A data processing method, apparatus, electronic device, and storage medium

Abstract

This application discloses a data processing method, apparatus, electronic device, and storage medium. The method includes: acquiring first data of a currently detected first user; determining the identifier of at least one candidate user based on the first data; ensuring that second data of the candidate users and the first data satisfy the target conditions; receiving a user selection instruction; determining the identifier of a second user from the identifiers of the at least one candidate user based on the user selection instruction; and associating the first data with the second user for storage. This application can improve the user experience of using a smart scale.

Description

Technical Field

[0001] This application relates to the field of electronic weighing instruments, and more particularly to a data processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] As people's living standards continue to improve, they are paying more and more attention to health. Smart scales play a quick and convenient role in managing human health.

[0003] When using a smart scale, if multiple people weigh themselves on the scale and their weight data is similar, it is necessary to press a button on the mobile app to identify the user's account and save the user's various body indicators.

[0004] If the user does not have a mobile phone or cannot identify the user's account through the function buttons on the smart scale, the user will not be able to save the user's various physical indicators, which will affect the user's experience. Summary of the Invention

[0005] This application provides a data processing method and a smart scale that eliminates the need to identify a second user through a mobile phone's function keys, thus improving the user experience of using the smart scale.

[0006] The first aspect of this application provides a data processing method, including:

[0007] The system acquires the first data of the first detected user, determines the identifier of at least one candidate user based on the first data, the second data of the candidate user and the first data satisfy the target conditions, receives the user selection instruction, and determines the identifier of the second user from the identifiers of at least one candidate user based on the user selection instruction, and stores the first data in association with the second user.

[0008] Based on the implementation method of the first aspect of this application, in one possible implementation, the first data includes first anthropometric data; the second data of the candidate user includes second anthropometric data of a first parameter dimension and / or second anthropometric data of a second parameter dimension. If the second data only includes second anthropometric data of the first parameter dimension, the target condition is that the first comparison result is less than a first preset threshold; if the second data only includes second anthropometric data of the second parameter dimension, the target condition is that the second comparison result is less than a second preset threshold; if the second data includes second anthropometric data of the first parameter dimension and second anthropometric data of the second parameter dimension, the target condition is that the first comparison result is less than a first preset threshold, and the second comparison result is less than a second preset threshold; wherein, the second anthropometric data of the first parameter dimension is the second anthropometric prediction data of the candidate user obtained under the current trend; the second anthropometric data of the second parameter dimension is the second anthropometric prediction data obtained based on the historical measurement data of the candidate user; the first comparison result is the difference between the first anthropometric data and the second anthropometric data of the first parameter dimension; the second comparison result is the difference between the first anthropometric data and the second anthropometric data of the second parameter dimension.

[0009] Based on the implementation method of the first aspect of this application, in one possible implementation, the first data further includes the first time of the current detection, and the method further includes: determining the basic data and change values ​​of the candidate user, wherein the change values ​​include at least one of a first change value, a second change value, and a third change value; wherein the first change value is used to indicate the change value corresponding to the time period to which the first time belongs; the second change value is used to indicate the change value corresponding to the dietary records within a preset time period before the first time; the third change value is used to indicate the change value corresponding to the human body consumption within a preset time period before the first time, and the second human body data of the candidate user in the first parameter dimension is determined based on the basic data and the change values.

[0010] Based on the implementation method of the first aspect of this application, in one possible implementation, the second data of the candidate user also includes the second time corresponding to the basic data of the candidate user, and the target condition also includes: the third comparison result is less than the third preset threshold, and the third comparison result is the difference between the first time and the second time.

[0011] Based on the implementation method of the first aspect of this application, in one possible implementation, the first data further includes first behavior data, the second data of the candidate user further includes candidate behavior data, and the target condition further includes: the first behavior data and the candidate behavior data are the same as a fourth comparison result.

[0012] Based on the implementation method of the first aspect of this application, in one possible implementation, the method further includes: sorting and displaying at least one candidate user according to the priority of the candidate user; the priority of the candidate user is determined according to at least one of the following: the preset priority of the candidate user, the first comparison result of the candidate user, the second comparison result, the third comparison result, or the fourth comparison result.

[0013] Based on the implementation method of the first aspect of this application, in one possible implementation, the method further includes: adjusting the target conditions according to the data of the second user and the data of the alternative user.

[0014] Based on the implementation method of the first aspect of this application, in one possible implementation, receiving a user selection instruction includes: determining the user selection instruction by collecting the body movements of the first user through a sensor or camera, or determining the user selection instruction by collecting the facial recognition information of the first user through a camera, or receiving a user selection instruction sent by a smart wearable device or smart mirror, wherein the user selection instruction includes a user confirmation instruction or a user switching instruction.

[0015] A second aspect of this application provides a data processing apparatus, comprising:

[0016] The acquisition unit is used to acquire the first data of the first detected user.

[0017] The processing unit is configured to determine the identifier of at least one candidate user based on the first data, wherein the second data of the candidate user and the first data satisfy the target conditions;

[0018] The receiving unit is configured to receive a user selection instruction and determine the identifier of a second user from the identifiers of at least one candidate user according to the user selection instruction.

[0019] A storage unit is used to associate and store the first data with the second user.

[0020] Based on the implementation method of the second aspect of this application, in one possible implementation, the first data includes first anthropometric data; the second data of the candidate user includes second anthropometric data of the first parameter dimension and / or second anthropometric data of the second parameter dimension.

[0021] If the second data only includes the second human body data of the first parameter dimension, then the target condition is that the first comparison result is less than the first preset threshold.

[0022] If the second data only includes the second human body data of the second parameter dimension, then the target condition is: the target condition is that the second comparison result is less than the second preset threshold;

[0023] If the second data includes the second human body data of the first parameter dimension and the second human body data of the second parameter dimension, then the target condition is: the target condition is that the first comparison result is less than the first preset threshold and the second comparison result is less than the second preset threshold;

[0024] Wherein, the second human body data of the first parameter dimension is the second human body prediction data of the candidate user under the current trend; the second human body data of the second parameter dimension is the second human body prediction data obtained based on the historical measurement data of the candidate user; the first comparison result is the difference between the first human body measurement data and the second human body data of the first parameter dimension; the second comparison result is the difference between the first human body measurement data and the second human body data of the second parameter dimension.

[0025] Based on the implementation method of the second aspect of this application, in one possible implementation, the first data further includes the first time of the current detection, and the processing unit is further configured to determine the basic data and change values ​​of the candidate user, wherein the change values ​​include at least one of a first change value, a second change value, and a third change value; wherein the first change value is used to indicate the change value corresponding to the time period to which the first time belongs; the second change value is used to indicate the change value corresponding to the dietary record within a preset time period before the first time; and the third change value is used to indicate the change value corresponding to the human body consumption within the preset time period before the first time.

[0026] The processing unit is also used to determine the second human body data of the first parameter dimension of the candidate user based on the basic data and the change value.

[0027] Based on the implementation method of the second aspect of this application, in one possible implementation, the second data of the candidate user also includes the second time corresponding to the basic data of the candidate user, and the target condition also includes: the third comparison result is less than the third preset threshold, and the third comparison result is the difference between the first time and the second time.

[0028] Based on the implementation method of the second aspect of this application, in one possible implementation, the first data further includes first behavior data, the second data of the candidate user further includes candidate behavior data, and the target condition further includes:

[0029] The first row of data and the alternative row of data are the same fourth comparison result.

[0030] Based on the implementation method of the second aspect of this application, in one possible implementation, the processing unit is further configured to sort and display at least one candidate user according to the priority of the candidate user; the priority of the candidate user is determined according to at least one of the following: the preset priority of the candidate user, the first comparison result of the candidate user, the second comparison result, the third comparison result or the fourth comparison result.

[0031] Based on the implementation method of the second aspect of this application, in one possible implementation, the processing unit is further configured to adjust the target conditions according to the data of the second user and the data of the alternative user.

[0032] Based on the implementation method of the second aspect of this application, in one possible implementation, receiving the user selection instruction includes:

[0033] The user's selection command is determined by capturing the first user's body movements using sensors or cameras.

[0034] Alternatively, the user's selection command can be determined by collecting the first user's facial recognition information through a camera;

[0035] Alternatively, it can receive user selection instructions sent by smart wearable devices or smart mirrors;

[0036] Among them, user selection instructions include user confirmation instructions or user switching instructions.

[0037] A third aspect of this application provides an electronic device.

[0038] Processor, memory;

[0039] The processor is connected to the memory;

[0040] The processor executes the method as performed in any of the embodiments of the first aspect of this application.

[0041] The fourth aspect of this application provides a computer storage medium.

[0042] A computer storage medium storing instructions that, when executed on a computer, cause the computer to perform a method as described in any of the embodiments of the first aspect of this application.

[0043] The fifth aspect of this application provides a computer program product.

[0044] A computer program product, characterized in that, when executed on a computer, causes the computer to perform a method as performed in any of the embodiments of the first aspect of this application.

[0045] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0046] In this embodiment, after obtaining the first user's first data, at least one candidate user's identifier is determined based on the first data. The relationship between the candidate user's second data and the first data satisfies the target condition. Then, a second user is determined from the at least one candidate user through a user selection command. This eliminates the need to use the phone's function keys to determine the second user, thus improving the user experience of using the smart scale. Attached Figure Description

[0047] Figure 1 This is a schematic diagram illustrating a usage scenario of a smart scale in the prior art, provided as an embodiment of this application.

[0048] Figure 2 A schematic diagram illustrating a use case of the data processing method provided in this application embodiment;

[0049] Figure 3 A flowchart illustrating the data processing method provided in this application embodiment;

[0050] Figure 4 A schematic diagram of a planar structure of a smart scale provided in an embodiment of this application;

[0051] Figure 5 This is a schematic diagram of another planar structure of the smart scale provided in the embodiments of this application;

[0052] Figure 6 A schematic diagram of the structure of the smart scale provided in this application embodiment;

[0053] Figure 7 This is another structural schematic diagram of the smart scale provided in an embodiment of this application. Detailed Implementation

[0054] This application provides a data processing method, apparatus, electronic device, and storage medium. After acquiring first data of a first user, the identifier of at least one candidate user is determined based on the first data, and the relationship between the second data of the candidate user and the first data satisfies a target condition. Then, a second user is determined from the at least one candidate user through a user selection command, which eliminates the need to use the function keys of a mobile phone to determine the second user, thus improving the user experience of using the smart scale.

[0055] Please see Figure 1 This is a usage scenario of a smart scale in the prior art provided in the embodiments of this application.

[0056] With the continuous improvement of people's living standards, people are paying more and more attention to health. Smart scales play a quick and convenient role in health management. Currently, when multiple people use a single smart scale, if the weight data obtained from multiple weighings is similar, a function button on a mobile app is required to identify the user's account and save their corresponding body indicators. Figure 1 As shown, before weighing, users need to connect their mobile phones to the smart scale, and then select the corresponding account through the mobile app before they can use the smart scale to measure various indicators of the human body.

[0057] If the user does not have a mobile phone or cannot identify the user's account through the smart scale, the user will not be able to save the user's various physical indicators, which will affect the user's experience.

[0058] To address the aforementioned issues, embodiments of this application provide a data processing method, apparatus, electronic device, and storage medium that eliminate the need to identify a second user by operating a mobile phone, thereby improving the user experience of using a smart scale.

[0059] In this embodiment of the application, a smart weight scale is used as an example for the data processing device. In actual application, the data processing device may also be other devices, such as a smart body fat scale. In fact, this application does not limit the data processing device and electronic device to the devices that may be used.

[0060] Please see Figure 2 , Figure 2 A usage scenario diagram of the data processing method provided in the embodiments of this application.

[0061] When the first user uses the smart scale, the first user stands on the smart scale to weigh themselves. The smart scale uses sensors to calculate the first user's weight, body fat and other data, and then generates the first data, which represents the first user's weighing data. The first user is the user identified as the user to be confirmed.

[0062] After acquiring the first detected data, the smart scale identifies at least one candidate user from all saved users based on the first data. The relationship between the candidate user's second data and the first data satisfies a pre-set target condition. Specifically, based on the second data of all users, at least one candidate user meeting the target condition is selected from all users.

[0063] After identifying at least one candidate user, the smart scale receives the user's selection instruction.

[0064] After receiving a user selection instruction, the smart scale determines the identifier of a second user from at least one pool of candidate user identifiers. This second user is the one that matches the first data. Optionally, the user selection instruction may be used to instruct the selection of a second user from the displayed pool of candidate users.

[0065] After identifying the second user, the smart scale associates and stores the first data with the second user.

[0066] In this embodiment, after acquiring the first data, the smart scale identifies at least one candidate user based on the first data, and the relationship between the second data of the candidate user and the first data satisfies the target condition. Then, a second user is determined from the at least one candidate user through a user selection command, eliminating the need to use a mobile phone's function keys to determine the second user, thus improving the user experience of using the smart scale.

[0067] Based on the foregoing Figure 2 The data processing method shown is illustrated in one use case. The following is a detailed description of the data processing method in this embodiment of the application. Please refer to... Figure 3 This is a flowchart illustrating a data processing method provided in an embodiment of this application.

[0068] In step 301, the smart scale acquires the first data of the first user currently detected.

[0069] Traditional weighing scales typically only measure a user's weight. However, as people become increasingly health-conscious, they are also paying more attention to various health-related body data. Smart weighing scales can help users obtain more comprehensive body data, such as weight and body fat percentage.

[0070] When the first user weighs themselves using the smart scale, the detected data becomes the first user's "first data," where the first user is the user identified as the user to be confirmed. It is understood that this "first data" may include, but is not limited to, weight data, body fat data, etc.

[0071] In step 302, the smart scale determines the identifier of at least one candidate user based on the first data.

[0072] When using a smart scale, because everyone's body data is different, the smart scale will create different user accounts for different users to store their weighing data. After obtaining the first user's first data, the smart scale will determine the identifier of at least one candidate user from all the saved user accounts based on the first data. The relationship between the candidate user's second data and the first data satisfies the target condition.

[0073] This objective condition is used to filter out the identifier of at least one candidate user from multiple user identifiers. Specifically, it represents the correlation between the first set of data and the second set of data for the candidate user. The following section will provide a detailed explanation of how this objective condition is met:

[0074] (1) In a first possible implementation, the first data includes first anthropometric data, and the second data of the candidate user includes second anthropometric data in the first parameter dimension and / or second anthropometric data in the second parameter dimension. If the second data only includes second anthropometric data in the first parameter dimension, the target condition includes a first comparison result less than a first preset threshold. The first comparison result represents the difference between the first anthropometric data and the second anthropometric data in the first parameter dimension, wherein the second anthropometric data in the first parameter dimension represents the second anthropometric prediction data of the candidate user under the current trend.

[0075] (2) In the second possible implementation, if the second data only includes the second human body data of the second parameter dimension, the target condition includes that the second comparison result is less than the second preset threshold. The second comparison result represents the difference between the first human body measurement data and the second human body data of the second parameter dimension. The second human body data of the second parameter dimension represents the second human body prediction data obtained based on the historical measurement data of the candidate user. For example, the second human body data of the candidate user in the second parameter dimension may include the average of all weight measurements already saved by the candidate user or within a certain period (e.g., within two weeks), or a previous weight measurement value. The specific settings can be configured according to actual needs and are not limited here.

[0076] For example, the first anthropometric data includes the weight value measured by the first user through a smart scale, such as 70 kg, which means the first user weighs 70 kg. The second anthropometric data of the candidate user's second parameter dimension includes the last weight measurement value saved in the candidate user's database, such as 65 kg, which means the last weight measurement of the candidate user was 65 kg. The difference between the first anthropometric data and the candidate user's last weight measurement value is 5 kg. If the second preset threshold is 10 kg, then the difference of 5 kg is within the range of the second preset threshold of 10 kg, indicating that the user's weight change is within expectations, and it is likely the user account corresponding to the first data, thus confirming them as a candidate user. Figure 4 As shown, on the display screen of the smart scale, the weight information represents the first human body measurement data of the first user, and user 1 and user 2 among the candidate users are the confirmed candidate users.

[0077] (3) In the third possible implementation, if the second data includes both the second human body data of the first parameter dimension and the second human body data of the second parameter dimension, then the first comparison result is less than the first preset threshold, and the second comparison result is less than the second preset threshold; wherein, the second human body data of the first parameter dimension is the second human body prediction data obtained by the candidate user under the current trend; the second human body data of the second parameter dimension is the second human body prediction data obtained based on the candidate user's historical measurement data; the first comparison result is the difference between the first human body measurement data and the second human body data of the first parameter dimension; the second comparison result is the difference between the first human body measurement data and the second human body data of the second parameter dimension. For example, the weight value in the first human body measurement data is 70 kg, the weight value in the second human body data of the first parameter dimension is 65 kg, the weight value in the second human body data of the second parameter dimension, that is, the average weight value of the user in the past two weeks is 68 kg, the first preset threshold is 10 kg, and the second preset value is 8 kg, then the difference between the first human body measurement data and the second human body data of the first parameter dimension is less than the first preset threshold, and the difference between the first human body measurement data and the second human body data of the second parameter dimension is less than the second preset threshold, thus satisfying the target condition.

[0078] It should be noted that the first and second preset thresholds can be set based on the user's historical weight data curve, the user's expectations, or the weight data from big data analysis; no specific limitations are made here.

[0079] (4) In a fourth possible implementation, combining the first or third possible implementation methods described above, the first data further includes the first time of the current detection, and the second human body data of the first parameter dimension is determined based on the basic data and change values ​​of the candidate users. The change values ​​include at least one of the first, second, and third change values. The basic data of the candidate users includes the most recent human body data measured for the user corresponding to that candidate user. The change values ​​represent the weights corresponding to certain factors that can affect weight measurement. This is because during the process of measuring the weight data of the weighing user, some factors that may affect the weight of the weighing user may arise.

[0080] The "first change value" indicates the change within the time period corresponding to the "first time". For example, during a day's weight measurement, the weight measured in the morning is often lower than the weight measured in the afternoon. Therefore, "first time" represents the time when the user's first data measurement was taken, and the time period to which the "first time" belongs represents the time period of the day corresponding to this measurement. Thus, if the user's baseline data measurement was in the afternoon, and this user's first data measurement was in the morning, the weight difference due to the different weighing times throughout the day can be added to this measurement. In other words, the "first change value" represents the weight difference due to the different weighing times throughout the day. Comparing this with the previous weight measurement improves the accuracy of identifying potential users.

[0081] The second change value indicates the change in dietary records within a preset time period prior to the first measurement. For example, if the smart scale obtains the dietary records of a candidate user before this measurement from the server, it can calculate the potential weight gain based on these records. The smart scale then uses this potential difference as the second change value in its calculations, thus improving the accuracy of its assessment of the candidate user. It is understood that the preset time period can be set according to actual circumstances, such as a day or 5 hours, etc., and is not specifically limited here.

[0082] The third change value is used to indicate the change in the body's energy expenditure within a preset time period prior to the first time. For example, if the smart scale obtains the energy expenditure data of a candidate user before this measurement from the server, it can calculate the possible weight loss based on the energy expenditure data. The smart scale will then use the potential difference as the third change value in its calculations, thus improving the accuracy of identifying the candidate user.

[0083] It is understandable that the body's energy expenditure can be information on the calories consumed by the body's normal daily metabolism, or information on the calories consumed by the body's exercise, etc. The specific details of the body's energy expenditure are not limited here.

[0084] It should be noted that the smart scale can obtain dietary records and body consumption data from a server, or it can obtain these records and data from the local storage of the smart scale. The specific method of obtaining these records and data is not limited here.

[0085] (5) In the fifth possible implementation, the second data of the candidate user also includes the second time corresponding to the basic data of the candidate user, and the target condition also includes the third comparison result being less than the third preset threshold, whereby the third comparison result represents the difference between the first time and the second time.

[0086] Specifically, the first time represents the time corresponding to the first user's current weighing, for example, 12:10. The second time represents the last recorded weighing time of the candidate user, for example, 12:30. The difference between the first and second times is 20 minutes. If the third preset threshold is 30 minutes, it means that the weighing time corresponding to the first data is close to the weighing time of the candidate user, possibly indicating the same user. Therefore, this user can be identified as a candidate user. It is understood that the third preset threshold can be an empirical value or calculated using an algorithm; this is not limited here. It is also understood that the second time can represent the candidate user's historical average weighing time or any one of the historical weighing times; this is not limited here either. Figure 5 As shown, the weighing time represents the first time, and User 1 and User 2 among the alternative users represent users whose weighing time is close to the first weighing time.

[0087] The fifth implementation can be used as a standalone objective condition to determine the identifier of at least one candidate user, which can be achieved by obtaining the first time of the current detection included in the first data and the second time corresponding to the basic data of the candidate user; or it can be combined with the objective conditions of one or more of the above four implementation methods (1), (2), (3), and (4) to determine the identifier of at least one candidate user. This application does not limit this.

[0088] It is understandable that in practical applications, the first data and the second data of the candidate users may also include indicators such as the user's body fat percentage, or indicators that judge both body data such as weight and body fat. No specific restrictions are made here.

[0089] (6) In one possible implementation, the first data also includes first behavioral data, the second data of the candidate user also includes candidate behavioral data, and the target condition also includes a fourth comparison result in which the first behavioral data and the candidate behavioral data are the same.

[0090] Specifically, the first behavioral data represents the user's weighing habits during this weighing, such as whether the user puts their left foot on the smart scale first or their right foot first. The alternative behavioral data represents the user's previous weighing habits, or habits derived from historical weighing patterns that are likely to lead to a weighing outcome. Comparing the first behavioral data and the alternative behavioral data yields a fourth comparison result. If the fourth comparison result is the same, the user is identified as a potential alternative user. Specifically, to determine whether the user puts their left foot on first or their right foot on first, the pressure is measured at different locations on the smart scale using four different pressure sensors at its four corners. For example, if the user puts their left foot on first, the pressure sensor on the left will show a higher reading, and the pressure at the four corners will gradually converge. If the user puts their right foot on first, the pressure sensor on the right will show a higher reading. Therefore, it can be determined whether the user put their left foot on first or their right foot on first. It is understandable that the primary behavioral data and alternative behavioral data may also include other behavioral habits, such as whether or not one rotates their body on the smart scale. These can be further determined by combining gyroscopes and accelerometers. No specific restrictions are placed on the behavioral habits corresponding to the primary behavioral data and alternative behavioral data.

[0091] This sixth implementation can be used alone as a target condition to determine the identifier of at least one candidate user, as long as the first behavior data included in the first data and the candidate behavior data included in the second data are obtained; or it can be combined with the target conditions of one or more of the above five implementations (1), (2), (3), (4), and (5) to determine the identifier of at least one candidate user. This application does not limit this.

[0092] It should be noted that in scenarios where two or more target conditions are combined to identify at least one candidate user, different target conditions can be quantified into different weights. That is, the weight ratios corresponding to different target conditions can be set according to actual circumstances or experience, because different target conditions have different degrees of influence on the identification of candidate users, thereby improving the accuracy of the candidate user judgment. For example, when the target conditions include a second comparison result less than a second preset threshold, a first comparison result less than a first preset threshold, a third comparison result less than a third preset threshold, and a fourth comparison result where the first line of data is the same as the candidate line of data, if the second comparison result is less than the second preset weight, a weight of 0.4 is obtained; if the third comparison result is less than the third preset threshold, a weight of 0.1 is obtained; and when the total weight obtained is greater than 0.5, the user is determined to be a candidate user.

[0093] In step 303, the smart scale sorts and displays at least one candidate user according to the priority of the candidate users.

[0094] After identifying at least one candidate user, the smart scale can sort and display the candidate users according to their priority.

[0095] Specifically, the priority of candidate users can be determined based on at least one of the following: the preset priority of candidate users, the first comparison result, the second comparison result, the third comparison result, or the fourth comparison result. For example, the more target conditions a candidate user meets, the higher the degree of relevance, and thus the higher the priority; or, the higher the weight corresponding to the target conditions, the higher the degree of relevance, and thus the higher the priority of the candidate user. For example, candidate users who meet the first, second, and third comparison results have a higher priority than candidate users who only meet the first and second comparison results.

[0096] Alternatively, you can manually set the preset priority of the alternative users, for example, setting the priority of frequently used accounts to be higher than that of infrequently used accounts.

[0097] After prioritizing candidate users, the smart scale can display at least one candidate user's information on its built-in electronic display. Alternatively, in one possible implementation, the smart scale can also interact with other devices, sending data to those devices for display. For example, the smart scale can interact with smart wearable devices or smart mirrors to display data for at least one candidate user. Specifically, the smart wearable device or smart mirror establishes a communication connection with the smart scale via a wireless network or Bluetooth, and the smart scale then sends the candidate user's data to the smart wearable device or smart mirror in the form of data packets. After receiving the data packets, the smart wearable device or smart mirror displays the data in the data packets on its own display screen.

[0098] In step 304, the smart scale determines a second user from at least one candidate user based on the user's selection instruction.

[0099] After displaying at least one candidate user, the smart scale determines a second user from the candidate users based on a user selection instruction. The user selection instruction is used to select the second user.

[0100] In practical applications, the displays of smart scales, smart wearable devices, or smart mirrors may be limited in size (meaning the number of user identifiers that can be displayed is less than the total number of identifiers for at least one candidate user). Therefore, the displayed candidate users can be adjusted based on user switching commands. For example, if candidate user 1 and candidate user 2 are displayed, upon receiving a user switching command, the display can switch to candidate user 2 and candidate user 3 (equivalent to hiding the previous one and displaying the next) or switch to candidate user 3 and candidate user 4 (equivalent to page turning).

[0101] Specifically, in one possible implementation, the smart scale is equipped with an infrared sensor that can detect the body movements of the first user. These body movements can be gestures or movements of other body parts, which are not limited in this application. For example, the first user can switch between alternative users by waving their hand or foot. For instance, waving the hand to the left switches the user to the right, waving the foot to the right switches the user to the left, waving the hand upwards receives a negative command, and waving the hand downwards receives a confirmation command, and so on. The specific mapping relationship between body movements and user selection commands is not limited here.

[0102] In practical applications, a camera can be installed on the smart scale to select and confirm a second user. For example, the camera can recognize the current user's body movements, which can be gestures or movements of other body parts; this application does not limit this. For instance, the current user can wave their hands left and right, or make numerical gestures to select a potential user. Furthermore, the smart scale can also use other sensors to achieve the selection and switching of potential users. For example, a structured light vision sensor can be used to recognize three-dimensional gestures or movements to determine a second user; for example, turning the head to the left indicates switching users, and nodding indicates confirming a user. Alternatively, an accelerometer or gyroscope sensor can be used to select or confirm a second user; specific methods are not limited here.

[0103] In one possible implementation, the smart scale can also directly perform facial recognition on the current user through a camera, obtain facial recognition information, and compare the facial recognition information with the facial recognition information already saved among the candidate users to determine the second user.

[0104] In one possible implementation, when the smart scale is connected to a smart wearable device or smart mirror, it can also receive user selection commands through the smart wearable device or smart mirror. Specifically, the smart wearable device or smart mirror receives the user's selection commands through its own sensors or camera, and then forwards the user's selection commands to the smart scale.

[0105] In one possible implementation, the candidate users may also include guest users. For example, when the correlation between the second and first data among the candidate users is not high, i.e., the second and first data do not meet the target conditions, or when no user selection instruction is received within a certain period, a guest user can be identified as the second user, and this guest user is represented as a new user; or, if the user selection instruction is used to instruct the selection of a guest user, the guest user can also be identified as the second user. Alternatively, after at least one candidate user is obtained based on the target conditions, an additional guest user option can be added, and this guest user can be placed as the last option or the first option after the at least one candidate user is sorted. This application does not limit this.

[0106] In step 305, the smart scale associates and stores the first data with the second user.

[0107] After identifying the second user, the smart scale associates and stores the first data with the second user, so that after the smart scale establishes a connection with the terminal device corresponding to the second user, the first data can be stored in the terminal device corresponding to the second user.

[0108] In practical applications, smart scales can also adjust target conditions in real time based on the differences between the data of the second user and the data of the alternative user.

[0109] Specifically, by using data calculations and creating neural networks, a comparison can be made between the second user and the candidate user each time, and the weights in the target conditions can be adjusted based on the comparison results, which can improve the accuracy of judging the candidate user in the next round.

[0110] In this embodiment, step 303 is an optional step. When step 303 is not executed, the display screen of the smart scale can display candidate users randomly or without any pattern.

[0111] In this embodiment, after obtaining the first user's first data, at least one candidate user's identifier is determined based on the first data. The relationship between the candidate user's second data and the first data satisfies the target condition. Then, a second user is determined from the at least one candidate user through a user selection command. This eliminates the need to use the phone's function keys to determine the second user, thus improving the user experience of using the smart scale.

[0112] The data processing method in the embodiments of this application has been described above. The data processing apparatus in the embodiments of this application will now be described in detail. Please refer to... Figure 6 This is a schematic diagram of the data processing device provided in the embodiments of this application.

[0113] A data processing apparatus, comprising:

[0114] Acquisition unit 601 is used to acquire the first data of the currently detected first user;

[0115] Processing unit 602 is used to determine the identifier of at least one candidate user based on the first data, wherein the second data of the candidate user and the first data satisfy the target condition;

[0116] The receiving unit 603 is configured to receive a user selection instruction and determine the identifier of a second user from the identifiers of at least one candidate user according to the user selection instruction.

[0117] Storage unit 604 is used to associate and store the first data with the second user.

[0118] Optionally, the first data includes first anthropometric data; the second data of the candidate users includes second anthropometric data in the first parameter dimension and / or second anthropometric data in the second parameter dimension.

[0119] If the second data only includes the second human body data of the first parameter dimension, then the target condition is that the first comparison result is less than the first preset threshold.

[0120] If the second data only includes the second human body data of the second parameter dimension, then the target condition is: the target condition is that the second comparison result is less than the second preset threshold;

[0121] If the second data includes the second human body data of the first parameter dimension and the second human body data of the second parameter dimension, then the target condition is: the target condition is that the first comparison result is less than the first preset threshold and the second comparison result is less than the second preset threshold;

[0122] Wherein, the second human body data of the first parameter dimension is the second human body prediction data of the candidate user under the current trend; the second human body data of the second parameter dimension is the second human body prediction data obtained based on the historical measurement data of the candidate user; the first comparison result is the difference between the first human body measurement data and the second human body data of the first parameter dimension; the second comparison result is the difference between the first human body measurement data and the second human body data of the second parameter dimension.

[0123] Optionally, the first data also includes the first time of the current detection. The processing unit 602 is further used to determine the basic data and change values ​​of the candidate users. The change values ​​include at least one of a first change value, a second change value, and a third change value. The first change value is used to indicate the change value corresponding to the time period to which the first time belongs. The second change value is used to indicate the change value corresponding to the dietary records within a preset time period before the first time. The third change value is used to indicate the change value corresponding to the human body consumption within a preset time period before the first time.

[0124] Processing unit 602 is also used to determine the second human body data of the first parameter dimension of the candidate user based on the basic data and the change value.

[0125] Optionally, the second data of the candidate user also includes the second time corresponding to the basic data of the candidate user, and the target condition also includes: the third comparison result is less than the third preset threshold, and the third comparison result is the difference between the first time and the second time.

[0126] Optionally, the first data may also include first behavioral data, the second data for candidate users may also include candidate behavioral data, and the target conditions may also include:

[0127] The first row of data and the alternative row of data are the same fourth comparison result.

[0128] Optionally, the processing unit 602 is further configured to sort and display at least one candidate user according to the priority of the candidate user; the priority of the candidate user is determined according to at least one of the following: the preset priority of the candidate user, the first comparison result of the candidate user, the second comparison result, the third comparison result or the fourth comparison result.

[0129] Optionally, the processing unit 602 is also configured to adjust the target conditions based on the data of the second user and the data of the alternative user.

[0130] Optionally, receiving user selection instructions includes:

[0131] The user's selection command is determined by capturing the first user's body movements using sensors or cameras.

[0132] Alternatively, the user's selection command can be determined by collecting the first user's facial recognition information through a camera;

[0133] Alternatively, it can receive user selection instructions sent by smart wearable devices or smart mirrors;

[0134] Among them, user selection instructions include user confirmation instructions or user switching instructions.

[0135] In this embodiment, the methods performed by each unit in the electronic device are the same as those described above. Figure 3The method performed by the smart scale in the illustrated embodiment is similar, and will not be described in detail here.

[0136] Please see Figure 7 This is another structural schematic diagram of the electronic device provided in this application.

[0137] The electronic device includes a processor 701, a memory 702, a bus 705, and an interface 704. The processor 701 is connected to the memory 702 and the interface 704. The bus 705 connects the processor 701, the memory 702, and the interface 704. The interface 704 is used to receive or send data. The processor 701 is a single-core or multi-core central processing unit, a specific integrated circuit, or one or more integrated circuits configured to implement embodiments of the present invention. The memory 702 can be random access memory (RAM) or non-volatile memory, such as at least one hard disk drive. The memory 702 is used to store computer-executable instructions. Specifically, the computer-executable instructions may include a program 703.

[0138] In this embodiment, the processor 701 can execute the aforementioned... Figure 3 The specific operations performed by the smart scale in the illustrated embodiment will not be described in detail here.

[0139] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer, implements the method flow related to the smart scale in any of the above method embodiments.

[0140] It should be understood that the processor mentioned in the smart scale in the above embodiments of this application, or the processor provided in the above embodiments of this application, can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0141] It should also be understood that the number of processors in the smart scales in the above embodiments of this application can be one or more, and can be adjusted according to the actual application scenario. This is merely an illustrative example and is not intended to limit the scope. Similarly, the number of memory units in the embodiments of this application can be one or more, and can be adjusted according to the actual application scenario. This is merely an illustrative example and is not intended to limit the scope.

[0142] It should also be understood that the memory or readable storage medium mentioned in the smart scales described in the above embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0143] It should also be noted that when the smart scale includes a processor (or processing unit) and a memory, the processor in this application may be integrated with the memory, or the processor and the memory may be connected through an interface. This can be adjusted according to the actual application scenario and is not limited.

[0144] This application also provides a computer program or a computer program product including a computer program, which, when executed on a computer, will cause the computer to implement the method flow executed by the smart scale in any of the above method embodiments.

[0145] In the above Figure 3In the embodiments, it can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented entirely or partially in the form of a computer program product.

[0146] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).

[0147] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0149] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0151] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or other network device, etc.) to execute this application. Figures 2 to 6 The methods described in the various embodiments include all or part of the steps. The storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0152] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not explicitly listed or inherent to those processes, methods, products, or apparatuses.

[0153] The names of messages / frames / information, modules, or units provided in the embodiments of this application are merely examples, and other names may be used as long as the function of the messages / frames / information, modules, or units is the same.

[0154] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms "a," "the," and "the" used in the embodiments of this application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that in the description of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship; for example, A / B can represent A or B. "And / or" in this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural.

[0155] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrase “if determination” or “if detection (of the condition or event of the statement)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the condition or event of the statement)” or “in response to detection (of the condition or event of the statement).”

[0156] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A data processing method, characterized by, The method comprises: acquiring first data of a first user currently detected; determining an identity of at least one candidate user according to the first data, second data of the candidate user and the first data satisfying a target condition; receiving a user selection instruction and determining an identity of a second user from the at least one candidate user according to the user selection instruction; storing the first data in association with the second user; the first data comprises first anthropometric data; the second data of the candidate user comprises second anthropometric data of a first parameter dimension and / or second anthropometric data of a second parameter dimension; the second anthropometric data of the first parameter dimension is second anthropometric prediction data of the candidate user under a current trend; the second anthropometric data of the second parameter dimension comprises a mean value of all or anthropometric data of a time period of the candidate user that has been saved or comprises anthropometric data of the candidate user obtained at any previous measurement; the second anthropometric data of the first parameter dimension is dynamically predicted based on base data and a change value; if the second data only comprises the second anthropometric data of the first parameter dimension, the target condition is that a first comparison result is less than a first preset threshold; if the second data only comprises the second anthropometric data of the second parameter dimension, the target condition is that a second comparison result is less than a second preset threshold; if the second data comprises the second anthropometric data of the first parameter dimension and the second anthropometric data of the second parameter dimension, the target condition is that the first comparison result is less than the first preset threshold and the second comparison result is less than the second preset threshold; wherein the first comparison result is a difference between the first anthropometric data and the second anthropometric data of the first parameter dimension; and the second comparison result is a difference between the first anthropometric data and the second anthropometric data of the second parameter dimension; the first data further comprises a first time of current detection, and the method further comprises: determining base data and a change value of the candidate user, the change value comprising at least one of a first change value, a second change value and a third change value; wherein the first change value is used to indicate a change value corresponding to a time period to which the first time belongs; the second change value is used to indicate a change value corresponding to a diet record within a preset time period before the first time; and the third change value is used to indicate a change value corresponding to a body consumption within a preset time period before the first time; determining the second anthropometric data of the first parameter dimension of the candidate user according to the base data and the change value.

2. The method of claim 1, wherein, the second data of the candidate user further comprises a second time corresponding to the base data of the candidate user, and the target condition further comprises that a third comparison result is less than a third preset threshold, the third comparison result being a difference between the first time and the second time.

3. The method of any one of claims 1-2, wherein, the first data further comprises first behavior data, the second data of the candidate user further comprises candidate behavior data, and the target condition further comprises: The fourth comparison result of the first behavior data and the alternative behavior data.

4. The method of claim 3, wherein, The method further comprises: The priority of the alternative user is determined according to at least one of the following: the preset priority of the alternative user, the first comparison result, the second comparison result, the third comparison result or the fourth comparison result of the alternative user.

5. The method of any one of claims 1 to 2, wherein, The receiving user selection instruction comprises: The user selection instruction is determined by collecting the limb action of the first user through a sensor or a camera; Or, the user selection instruction is determined by collecting the face recognition information of the first user through a camera; Or, the user selection instruction sent by a smart wearable device or a smart mirror is received; The user selection instruction comprises a user confirmation instruction or a user switching instruction.

6. A data processing apparatus, characterized by, The method comprises: An acquisition unit is configured to acquire first data of a first user currently detected; A processing unit is configured to determine the identity of at least one alternative user according to the first data, wherein the second data of the alternative user and the first data satisfy a target condition; A receiving unit is configured to receive a user selection instruction and determine the identity of a second user from the identity of at least one alternative user according to the user selection instruction; A storage unit is configured to store the first data in association with the second user; The first data comprises first anthropometric data; the second data of the alternative user comprises second anthropometric data of a first parameter dimension and / or second anthropometric data of a second parameter dimension; the second anthropometric data of the first parameter dimension is second anthropometric prediction data of the alternative user under a current trend; the second anthropometric data of the second parameter dimension comprises the mean of all or a period of anthropometric data of the alternative user that has been saved, or comprises anthropometric data of the alternative user obtained at any previous measurement; the second anthropometric data of the first parameter dimension is dynamically predicted based on basic data and a change value; If the second data only comprises the second anthropometric data of the first parameter dimension, the target condition is that the first comparison result is less than a first preset threshold; If the second data only comprises the second anthropometric data of the second parameter dimension, the target condition is that the second comparison result is less than a second preset threshold; If the second data comprises the second anthropometric data of the first parameter dimension and the second anthropometric data of the second parameter dimension, the target condition is that the first comparison result is less than the first preset threshold and the second comparison result is less than the second preset threshold; The first comparison result is the difference between the first anthropometric data and the second anthropometric data of the first parameter dimension; the second comparison result is the difference between the first anthropometric data and the second anthropometric data of the second parameter dimension. The first data further comprises a first time of a current detection, and the processing unit is further configured to determine basic data and change values of the candidate user, the change values comprising at least one of a first change value, a second change value and a third change value; wherein the first change value is used to indicate a change value corresponding to a time period to which the first time belongs; the second change value is used to indicate a change value corresponding to a diet record situation within a preset time period before the first time; and the third change value is used to indicate a change value corresponding to a human body consumption situation within the preset time period before the first time; The processing unit is further configured to determine second human body data of the candidate user in the first parameter dimension according to the basic data and the change values.

7. An electronic device, comprising: Comprise: a processor, a memory; the processor is connected with the memory; the processor executes the method as claimed in any one of claims 1 to 5.

8. A computer storage medium, the computer storage medium storing instructions, the instructions causing a computer to execute the method as claimed in any one of claims 1 to 5 when executed on the computer.

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