CN114910150 B CN 114910150B
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- weight sensor
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G23/00—Auxiliary devices for weighing apparatus
- G01G23/01—Testing or calibrating of weighing apparatus
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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
- A47C—CHAIRS; SOFAS; BEDS
- A47C27/00—Spring, stuffed or fluid mattresses or cushions specially adapted for chairs, beds or sofas
Abstract
The invention discloses a calibration method, a device, an intelligent pad and a storage medium of a capacitive weight sensor, 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, the first polar plate and the second polar plate form a capacitor, and the calibration method of the capacitive weight sensor comprises the following steps: when standard mass bodies with different masses apply pressure to the capacitive weight sensor respectively, capacitance values corresponding to the standard mass bodies with different masses one by one are obtained; determining calibration parameters of the capacitive weight sensor according to the mass of each standard mass body and each capacitance value; based on the calibration parameters, a calibration model of the capacitive weight sensor is established, and a more accurate calibration model of the capacitive weight sensor can be established according to the calibration parameters, so that a simple and highly accurate calibration method of the capacitive weight sensor 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 comprising sensors are nowadays favored and used in various application scenarios, for example sensor mats applied in car seats, which are capable of automatically starting a vehicle when a person is sensed sitting down; or in smart homes such as sofas and mattresses.
The detection function of the intelligent pad is single today, and whether the weight is placed on the intelligent pad or whether the human body sits down can only be judged, namely 0/1 signal is intelligently output, the weight of an object or a human body on the intelligent pad can not be measured, and the current diversified requirements for intelligent home cannot be met.
Disclosure of Invention
The invention provides a calibration method and device for a capacitive weight sensor, an intelligent pad and a storage medium, which can accurately calibrate the capacitive weight sensor.
According to an aspect of the present invention, there is provided a calibration method of a capacitive weight sensor, for calibrating a weight of 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, and the calibration method of 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 by 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 mass bodies of different masses one to one includes:
acquiring N capacitance measurement values corresponding to the same standard mass body;
calculating capacitance average values of N capacitance measurement values corresponding to the same standard mass body;
and determining the average value of the capacitance corresponding to the standard mass body as the capacitance 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 the calibration parameter of the capacitive weight sensor according to the mass sum 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 relationship based on the mass sum of each of the standard masses and each of the capacitance values;
determining a slope average value by averaging the plurality of slope values and an intercept average value by averaging the plurality of intercept values;
and determining the slope average value and the intercept average value as calibration parameters of the capacitive weight sensor.
Optionally, determining the calibration parameter of the capacitive weight sensor according to the mass sum of each standard mass body and each capacitance value includes:
based on the initial value of linear fitting, carrying out linear fitting on the mass sum of each standard mass body and each capacitance value, and determining 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 fitting parameters and fitting degrees;
determining calibration parameters of the capacitive weight sensor according to the linear fitting result, wherein the calibration parameters comprise:
judging whether the fitting degree is larger than a preset fitting degree or not;
If yes, determining the fitting parameter as a calibration parameter of the capacitive weight sensor;
if not, after the initial value of the linear fitting is adjusted, the step of carrying out linear fitting on the mass sum of each standard mass body and each capacitance value based on the initial value of the linear fitting is carried out, and a linear fitting result is determined until the fitting times reach the preset times, and the fitting parameter of the last fitting is determined as the calibration parameter of the capacitive weight sensor.
According to another aspect of the present invention, there is provided a calibration device for a capacitive weight sensor, for weight calibration of 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, and the calibration device for the capacitive weight sensor includes:
the capacitance value acquisition module is used for acquiring capacitance values corresponding to standard mass bodies with different masses one by 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 the calibration parameters of the capacitive weight sensor according to the mass of each standard mass body and each capacitance value;
and the calibration model establishment 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 liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of calibrating a capacitive weight sensor described above.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the calibration method of the capacitive weight sensor described above.
According to another aspect of the present invention, there is provided a smart pad 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 performs weight calibration by adopting the calibration method of the capacitive weight sensor.
According to the calibration method of the capacitive weight sensor, the standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor, and meanwhile, capacitance values corresponding to the standard mass bodies with different masses one by one are respectively obtained, so that the calibration parameters of the capacitive weight sensor can be determined accurately according to the plurality of groups of masses and the corresponding capacitance values, a calibration model of the capacitive weight sensor can be established accurately according to the calibration parameters, the calibration method of the capacitive weight sensor which is simple and high in accuracy is achieved, and in practical application, the weight of an object or a human body can be determined accurately according to the capacitance values output by the current capacitive weight sensor.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic 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 calibration method for a capacitive weight sensor according to an embodiment of the present invention;
FIG. 4 is a flow chart of a calibration method of a capacitive weight sensor according to an embodiment of the present invention;
FIG. 5 is a flow chart of a calibration method of a capacitive weight sensor according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a calibration device of a capacitive weight sensor according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a calibration device of another capacitive weight sensor according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a calibration device for a capacitive weight sensor according to 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 that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise 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 calibrating the weight of the capacitive weight sensor, and can be executed by the calibration device of the capacitive weight sensor, which can be executed by software and/or hardware, and can be integrated into an intelligent pad provided by the embodiment of the invention.
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 electrode plate 10, a second electrode plate 20, and a dielectric layer 30 between the first electrode plate 10 and the second electrode plate 20, where the first electrode plate 10 and the second electrode plate 20 form a capacitor. The dielectric layer 30 is preferably a flexible dielectric layer with elasticity, when a weight 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 plate distance of the capacitor is reduced, as shown in the decision formula c=epsilon S/4 pi kd of the capacitor, when the plate distance of the capacitor is reduced, the capacitance value is increased, and the weight of the weight is heavier, the plate distance of the capacitor is smaller, according to which the weight of the weight can be determined according to the acquired capacitance value between two plates of the capacitor, so that the weight of the weight determined according to the capacitance value is more accurate.
Fig. 2 is a flowchart of a calibration method of a capacitive weight sensor according to an embodiment of the present invention, as shown in fig. 2, the method includes:
and S110, when the standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor, acquiring capacitance values corresponding to the standard mass bodies with different masses one by one.
Specifically, the standard mass body is an object with higher mass (unit: kg) precision, the standard mass bodies with different masses can be sequentially and respectively placed on one plate surface of the capacitive weight sensor, for example, the standard mass body with the mass of M1, M2, M3, … … and Mk (k is a natural number) can be placed on one plate surface of the capacitive weight sensor, so as 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, a capacitance value C1 corresponding to the standard mass body is obtained, then the standard mass body with the mass of M1 is taken off from the plate of the capacitive weight sensor, and a dielectric layer between the two plates of the capacitor is recovered and deformed at intervals. For example, a standard mass body with a mass of M2 may be placed on a surface of one plate of the capacitive weight sensor, and a capacitance value C2 corresponding to the standard mass body with a mass of M2 is obtained when the placement is stable, and so on until k capacitance values C1, C2, C3, … …, ck corresponding to k standard mass bodies with masses of M1, M2, M3, … …, mk one by one are obtained.
S120, determining calibration parameters of the capacitive weight sensor according to the mass of each standard mass body and each capacitance value.
Specifically, referring to fig. 1, since the thickness of the medium 30 is reduced when a standard mass body applies pressure to the capacitive weight sensor 00, 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 in inverse proportion to the plate distance d of the capacitor, according to the decision formula c=εs/4Σ kd of the capacitor (where C is a capacitance value, ε is a dielectric constant of the medium between the plates, S is a facing area of the two plates of the capacitor, k is an electrostatic force constant, d is an inter-plate distance), it is known that the capacitance value C is in inverse proportion to the plate distance d, that is, that when the plate distance d of the capacitor is reduced, the capacitance value is increased, it can be determined that the pressure borne by the capacitive weight sensor is in direct proportion to the capacitance value C, that the mass M of the standard mass body is in direct proportion to the capacitance value is the capacitance value C, that the larger the pressure borne by the capacitive weight sensor is the larger the pressure borne by the plate distance is the larger is the plate distance is the larger; it can be assumed that the relation between the mass M of the standard mass and the capacitance C is: m=ac+b, where a, c are calibration parameters, and a is the slope and b is the intercept. Thus, the capacitance value may be determined from the plurality of sets of measured capacitance values and the mass of the corresponding proof mass.
S130, based on calibration parameters, a calibration model of the capacitive weight sensor is established.
Specifically, after the values of the standard parameters a and b are determined, m=ac+b can be used as a calibration model of the capacitive weight sensor, and in practical application, the pressure born by the current capacitive weight sensor can be determined according to the obtained capacitance value, so that the weight of an object or a human body on the current capacitive weight sensor can be determined.
According to the calibration method of the capacitive weight sensor, the standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor, and meanwhile, capacitance values corresponding to the standard mass bodies with different masses one by one are respectively obtained, so that the calibration parameters of the capacitive weight sensor can be determined accurately according to the plurality of groups of masses and the corresponding capacitance values, a calibration model of the capacitive weight sensor can be established accurately according to the calibration parameters, the calibration method of the capacitive weight sensor which is simple and high in accuracy is achieved, and in practical application, the weight of an object or a human body can be determined accurately according to the capacitance values output by the current capacitive weight sensor.
Optionally, fig. 3 is a flowchart of another calibration method of a capacitive weight sensor according to an embodiment of the present invention, as shown in fig. 3, where the method includes:
and S210, when the standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor, acquiring capacitance values corresponding to the standard mass bodies with different masses one by one.
S220, 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.
S230, averaging a plurality of slope values to determine a slope average value, and averaging a plurality of intercept values to determine an intercept average value.
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 of the acquired standard mass body and each capacitance value; for example, a set of calibration parameters may be determined by sequentially using k standard mass bodies of mass and two sets of acquired k capacitance values C1, C2, C3, … …, ck corresponding to each of the k standard mass bodies, and taking the average value of each calibration parameter as the final calibration parameter, for example, the calibration parameter determined according to (M1, C1) and (M2, C2) is (a 1, b 1), the calibration parameter determined according to (M3, C3) and (M4, C4) is (a 2, b 2), and so on, and the calibration parameter determined according to (Mk-1, ck-1) and (Mk, ck is (ak/2, bk/2), and then the final calibration parameter may be determined as: Or (M1, C1) may be sequentially combined with (M2, C2), (M3, C3) … …, (Mk, ck) to obtain corresponding calibration parameters, then (M2, C2) is sequentially combined with (M3, C3) … …, (Mk, ck) to obtain corresponding calibration parameters, and so on, and finally the average value of all the calibration parameters is determined to be the final calibration parameter of the capacitive weight sensor, so that the weight calibration precision of the capacitive weight sensor can be improved.
S250, based on calibration parameters, a calibration model of the capacitive weight sensor is established.
The above embodiments are merely exemplary, and the calibration parameters of the capacitive weight sensor may be determined in a relatively easy to understand manner, and the embodiments of the present invention are not limited thereto in any other possible manner.
Optionally, fig. 4 is a flowchart of a calibration method of a capacitive weight sensor according to an embodiment of the present invention, as shown in fig. 4, where the method includes:
and S310, when the 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 by one are obtained.
S320, based on the linear fitting initial value, carrying out linear fitting on the mass sum of each standard mass body and each capacitance value, and determining a linear fitting result.
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 a linear fitting method, for example, the (M1, C1), (M2, C2), (M3, C3) … …, (Mk, ck) may be fitted by a least square method or a polynomial fitting method, when the fitting is performed by a linear fitting algorithm, initial values of the slope a and the intercept b (i.e., calibration parameters) in the linear relationship m=ac+b may be preset first, 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 the fitting straight line determined according to the linear fitting algorithm is assumed to beFitting degree->Wherein i is 1.ltoreq.i.ltoreq.k and i is an integer, & lt/EN & gt>For the mass mean of k standard masses, the larger the fitting degree r2, the fitting straight line is +.>Therefore, after the linear fitting result is determined, the calibration parameters of the capacitive weight sensor with higher accuracy can be determined according to the fitting degree.
For example, after determining the linear fitting result, it may be determined whether the fitting degree is greater than a preset fitting degree, and if the fitting degree is determined to be greater than the preset fitting degree, the fitting parameter is determined to be a calibration parameter of the capacitive weight sensor; if the fitting degree is not greater than the preset fitting degree, the method returns to execute S220 after the linear fitting initial value is adjusted, namely, based on the linear fitting initial value, the mass sum of each standard mass body and each capacitance value are linearly fitted, and a linear fitting result is determined, until the fitting times reach the preset times, the fitting is stopped, and the last fitting result is used as the calibration parameter of the final 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 times.
S340, based on the calibration parameters, establishing a calibration model of the capacitive weight sensor.
Optionally, fig. 5 is a flowchart of a calibration method of a capacitive weight sensor according to an embodiment of the present invention, as shown in fig. 5, where the method includes:
and 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.
S420, calculating capacitance average values of N capacitance measurement values corresponding to the same standard mass body.
And S430, determining the average value of the capacitance corresponding to the standard mass body as the capacitance corresponding to the standard mass body.
Specifically, in order to improve the calibration accuracy of the capacitive weight sensor, the standard mass body with the same mass can be repeatedly placed on the capacitive weight sensor, namely, the standard mass body with the same mass is adopted to repeatedly apply pressure to the capacitive weight sensor, and capacitance measurement values when the pressure is applied each time are obtained, and the repetition times can be set by itself according to design requirements, for example, 30 times; for example, the standard mass weight with the mass of M1 may be placed on the capacitive weight sensor for 30 times, the average value of the capacitance measurement values obtained for 30 times is taken as the capacitance value corresponding to the standard mass with the mass of M1, and so on until the capacitance value corresponding to the standard mass with the mass of Mk is determined, the capacitance value corresponding to each standard mass may be determined, and the calibration accuracy of the capacitive weight sensor is further improved.
S440, determining calibration parameters of the capacitive weight sensor according to the mass of each standard mass body and each capacitance value.
S450, based on the calibration parameters, a calibration model of the capacitive weight sensor is established.
S460, determining the standard deviation of the capacitance corresponding to the standard mass body according to the capacitance measured value and the capacitance average value corresponding to the same standard mass body.
S470, correcting the calibration model according to the capacitance standard deviation corresponding to each standard mass body.
Specifically, because the positions, the speeds, the angles and the like of the standard mass bodies with the same mass are different in the actual operation process of the calibration process, when the same mass body is placed on the capacitive weight sensor, the thickness variation of the elastic medium layer in the standard mass body can also generate the difference, for example, the thickness of the elastic medium layer can be quickly reduced and then restored to be a little due to overlarge acceleration during placement, although the thickness of the elastic medium layer can still be restored to the thickness corresponding to the mass of the standard mass body after a long time, the acquired capacitance measurement value is difficult to ensure to be the capacitance value corresponding to the complete restoration, so that errors are easily caused, the calibration model of the weight sensor can be revised to have a certain fault tolerance range for eliminating the errors, and thus, when the weight is determined according to the capacitance value in actual use, for example, when the weight is applied to an intelligent pad, if the weight determined according to the capacitance value acquired each time is within the error range, the weight of the human body is not changed.
For example, the standard deviation σi of the capacitance repeatedly placed on the capacitive weight sensor for each standard mass (i is 1+.ltoreq.k and i is an integer) may be calculated, and assuming that n capacitance measurements are repeatedly obtained for each standard mass, the standard deviation of the capacitance corresponding to the i-th standard mass is:wherein σi is the capacitance standard deviation corresponding to the standard mass of Mi, ci, j is the capacitance measurement value for the jth time of the standard mass of Mi, < >>For the capacitance average value corresponding to the standard mass body with the mass Mi, the standard deviation sigma of the calibration model, i.e. & lt/EN, & gt, can then be determined from the average value of the capacitance standard deviations of the k standard mass bodies>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 to be: m=a (c±σ) +b. Thus, when the weight is determined according to the capacitance value in actual use, for example, when the weight is applied to an intelligent pad, if the weight determined according to the capacitance value obtained each time is within an error range when a human body sits or lies on the intelligent pad for a plurality of times within a certain period of time (for example, one week), the weight of the human body can be determined to be unchanged.
Based on the same inventive concept, the embodiment of the invention also provides a calibration device of a capacitive weight sensor, which can calibrate and accurately calibrate the weight of the capacitive weight sensor, and the calibration device of the capacitive weight sensor can be used for executing the calibration method of the capacitive weight sensor, 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. Referring to fig. 1, the capacitive weight sensor 00 includes a first electrode plate 10, a second electrode plate 20, and a dielectric layer 30 between the first electrode plate 10 and the second electrode plate 20, wherein the first electrode plate 10 and the second electrode plate 20 form a capacitor; among them, the dielectric layer 30 is preferably a flexible dielectric layer having elasticity.
Fig. 6 is a schematic diagram of a mechanism of a calibration device of a capacitive weight sensor according to an embodiment of the present invention, as shown in fig. 6, the calibration device of the capacitive weight sensor includes: the capacitance value obtaining module 100 is configured to obtain capacitance values corresponding to standard mass bodies with different masses one by one when the standard mass bodies with different masses apply pressure to the capacitive weight sensor respectively; the calibration parameter determining module 200 is configured to determine a calibration parameter of the capacitive weight sensor according to the mass of each standard mass body and each capacitance value; the calibration model establishment module 300 is 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, when the standard mass bodies with different masses respectively apply pressure to the capacitive weight sensor, the capacitance value obtaining modules respectively obtain the capacitance values corresponding to the standard mass bodies with different masses one by one, so that the calibration parameters of the capacitive weight sensor can be accurately determined according to the plurality of groups of masses and the corresponding capacitance values through the calibration parameter determining modules, the calibration model building module can build a more accurate calibration model of the capacitive weight sensor according to the calibration parameters, the calibration of the capacitive weight sensor which is simpler and higher in accuracy is realized, and in practical application, the weight of an object or a human body can be accurately determined according to the capacitance values output by the current capacitive weight sensor.
Optionally, fig. 7 is a schematic diagram of a mechanism of another calibration device of a capacitive weight sensor according to an embodiment of the present invention, 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 the same standard mass body; a capacitance average value calculation unit 120 for calculating capacitance average values of N capacitance measurement values corresponding to the same standard mass body; the capacitance value determining unit 130 is configured to determine an average value of capacitances corresponding to the standard mass body as a capacitance value corresponding to the standard mass body.
Optionally, referring to fig. 7, the calibration device of the capacitive weight sensor further includes a capacitance standard deviation determining module 400, configured to determine a capacitance standard deviation corresponding to the standard mass according to a capacitance measurement value and a capacitance average value corresponding to the same standard mass; the calibration model correction module 500 is configured to correct the calibration model according to the capacitance standard deviation corresponding to each standard mass body.
Optionally, fig. 8 is a schematic diagram of a calibration device of another 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 determining unit 210 for determining a plurality of slope values and a plurality of intercept values based on a linear relation from the mass sum of each standard mass and each capacitance value; an average value determining unit 220 for determining an average value of the slope by averaging the plurality of slope values and an average value of the intercept by averaging the plurality of intercept values; the first calibration parameter determining unit 230 is configured to determine 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 determination 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 configured to determine a calibration parameter of the capacitive weight sensor according to the linear fitting result.
Optionally, with continued 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 judging subunit 251 configured to judge 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 fitting degree is greater than the preset fitting degree; when the judging subunit determines that the fitting degree is not greater than the preset fitting degree, after the linear fitting initial value is adjusted, the step of carrying out linear fitting on the mass sum of each standard mass body and each capacitance value based on the linear fitting initial value and determining a linear fitting result is carried out, until the fitting times reach the preset times, and the fitting parameter of the last fitting is determined as the calibration parameter of the capacitive weight sensor.
Fig. 9 shows a schematic diagram of the structure of an electronic device 01 that may be used to implement an embodiment 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. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, 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, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required 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 bus 14.
A number 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, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an 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 via 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, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. 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 onto 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 in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On 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, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out 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 implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the 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. The 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) through 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 may 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 input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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. The client and server are typically 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 hosts and VPS service are overcome.
Based on the same inventive concept, an embodiment of the present invention further provides an intelligent pad, including: at least one capacitive weight sensor; referring to fig. 1, the capacitive weight sensing 00 includes a first electrode plate 10, a second electrode plate 20, and a dielectric layer 30 between the first electrode plate 10 and the second electrode plate 20, the first electrode plate 10 and the second electrode plate 20 forming a capacitor; the capacitive weight sensor 00 performs weight calibration by adopting the calibration method of the capacitive weight sensor provided by any embodiment of the present invention, so that the technical characteristics of the calibration method of the capacitive weight sensor provided by any embodiment of the present invention can be achieved, and the technical effects of the calibration method of the capacitive weight sensor provided by any embodiment of the present invention can be achieved. Wherein, the intelligent pad can be applied to a seat, a sofa or a mattress.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (8)
1. The method for calibrating the capacitive weight sensor is characterized by comprising 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, and the method for calibrating 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 by one are obtained;
determining calibration parameters of the capacitive weight sensor according to the mass of each standard mass body and each capacitance value;
based on the calibration parameters, a calibration model of the capacitive weight sensor is established;
The method for acquiring the capacitance values corresponding to standard mass bodies with different masses one by one comprises the following steps:
acquiring N capacitance measurement values corresponding to the same standard mass body;
calculating capacitance average values of N capacitance measurement values corresponding to the same standard mass body;
determining the average value of the capacitance corresponding to the standard mass body as a capacitance value corresponding to the standard mass body;
after establishing the calibration model of the capacitive weight sensor based on the calibration parameters, the method further comprises the following steps:
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;
correcting the calibration model according to the capacitance standard deviation corresponding to each standard mass body;
calculating the capacitance standard deviation of each standard mass weight placed on the capacitive weight sensor
Sigma i is more than or equal to 1 and less than or equal to k, i is an integer, n times of capacitance measurement values are repeatedly obtained for each standard mass body, and the standard deviation of the capacitance corresponding to the ith standard mass body is as follows:wherein σi is the capacitance standard deviation corresponding to the standard mass of Mi, ci, j is the capacitance measurement value for the jth time of the standard mass of Mi, < > >For the capacitance average value corresponding to the standard mass body with the mass Mi, the standard deviation sigma of the calibration model is determined according to the average value of the capacitance standard deviations of the k standard mass bodies, namely +.>If the calibration model of the capacitive weight sensor is determined to be m=ac+b, the calibration model of the corrected capacitive weight sensor is: m=a (c±σ) +b;
wherein M is the mass of the standard mass body, the capacitance value is C, a is the slope, and b is the intercept.
2. The method of calibrating a capacitive weight sensor according to claim 1, wherein determining calibration parameters of the capacitive weight sensor based on the mass sum of the standard masses and the capacitance values comprises:
determining a plurality of slope values and a plurality of intercept values based on a linear relationship based on the mass sum of each of the standard masses and each of the capacitance values;
determining a slope average value by averaging the plurality of slope values and an intercept average value by averaging the plurality of intercept values;
and determining the slope average value and the intercept average value as calibration parameters of the capacitive weight sensor.
3. The method of calibrating a capacitive weight sensor according to claim 1, wherein determining calibration parameters of the capacitive weight sensor based on the mass sum of the standard masses and the capacitance values comprises:
Based on the initial value of linear fitting, carrying out linear fitting on the mass sum of each standard mass body and each capacitance value, and determining a linear fitting result;
and determining calibration parameters of the capacitive weight sensor according to the linear fitting result.
4. A method of calibrating a capacitive weight sensor according to claim 3, characterized in that the linear fitting result comprises fitting parameters and fitting degrees;
determining calibration parameters of the capacitive weight sensor according to the linear fitting result, wherein the calibration parameters comprise:
judging whether the fitting degree is larger than a preset fitting degree or not;
if yes, determining the fitting parameter as a calibration parameter of the capacitive weight sensor;
if not, after the initial value of the linear fitting is adjusted, the step of carrying out linear fitting on the mass sum of each standard mass body and each capacitance value based on the initial value of the linear fitting is carried out, and a linear fitting result is determined until the fitting times reach the preset times, and the fitting parameter of the last fitting is determined as the calibration parameter of the capacitive weight sensor.
5. A calibration device for a capacitive weight sensor, for performing weight calibration on the capacitive weight sensor, the calibration device for a capacitive weight sensor being used for performing the calibration method for a capacitive weight sensor provided in any one of claims 1 to 4, wherein the capacitive weight sensor comprises a first polar plate, a second polar plate, and a dielectric layer located 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 for a capacitive weight sensor comprises:
The capacitance value acquisition module is used for acquiring capacitance values corresponding to standard mass bodies with different masses one by 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 the calibration parameters of the capacitive weight sensor according to the mass of each standard mass body and each capacitance value;
the calibration model establishment module is used for establishing a calibration model of the capacitive weight sensor based on the calibration parameters;
the capacitance standard deviation determining module is used for 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 the calibration model correction module is used for correcting the calibration model according to the capacitance standard deviation corresponding to each standard mass body.
6. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the calibration method of the capacitive weight sensor of any one of claims 1-4.
7. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to execute the calibration method of the capacitive weight sensor according to any one of claims 1 to 4.
8. An intelligent 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;
wherein the capacitive weight sensor performs weight calibration by using the calibration method of the capacitive weight sensor according to any one of claims 1 to 4.
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