CN118094090B - Mobile identification method, mobile identification device, beauty instrument and storage medium - Google Patents
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Abstract
The invention relates to the technical field of mobile identification, and discloses a mobile identification method, a mobile identification device, a beauty instrument and a storage medium.
Description
Technical Field
The present invention relates to the field of mobile identification technologies, and in particular, to a mobile identification method, a mobile identification device, a cosmetic instrument, and a storage medium.
Background
Radio frequency beauty instruments are currently very popular beauty instruments and are popular with individual consumers. The high-frequency electromagnetic waves (namely, radio frequency, also called radio frequency current) output by the radio frequency beauty instrument can directly penetrate through the epidermis layer of the skin to reach the dermis layer, and heat the dermis layer through ohmic heating effect to stimulate the fiber cells to increase secretion and synthesis of protein, so that the effects of filling collagen, reducing wrinkles and recovering skin elasticity are achieved.
Because the radio frequency beauty instrument needs continuous movement in the use process, the skin can be prevented from being scalded due to overlong stay time at the same position of the skin, and in order to ensure the skin safety of a user, the user needs to be reminded of avoiding staying for overlong time at the same position of the skin in the use process of the radio frequency beauty instrument.
At present, a part of the radio frequency beauty instruments on the market adopt a mobile sensor (also called a displacement sensor) for mobile identification, and the problem is that the radio frequency beauty instruments can realize simple mobile identification and have a limited identification range; the other part adopts a single-axis or multi-axis acceleration sensor to carry out movement identification, and has a wider identification range, but the logic involved in the movement identification is complex, the operation amount is large, so that a long running time is required on a low-end microcontroller, and the normal function operation of the system is easy to be blocked.
Accordingly, improvements in the art are needed.
The above information is presented as background information only to aid in the understanding of the present disclosure and is not intended or admitted to be prior art relative to the present disclosure.
Disclosure of Invention
The invention provides a mobile identification method, a mobile identification device, a beauty instrument and a storage medium, which break through the limitation of the prior art, have wider identification range, simple logic and small operation amount.
In order to achieve the above object, the present invention provides the following technical solutions: in a first aspect, the present invention provides a mobile identification method, including: acquiring triaxial acceleration data, wherein the triaxial acceleration data comprise X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data; storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data into corresponding original data storage areas respectively, wherein the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data correspond to one original data storage area respectively; respectively carrying out average value calculation on the data in the three original data storage areas to obtain three average values; respectively storing the three average values into corresponding average value storage areas, wherein the three average values respectively correspond to one average value storage area; respectively carrying out difference calculation on the maximum value and the minimum value in the three average value storage areas to obtain three average value difference values; judging whether at least one of the three average value differences is larger than a first preset value; if yes, identifying that movement occurs; if not, identifying that no movement occurs.
Further, in the movement recognition method, before the step of recognizing that no movement occurs, the method further includes: judging whether at least two of the three average value differences are larger than a second preset value or not, wherein the second preset value is smaller than the first preset value; if yes, returning to the step of executing the identification movement; if not, the step of identifying that no movement occurs is executed downwards.
Further, in the mobile recognition method, the step of storing the X-axis acceleration data, the Y-axis acceleration data, and the Z-axis acceleration data in the corresponding raw data storage areas respectively specifically includes: advancing all data stored in the three original data storage areas by one bit respectively; and respectively storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data to the tail parts of the corresponding original data storage areas.
Further, in the mobile identification method, the step of calculating the average value of the data in the three original data storage areas to obtain three average values specifically includes: and respectively carrying out average value calculation on the rest data except the maximum value and the minimum value in the three original data storage areas to obtain three average values.
Further, in the mobile identification method, the step of storing the three average values in the corresponding average value storage areas respectively specifically includes: advancing all data stored in the three average value storage areas by one bit respectively; and respectively storing the three average values to the tail parts of the corresponding average value storage areas.
In a second aspect, the present invention provides a mobile identification device comprising: the data acquisition module is used for acquiring triaxial acceleration data, wherein the triaxial acceleration data comprise X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data; the data storage module is used for respectively storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data into corresponding original data storage areas, and the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data respectively correspond to one original data storage area; the average value calculation module is used for calculating the average value of the data in the three original data storage areas respectively to obtain three average values; the average value storage module is used for respectively storing the three average values into corresponding average value storage areas, and the three average values respectively correspond to one average value storage area; the difference calculation module is used for respectively carrying out difference calculation on the maximum value and the minimum value in the three average value storage areas to obtain three average value differences; the difference judging module is used for judging whether at least one of the three average value differences is larger than a first preset value; if yes, identifying that movement occurs; if not, identifying that no movement occurs.
Further, in the mobile recognition device, the difference value judging module is further configured to: before the step of identifying that no movement occurs, judging whether at least two of the three average value differences are larger than a second preset value, wherein the second preset value is smaller than the first preset value; if yes, returning to the step of executing the identification movement; if not, the step of identifying that no movement occurs is executed downwards.
Further, in the mobile identification device, the data storage module is specifically configured to: advancing all data stored in the three original data storage areas by one bit respectively; and respectively storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data to the tail parts of the corresponding original data storage areas.
Further, in the mobile recognition device, the mean value calculation module is specifically configured to: and respectively carrying out average value calculation on the rest data except the maximum value and the minimum value in the three original data storage areas to obtain three average values.
Further, in the mobile identification device, the mean value storage module is specifically configured to: advancing all data stored in the three average value storage areas by one bit respectively; and respectively storing the three average values to the tail parts of the corresponding average value storage areas.
In a third aspect, the present invention provides a cosmetic apparatus comprising a memory storing a computer program and a processor implementing the movement identification method according to any one of the preceding aspects when the computer program is executed by the processor.
In a fourth aspect, the present invention provides a storage medium containing computer executable instructions for execution by a computer processor to implement the method of movement identification as described in any of the above aspects.
Compared with the prior art, the invention has the following beneficial effects:
According to the mobile identification method, the mobile identification device, the beauty instrument and the storage medium, the mobile identification is performed by acquiring and utilizing the triaxial acceleration data, and only the triaxial acceleration data is compared and sorted and is subjected to simple operation, so that the logic is simple, the operation amount is small, the requirement on a processor is low, the mobile identification range is wide, and the mobile identification method has a high application prospect in the field of mobile identification.
The invention has other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, taken in conjunction with the accompanying drawings and the detailed description, which illustrate certain principles of the invention.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a mobile identification method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of three primary data storage areas provided in accordance with a first embodiment of the present invention;
FIG. 3 is a schematic diagram of three average value storage areas according to a first embodiment of the present invention;
Fig. 4 is a flow chart of a mobile identification method according to a second embodiment of the present invention;
Fig. 5 is a flow chart of a mobile identification method according to a third embodiment of the present invention;
fig. 6 is a flow chart of a mobile identification method according to a fourth embodiment of the present invention;
fig. 7 is a flow chart of a mobile identification method according to a fifth embodiment of the present invention;
Fig. 8 is a schematic functional block diagram of a mobile recognition device according to a sixth embodiment of the present invention;
Fig. 9 is a schematic structural view of a cosmetic apparatus according to a seventh embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application. In addition, as one of ordinary skill in the art can know, with technical development and new scenarios, the technical solution provided by the embodiment of the present application is also applicable to similar technical problems.
In the description of the present application, it is to be understood that all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs unless defined otherwise. Furthermore, any terminology used is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
In addition, numerous specific details are set forth in the following description in order to provide a better illustration of the application. It will be understood by those skilled in the art that the present application may be practiced without some of these specific details. In some instances, well known methods, procedures, components, and circuits have not been described in detail so as not to obscure the present application.
The technical scheme of the invention is further described below by the specific embodiments with reference to the accompanying drawings.
Example 1
Referring to fig. 1, a flow chart of a mobile identification method according to an embodiment of the invention is provided, the method is suitable for a user to use a beauty treatment instrument to perform the method, and the method is implemented by a mobile identification device, which can be implemented by software and/or hardware and integrated in the beauty treatment instrument. The method specifically comprises the following steps: s101, acquiring triaxial acceleration data, wherein the triaxial acceleration data comprise X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data.
In this embodiment, a triaxial acceleration sensor with low cost is specifically used to collect triaxial acceleration data of the beauty instrument at the current moment. In practical application, more axial acceleration sensors, such as six-axis or nine-axis acceleration sensors, can be adopted to collect related data according to practical needs, and only three-axis acceleration data in the acceleration sensors are used.
It can be understood that, since the initial purpose of the movement recognition is to prevent the skin from being scalded due to the fact that the beauty instrument heats the same position of the skin for a long time, the time required for the movement recognition must be the period when the beauty instrument is started and used on the skin, that is, after the beauty instrument is started and contacts the skin, the recognition of whether the beauty instrument moves is required, that is, the step of acquiring the triaxial acceleration data is required. If the beauty instrument is only started and is not contacted with skin, the triaxial acceleration data do not need to be acquired, so that the waste of calculation resources is avoided.
For the judgment of whether the beauty instrument contacts the skin, a mode of outputting a skin detection waveform can be generally adopted, and in view of the fact that the content is realized in the prior art, the method is not an important point of the design of the scheme, and is not further described herein.
S102, storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data into corresponding original data storage areas respectively, wherein the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data correspond to one original data storage area respectively.
It should be noted that, the three raw data storage areas, such as the a storage area, the B storage area, and the C storage area shown in fig. 2, are respectively applied to store acceleration data in three axial directions, that is, the X-axis acceleration data, the Y-axis acceleration data, and the Z-axis acceleration data. X1-Xn stored in an A storage area of FIG. 2 are n sequentially acquired X-axis acceleration data, Y1-Yn stored in a B storage area are n sequentially acquired Y-axis acceleration data, and Z1-Zn stored in a C storage area are n sequentially acquired Z-axis acceleration data.
And S103, respectively carrying out average value calculation on the data in the three original data storage areas to obtain three average values.
It should be noted that, since the acceleration data obtained each time are stored in the raw data storage areas, each raw data storage area stores a plurality of acceleration data corresponding to the axial direction, the step is to calculate the average value of a plurality of acceleration data (including the newly obtained acceleration data) stored in the same raw data storage area.
And S104, respectively storing the three average values into corresponding average value storage areas, wherein the three average values respectively correspond to one average value storage area.
It should be noted that, the average value storage areas include three total storage areas, such as an a ' storage area, a B ' storage area, and a C ' storage area as shown in fig. 3, which are respectively applied to the average value of data stored in the three original data storage areas (i.e., the a storage area, the B storage area, and the C storage area). X1' -Xm ' stored in the A ' storage area of FIG. 3 is the average value of data in the A storage area calculated in m times, Y1' -Ym ' stored in the B ' storage area is the average value of data in the B storage area calculated in m times, and Z1' -Zm ' stored in the C ' storage area is the average value of data in the C storage area calculated in m times.
And S105, respectively carrying out difference calculation on the maximum value and the minimum value in the three average value storage areas to obtain three average value difference values.
It should be noted that, since the average value obtained by each calculation is stored in the average value storage area, each average value storage area stores a plurality of average values, the step is to calculate the difference value by using a plurality of average values (including the newly calculated average value) stored in the same average value storage area, specifically, find the maximum value and the minimum value in a plurality of average values, and then calculate the difference value between the maximum value and the minimum value.
S106, judging whether at least one of the three average value differences is larger than a first preset value; if yes, step S107 is executed, and if no, step S108 is executed.
The first preset value is set by a technician through experience, and the experience is obtained based on specific experimental results and can be any value.
For example, if the maximum value in the a ' storage area is X1' and the minimum value is X7', the difference Δx between X1' and X7' is compared with the first preset value to determine whether the difference Δx is greater than the first preset value. And if the maximum value in the B ' storage area is Y3', and the minimum value is Y9', comparing the difference DeltaY of Y3' -Y9' with the first preset value, and judging whether the difference DeltaY is larger than the first preset value. And if the maximum value in the C ' storage area is Z6', and the minimum value is Z2', comparing the difference DeltaZ of Z6' -Z2' with the first preset value, and judging whether the difference DeltaZ is larger than the first preset value.
S107, identifying that movement occurs.
S108, identifying that the movement does not occur.
It should be noted that, in this embodiment, as long as any one of the three average value differences (such as Δx, Δy, and Δz in the above example) is greater than the first preset value, or any two, or even all three are greater than the first preset value, it is determined that the beauty treatment apparatus has moved, and otherwise, it is determined that the beauty treatment apparatus has not moved.
Although the terms of three-axis acceleration data, raw data storage area, average value, difference value, etc. are used more in the present application, the possibility of using other terms is not excluded. These terms are used merely for convenience in describing and explaining the nature of the application; they are to be interpreted as any additional limitation that is not inconsistent with the spirit of the present application.
According to the mobile identification method, the mobile identification is carried out by acquiring and utilizing the triaxial acceleration data, and only the triaxial acceleration data is compared and sorted and is subjected to simple operation, so that the logic is simple, the operation amount is small, the requirements on a processor are low, the identification range is wide, and the mobile identification method has a high application prospect in the field of mobile identification.
Example two
Referring to fig. 4, fig. 4 is a flow chart of a mobile identification method according to an embodiment of the invention. The present embodiment further optimizes the method before step S108 "identify no movement" based on the technical solution provided in the first embodiment. The explanation of the terms identical to or corresponding to the above embodiments is not repeated herein, and specifically, the method provided in this embodiment may further include the following steps: judging whether at least two of the three average value differences are larger than a second preset value or not, wherein the second preset value is smaller than the first preset value; if yes, returning to the step of executing the identification movement; if not, the step of identifying that no movement occurs is executed downwards.
Based on the above optimization, as shown in fig. 4, the mobile identification method provided in this embodiment may specifically include the following steps: s201, acquiring triaxial acceleration data, wherein the triaxial acceleration data comprise X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data.
S202, storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data into corresponding original data storage areas respectively, wherein the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data correspond to one original data storage area respectively.
And S203, respectively carrying out average value calculation on the data in the three original data storage areas to obtain three average values.
S204, respectively storing the three average values into corresponding average value storage areas, wherein the three average values respectively correspond to one average value storage area.
And S205, respectively carrying out difference calculation on the maximum value and the minimum value in the three average value storage areas to obtain three average value difference values.
S206, judging whether at least one of the three average value differences is larger than a first preset value; if yes, step S207 is executed, and if no, step S208 is executed.
S207, recognizing that movement occurs.
S208, judging whether at least two of the three average value differences are larger than a second preset value, wherein the second preset value is smaller than the first preset value; if yes, the process returns to step S207, and if no, step S209 is performed.
It should be noted that, in order to set only one preset value for judging whether the beauty instrument is moving or not, that is, if the situation that there is a possibility of false detection exists, that is, if the micro-movement cannot be identified, two preset values for judging whether the beauty instrument is moving or not are set in this embodiment, that is, the first preset value and the second preset value.
Similarly, the second preset value is also set by the technician through experience, and the experience is obtained based on specific experimental results and can be any value.
Specifically, by setting the first preset value to be relatively large, it is possible to recognize a movement of relatively large magnitude, that is, when at least one of the three average value differences (such as Δx, Δy, and Δz exemplified in embodiment one) is larger than the first preset value, it is indicated that the beauty instrument has moved by a relatively large magnitude; and by setting the second preset value to be smaller, the method can be used for identifying small movements with smaller amplitude, namely when it is initially determined that any one of the three average value differences is not larger than the first preset value, the method does not immediately determine that the beauty instrument does not move, but further determines whether at least two of the three average value differences are larger than the second preset value, if it is continuously determined that at least two of the three average value differences are larger than the second preset value, the beauty instrument is not moved to a larger extent, but is moved to a smaller extent, and if and only if only one of the three average value differences is larger than the second preset value, or none of the three average value differences is larger than the second preset value, the beauty instrument is finally determined that the beauty instrument does not move.
S209, identifying that no movement occurs.
Besides the beneficial effects of the first embodiment, the embodiment of the invention can accurately identify the large-amplitude movement and the small-amplitude movement of the beauty instrument by refining the judging conditions of whether the beauty instrument moves or not, namely setting a plurality of preset values, thereby being beneficial to improving the use experience of the beauty instrument.
Example III
Referring to fig. 5, fig. 5 is a flow chart of a mobile identification method according to an embodiment of the invention. Based on the technical solution provided in the first embodiment, step S102 "the X-axis acceleration data, the Y-axis acceleration data, and the Z-axis acceleration data are respectively stored in corresponding raw data storage areas, and the X-axis acceleration data, the Y-axis acceleration data, and the Z-axis acceleration data are respectively corresponding to one of the raw data storage areas" are further optimized. The explanation of the same or corresponding terms as those of the above embodiments will not be repeated here, namely: advancing all data stored in the three original data storage areas by one bit respectively, wherein the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data correspond to one original data storage area respectively; and respectively storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data to the tail parts of the corresponding original data storage areas.
Based on the above optimization, as shown in fig. 5, the mobile identification method provided in this embodiment may specifically include the following steps: s301, acquiring triaxial acceleration data, wherein the triaxial acceleration data comprise X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data.
S302, all data stored in the three original data storage areas are respectively moved forward by one bit, and the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data respectively correspond to one original data storage area.
And S303, storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data to the tail parts of the corresponding original data storage areas respectively.
It should be noted that, the data in the original data storage area is written in the order of each acquisition.
Because the requirements on real-time and accuracy of mobile identification of the beauty instrument in the embodiment are high, in order to overcome errors caused by random interference during data acquisition, the embodiment adopts a data moving average filtering method to process the data in the original data storage area, so that the aim of inhibiting periodic interference is fulfilled. The specific principle is as follows: the data in the original data storage area is regarded as a queue, the length of the queue is n, after new data are acquired each time, all the data in the queue are moved forward by one bit, then the new data acquired each time are put into the tail of the queue, so that n data which are arranged according to the acquisition time sequence are always arranged in the queue, and then the average value calculation is carried out on the n data in the newly-formed queue.
That is, the data in the a storage area, the B storage area, and the C storage area in fig. 2 are all arranged in the order of time of acquisition, with the acquired arrangement being before, with the acquired arrangement being after.
When the newly acquired X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data are Xn+1, yn+1 and Zn+1 respectively, storing Xn+1 to the tail of the data in the A storage area, namely the back of Xn, discarding the X1 at the head of the queue, and advancing the rest of the data in the queue by one bit to ensure that only n data remain in the A storage area. Similarly, yn+1 is stored to the tail of the data in the B storage area, namely, the tail of Yn is discarded by Y1 at the head of the queue, and the rest of the data in the queue is moved forward by one bit, so that only n data still exist in the B storage area. And storing Zn+1 to the tail of the data in the C storage area, namely, the back of Zn, discarding the Z1 at the head of the queue, and advancing the rest data in the queue by one bit to ensure that only n data still exist in the C storage area.
S304, respectively carrying out average value calculation on the data in the three original data storage areas to obtain three average values.
And S305, respectively storing the three average values into corresponding average value storage areas, wherein the three average values respectively correspond to one average value storage area.
S306, respectively carrying out difference calculation on the maximum value and the minimum value in the three average value storage areas to obtain three average value difference values.
S307, judging whether at least one of the three average value differences is larger than a first preset value; if yes, go to step S308, if no, go to step S309.
S308, identifying that movement occurs.
S309, identifying that no movement occurs.
It will be appreciated that this embodiment may be based on the content provided in embodiment two in addition to the content provided in embodiment one, that is, in another implementation manner, this embodiment may further include the added step content of embodiment two.
Besides the beneficial effects of the first embodiment, the embodiment of the invention further provides a scheme of how to store the newly acquired acceleration data into the original data storage area so as to inhibit periodic interference, thereby improving the accuracy and the reliability of mobile identification.
Example IV
Referring to fig. 6, fig. 6 is a flow chart of a mobile identification method according to an embodiment of the invention. Based on the technical scheme provided in the first embodiment, the present embodiment further optimizes step S103 "respectively performing average calculation on the data in the three original data storage areas to obtain three average values". The explanation of the same or corresponding terms as those of the above embodiments will not be repeated here, namely: and respectively carrying out average value calculation on the rest data except the maximum value and the minimum value in the three original data storage areas to obtain three average values.
Based on the above optimization, as shown in fig. 6, the mobile identification method provided in this embodiment may specifically include the following steps: s401, acquiring triaxial acceleration data, wherein the triaxial acceleration data comprise X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data.
S402, storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data into corresponding original data storage areas respectively, wherein the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data correspond to one original data storage area respectively.
And S403, respectively carrying out average value calculation on the rest data except the maximum value and the minimum value in the three original data storage areas to obtain three average values.
It should be noted that, since the average value is more easily affected by the extreme values, in order to eliminate the influence of the extreme values on the average value, the extreme values at the two ends, that is, the maximum value and the minimum value, need to be removed, and only the remaining data is used to perform average value calculation, so that the centralized trend of the data can be better reflected, and the purpose of removing the interference is achieved.
S404, respectively storing the three average values into corresponding average value storage areas, wherein the three average values respectively correspond to one average value storage area.
And S405, respectively carrying out difference calculation on the maximum value and the minimum value in the three average value storage areas to obtain three average value difference values.
S406, judging whether at least one of the three average value differences is larger than a first preset value; if yes, step S407 is executed, and if no, step S408 is executed.
S407, identifying that movement occurs.
S408, identifying that no movement occurs.
It will be appreciated that this embodiment may be based on the content provided in embodiment two and/or three in addition to the content provided in embodiment one, that is, in another implementation, this embodiment may further include the content of steps added in embodiment two and/or three.
The embodiment of the invention has the beneficial effects of the first embodiment, and the data cleaning mode of removing the maximum value and the minimum value and then averaging is adopted when the average value of the data in the three original data storage areas is calculated respectively, so that the accuracy of the data is greatly improved, and the effectiveness of the follow-up mobile identification judgment based on the data can be effectively ensured.
Example five
Referring to fig. 7, fig. 7 is a flow chart of a mobile identification method according to an embodiment of the invention. Based on the technical solution provided in the first embodiment, step S104 "storing the three average values in the corresponding average value storage areas, where the three average values correspond to one average value storage area" is further optimized. The explanation of the same or corresponding terms as those of the above embodiments will not be repeated here, namely: advancing all data stored in three average value storage areas by one bit respectively, wherein the three average values correspond to one average value storage area respectively; and respectively storing the three average values to the tail parts of the corresponding average value storage areas.
Based on the above optimization, as shown in fig. 7, the mobile identification method provided in this embodiment may specifically include the following steps: s501, acquiring triaxial acceleration data, wherein the triaxial acceleration data comprise X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data.
S502, storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data into corresponding original data storage areas respectively, wherein the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data correspond to one original data storage area respectively.
And S503, respectively carrying out average value calculation on the data in the three original data storage areas to obtain three average values.
S504, all data stored in the three average value storage areas are respectively shifted forward by one bit, and the three average values respectively correspond to one average value storage area.
And S505, respectively storing the three average values to the tail parts of the corresponding average value storage areas.
The averages in the average value storage area are written in the order obtained by each calculation.
For the same reason as the original data storage area, that is, because the real-time and accuracy requirements of the mobile identification of the beauty instrument in the embodiment are higher, in order to overcome the error caused by random interference in calculating the average value, the embodiment adopts a data moving average filtering method to process the average value in the average value storage area, so as to inhibit the periodic interference. The specific principle is as follows: and taking the average value in the average value storage area as a queue, wherein the length of the queue is m, after each calculation to obtain a new average value, advancing all the average values in the queue by one bit, and then putting the new average value obtained by each calculation into the tail of the queue, so that m average values which are arranged according to the time sequence obtained by calculation are always arranged in the queue, and subsequently, only the m average values in the newly-formed queue are subjected to difference calculation.
S506, respectively carrying out difference calculation on the maximum value and the minimum value in the three average value storage areas to obtain three average value difference values.
S507, judging whether at least one of the three average value differences is larger than a first preset value; if yes, step S508 is executed, and if no, step S509 is executed.
S508, identifying that movement occurs.
S509, identifying that no movement occurs.
It will be appreciated that this embodiment may be based on the content provided by the second and/or third and/or fourth embodiment in addition to the content provided by the first embodiment, that is, in another implementation manner, this embodiment may further include the content of the steps added by the second and/or third and/or fourth embodiment.
Besides the beneficial effects of the first embodiment, the embodiment of the invention further provides a scheme of how to store the newly calculated average value into the average value storage area so as to inhibit periodic interference, thereby improving the accuracy and the reliability of mobile identification.
Example six
Referring to fig. 8, a functional block diagram of a mobile recognition device according to a sixth embodiment of the present invention is shown, and the system is suitable for executing the mobile recognition method according to the embodiment of the present invention. The system specifically comprises the following modules: the data acquisition module 601 is configured to acquire triaxial acceleration data, where the triaxial acceleration data includes X-axis acceleration data, Y-axis acceleration data, and Z-axis acceleration data; the data storage module 602 is configured to store the X-axis acceleration data, the Y-axis acceleration data, and the Z-axis acceleration data in corresponding original data storage areas, where the X-axis acceleration data, the Y-axis acceleration data, and the Z-axis acceleration data respectively correspond to one of the original data storage areas; the average value calculating module 603 is configured to calculate average values of the data in the three original data storage areas, so as to obtain three average values; the average value storage module 604 is configured to store three average values into corresponding average value storage areas, where the three average values correspond to one average value storage area respectively; the difference calculation module 605 is configured to perform difference calculation on the maximum value and the minimum value in the three average value storage areas, so as to obtain three average value differences; a difference judging module 606, configured to judge whether at least one of the three average value differences is greater than a first preset value; if yes, identifying that movement occurs; if not, identifying that no movement occurs.
Preferably, the difference value determining module 606 is further configured to: before the step of identifying that no movement occurs, judging whether at least two of the three average value differences are larger than a second preset value, wherein the second preset value is smaller than the first preset value; if yes, returning to the step of executing the identification movement; if not, the step of identifying that no movement occurs is executed downwards.
Preferably, the data storage module 602 is specifically configured to: advancing all data stored in the three original data storage areas by one bit respectively; and respectively storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data to the tail parts of the corresponding original data storage areas.
Preferably, the mean value calculating module 603 is specifically configured to: and respectively carrying out average value calculation on the rest data except the maximum value and the minimum value in the three original data storage areas to obtain three average values.
Preferably, the mean value storage module 604 is specifically configured to: advancing all data stored in the three average value storage areas by one bit respectively; and respectively storing the three average values to the tail parts of the corresponding average value storage areas.
According to the mobile identification device, the triaxial acceleration data are acquired and utilized for mobile identification, and only the triaxial acceleration data are compared and sorted and are subjected to simple operation, so that the mobile identification device is simple in logic and small in operation amount, has low requirements on a processor, has a wide identification range and has a high application prospect in the field of mobile identification.
The device can execute the method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the method.
Example seven
Fig. 9 is a schematic structural diagram of a cosmetic apparatus according to a seventh embodiment of the present invention. Fig. 9 shows a block diagram of an exemplary cosmetic device 12 suitable for use in implementing embodiments of the present invention. The beauty treatment instrument 12 shown in fig. 9 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 9, the cosmetic device 12 is in the form of a general purpose computing device. The components of the cosmetic instrument 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
The cosmetic instrument 12 typically includes a variety of computer system-readable media. Such media can be any available media that can be accessed by cosmetic instrument 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. The cosmetic instrument 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 9, commonly referred to as a "hard disk drive"). Although not shown in fig. 9, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The cosmetic instrument 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the cosmetic instrument 12, and/or any devices (e.g., network card, modem, etc.) that enable the cosmetic instrument 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Furthermore, the cosmetic device 12 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 20. As shown, the network adapter 20 communicates with other modules of the cosmetic instrument 12 via the bus 18. It should be appreciated that although not shown in fig. 9, other hardware and/or software modules may be used in connection with the cosmetic instrument 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing the mobile recognition method provided by the embodiment of the present invention.
That is, the processing unit realizes when executing the program: acquiring triaxial acceleration data, wherein the triaxial acceleration data comprise X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data; storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data into corresponding original data storage areas respectively, wherein the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data correspond to one original data storage area respectively; respectively carrying out average value calculation on the data in the three original data storage areas to obtain three average values; respectively storing the three average values into corresponding average value storage areas, wherein the three average values respectively correspond to one average value storage area; respectively carrying out difference calculation on the maximum value and the minimum value in the three average value storage areas to obtain three average value difference values; judging whether at least one of the three average value differences is larger than a first preset value; if yes, identifying that movement occurs; if not, identifying that no movement occurs.
Example eight
An eighth embodiment of the present application provides a computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement a movement recognition method as provided by all embodiments of the present application: that is, the processor, when executing the computer-executable instructions, implements: acquiring triaxial acceleration data, wherein the triaxial acceleration data comprise X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data; storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data into corresponding original data storage areas respectively, wherein the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data correspond to one original data storage area respectively; respectively carrying out average value calculation on the data in the three original data storage areas to obtain three average values; respectively storing the three average values into corresponding average value storage areas, wherein the three average values respectively correspond to one average value storage area; respectively carrying out difference calculation on the maximum value and the minimum value in the three average value storage areas to obtain three average value difference values; judging whether at least one of the three average value differences is larger than a first preset value; if yes, identifying that movement occurs; if not, identifying that no movement occurs.
Any combination of one or more computer readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having 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. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (9)
1. A method of mobile identification, comprising:
Acquiring triaxial acceleration data, wherein the triaxial acceleration data comprise X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data;
Storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data into corresponding original data storage areas respectively, wherein the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data correspond to one original data storage area respectively;
respectively carrying out average value calculation on the data in the three original data storage areas to obtain three average values;
respectively storing the three average values into corresponding average value storage areas, wherein the three average values respectively correspond to one average value storage area; each average value storage area stores a plurality of average values;
Respectively carrying out difference calculation on the maximum value and the minimum value in the three average value storage areas to obtain three average value difference values;
Judging whether at least one of the three average value differences is larger than a first preset value;
if yes, identifying that movement occurs;
if not, identifying that the movement does not occur;
the step of storing the X-axis acceleration data, the Y-axis acceleration data, and the Z-axis acceleration data in the corresponding raw data storage areas respectively specifically includes:
Advancing all data stored in the three original data storage areas by one bit respectively;
storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data to the tail parts of the corresponding original data storage areas respectively;
The data in the original data storage area are written in the order of each acquisition; the data in the original data storage area is regarded as a queue, the length of the queue is n, after new data are acquired each time, all the data in the queue are moved forward by one bit, then the new data acquired each time are put into the tail of the queue, so that n data which are arranged according to the acquisition time sequence are always arranged in the queue, and then the average value calculation is carried out on the n data in the newly-formed queue.
2. The method of mobile identification of claim 1, wherein prior to the step of identifying that no movement has occurred, the method further comprises:
Judging whether at least two of the three average value differences are larger than a second preset value or not, wherein the second preset value is smaller than the first preset value;
if yes, returning to the step of executing the identification movement;
if not, the step of identifying that no movement occurs is executed downwards.
3. The mobile recognition method according to claim 1, wherein the step of calculating the average value of the data in the three original data storage areas to obtain three average values is specifically:
And respectively carrying out average value calculation on the rest data except the maximum value and the minimum value in the three original data storage areas to obtain three average values.
4. The mobile recognition method according to claim 1, wherein the step of storing the three average values in the corresponding average value storage areas, respectively, specifically comprises:
Advancing all data stored in the three average value storage areas by one bit respectively;
And respectively storing the three average values to the tail parts of the corresponding average value storage areas.
5. A mobile identification appliance comprising:
The data acquisition module is used for acquiring triaxial acceleration data, wherein the triaxial acceleration data comprise X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data;
the data storage module is used for respectively storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data into corresponding original data storage areas, and the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data respectively correspond to one original data storage area;
the average value calculation module is used for calculating the average value of the data in the three original data storage areas respectively to obtain three average values;
The average value storage module is used for respectively storing the three average values into corresponding average value storage areas, and the three average values respectively correspond to one average value storage area; each average value storage area stores a plurality of average values;
The difference calculation module is used for respectively carrying out difference calculation on the maximum value and the minimum value in the three average value storage areas to obtain three average value differences;
the difference judging module is used for judging whether at least one of the three average value differences is larger than a first preset value; if yes, identifying that movement occurs; if not, identifying that the movement does not occur;
The data storage module is specifically configured to:
Advancing all data stored in the three original data storage areas by one bit respectively;
storing the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data to the tail parts of the corresponding original data storage areas respectively;
The data in the original data storage area are written in the order of each acquisition; the data in the original data storage area is regarded as a queue, the length of the queue is n, after new data are acquired each time, all the data in the queue are moved forward by one bit, then the new data acquired each time are put into the tail of the queue, so that n data which are arranged according to the acquisition time sequence are always arranged in the queue, and then the average value calculation is carried out on the n data in the newly-formed queue.
6. The mobile identification device of claim 5, wherein the difference determination module is further configured to:
before the step of identifying that no movement occurs, judging whether at least two of the three average value differences are larger than a second preset value, wherein the second preset value is smaller than the first preset value; if yes, returning to the step of executing the identification movement; if not, the step of identifying that no movement occurs is executed downwards.
7. The mobile identification device of claim 5, wherein the means for calculating the average value is specifically configured to:
respectively carrying out average value calculation on the rest data except the maximum value and the minimum value in the three original data storage areas to obtain three average values;
the mean value storage module is specifically configured to:
Advancing all data stored in the three average value storage areas by one bit respectively;
And respectively storing the three average values to the tail parts of the corresponding average value storage areas.
8. Cosmetic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the movement recognition method according to any one of claims 1-4 when executing the computer program.
9. A storage medium containing computer executable instructions for execution by a computer processor to implement the mobile identification method of any one of claims 1-4.
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| CN114385012B (en) * | 2022-01-17 | 2023-06-30 | 维沃移动通信有限公司 | Motion recognition method, motion recognition device, electronic equipment and readable storage medium |
| CN115191984B (en) * | 2022-06-23 | 2025-04-11 | 广东花至美容科技有限公司 | Skin impedance acquisition method, storage medium and electronic device of beauty instrument |
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