Data Processing Method and Apparatus, and Fitness Robot

Abstract

A data processing method, apparatus, and a fitness robot relate to the artificial intelligent field, where the data processing method includes calculating first energy consumption of a user in a preset time interval based on exercise data of the user, obtaining a first body weight change of the user in the preset time interval, predicting, based on the first energy consumption and the first body weight change, second energy consumption and a second body weight change of the user in a future preset time interval, obtaining, based on the first energy consumption, the first body weight change, the second energy consumption, and the second body weight change, a result indicating the user capability of completing an intended fitness plan, and correcting specified energy consumption of the user and a specified body weight change of the user in the preset time interval in the intended fitness plan based on the result.

What is claimed is: 
     
         1 . A data processing method, comprising:
 calculating first energy consumption of a user in a preset time interval based on exercise data of the user;   obtaining a first body weight change of the user in the preset time interval;   predicting, based on the first energy consumption and the first body weight change, second energy consumption and a second body weight change of the user in a future preset time interval;   obtaining, based on the first energy consumption, the first body weight change, the second energy consumption, and the second body weight change, a result indicating the user capability of completing an intended fitness plan; and   correcting a specified energy consumption of the user and a specified body weight change of the user in the preset time interval in the intended fitness plan based on the result.   
     
     
         2 . The data processing method of  claim 1 , wherein predicting the second energy consumption and the second body weight change comprises predicting, using a least square method based on the first energy consumption and the first body weight change, the second energy consumption and the second body weight change. 
     
     
         3 . The data processing method of  claim 1 , wherein predicting the second energy consumption and the second body weight change comprises:
 predicting the second energy consumption using a formula of Kt=w1×Kt−1+w2×Kt−2+w3×Kt−3+ . . . +wn×Kt−n, wherein Kt is second energy consumption of the user in a t th  preset time interval, wherein n is a quantity of preset time intervals in which the user has exercised, wherein Kt−n is first energy consumption of the user in a (t−n) th  preset time interval, wherein wn is a weight of the first energy consumption of the user in the (t−n) th  preset time interval, wherein w1 is a weight of first energy consumption of the user in a (t−l) st  preset time interval, and wherein w1+w2+ . . . +wn=one; and   calculating, using the second energy consumption predicted using the formula of Kt and the first body weight change, a second body weight change corresponding to the second energy consumption predicted using the formula of Kt.   
     
     
         4 . The data processing method of  claim 1 , further comprising:
 recognizing an exercise movement of the user based on the exercise data;   comparing the exercise movement with a preset movement; and   generating a movement correction instruction to correct the exercise movement when the exercise movement does not match the preset movement.   
     
     
         5 . The data processing method of  claim 4 , wherein the exercise data comprises an amplitude of the exercise movement, and wherein comparing the exercise movement and generating the movement correction instruction comprises:
 comparing the amplitude of the exercise movement with an amplitude of the preset movement; and   generating a movement correction guide instruction to correct the exercise movement when the amplitude of the exercise movement exceeds a specified amplitude range of the preset movement.   
     
     
         6 . The data processing method of  claim 5 , wherein the exercise data further comprises sign data of the user, and wherein before generating the movement correction guide instruction, the data processing method further comprises:
 obtaining characteristic sign data of the user based on the sign data of the user, wherein the characteristic sign data comprises shoulder position data and hip position data of the user; and   locating a user plane based on the characteristic sign data.   
     
     
         7 . The data processing method of  claim 4 , wherein the exercise data comprises a frequency of the exercise movement, and wherein comparing the exercise movement and generating the movement correction instruction comprises:
 comparing the frequency of the exercise movement with a specified frequency range of the preset movement; and   generating a movement correction instruction comprising a movement correction reminder message to correct the exercise movement when the frequency of the exercise movement falls outside the specified frequency range of the preset movement.   
     
     
         8 . The data processing method of  claim 1 , further comprising:
 determining, based on the exercise data of the user, that the user fails to complete the specified energy consumption of the user in the preset time interval and is in an idle state; and   punishing the user according to a predetermined rule.   
     
     
         9 . The data processing method of  claim 1 , further comprising:
 determining, based on the exercise data of the user, that the user has completed the specified energy consumption of the user in the preset time interval;   obtaining image data of the user in different periods from the exercise data of the user; and   sending a reminder message reminding the user to forward the image data to a social networking platform.   
     
     
         10 . A data processing apparatus, comprising:
 a memory configured to store instructions; and   a processor coupled to the memory, wherein the instructions cause the processor to be configured to:
 calculate first energy consumption of a user in a preset time interval based on exercise data of the user; 
 obtain a first body weight change of the user in the preset time interval; 
 predict, based on the first energy consumption and the first body weight change, second energy consumption and a second body weight change of the user in a future preset time interval; 
 obtain, based on the first energy consumption, the first body weight change, the second energy consumption, and the second body weight change, a result indicating the user capability of completing an intended fitness plan; and 
 correct specified energy consumption of the user and a specified body weight change of the user in the preset time interval in the intended fitness plan based on the result. 
   
     
     
         11 . The data processing apparatus of  claim 10 , wherein the instructions further cause the processor to be configured to predict, using a least square method based on the first energy consumption and the first body weight change, the second energy consumption and the second body weight change. 
     
     
         12 . The data processing apparatus of  claim 10 , wherein the instructions further cause the processor to be configured to:
 predict the second energy consumption using a formula of Kt=w1×Kt−1+w2×Kt−2+w3×Kt−3+ . . . +wn×Kt−n, wherein Kt is second energy consumption of the user in a t th  preset time interval, wherein n is a quantity of preset time intervals in which the user has exercised, wherein Kt−n is first energy consumption of the user in a (t−n) th  preset time interval, wherein wn is a weight of the energy consumption of the user in the (t−n) th  preset time interval, and wherein w1+w2+ . . . +wn=one; and   calculate, using the second energy consumption predicted using the formula Kt and the first body weight change, a second body weight change corresponding to the second energy consumption predicted using the formula Kt.   
     
     
         13 . The data processing apparatus of  claim 10 , wherein the instructions further cause the processor to be configured to:
 recognize an exercise movement of the user based on the exercise data;   compare the exercise movement with a preset movement; and   generate a movement correction instruction to correct the exercise movement when the exercise movement does not match the preset movement.   
     
     
         14 . The data processing apparatus of  claim 13 , wherein the exercise data comprises an amplitude of the exercise movement, and wherein the instructions further cause the processor to be configured to:
 compare the amplitude of the exercise movement with an amplitude of the preset movement; and   generate a movement correction guide instruction to correct the exercise movement when the amplitude of the exercise movement exceeds a specified amplitude range of the preset movement.   
     
     
         15 . The data processing apparatus of  claim 14 , wherein the exercise data further comprises sign data of the user, and wherein the instructions further cause the processor to be configured to:
 obtain characteristic sign data of the user based on the sign data, wherein the characteristic sign data comprises shoulder position data and hip position data of the user; and   locate a user plane based on the characteristic sign data.   
     
     
         16 . The data processing apparatus of  claim 13 , wherein the exercise data comprises a frequency of the exercise movement, and wherein the instructions further cause the processor to be configured to:
 compare the frequency of the exercise movement with a specified frequency range of the preset movement; and   generate a movement correction instruction comprising a movement correction reminder message to correct the exercise movement when the frequency of the exercise movement falls outside the specified frequency range of the preset movement.   
     
     
         17 . The data processing apparatus of  claim 10 , wherein the instructions further cause the processor to be configured to:
 determine, based on the exercise data of the user, that the user fails to complete the specified energy consumption of the user in the preset time interval and is in an idle state; and   punish the user according to a predetermined rule.   
     
     
         18 . The data processing apparatus of  claim 10 , wherein the instructions further cause the processor to be configured to:
 determine, based on the exercise data of the user, that the user has completed the specified energy consumption of the user in the preset time interval;   obtain image data of the user in different periods from the exercise data; and   send a reminder message reminding the user to forward the image data to a social networking platform.   
     
     
         19 . A fitness robot, comprising a data processing apparatus, wherein the data processing apparatus comprises:
 a memory configured to store instructions; and   a processor coupled to the memory, wherein the instructions cause the processor to be configured to:
 calculate first energy consumption of a user in a preset time interval based on exercise data of the user; 
 obtain a first body weight change of the user in the preset time interval; 
 predict, based on the first energy consumption and the first body weight change, second energy consumption and a second body weight change of the user in a future preset time interval; 
 obtain, based on the first energy consumption, the first body weight change, the second energy consumption, and the second body weight change, a result indicating the user capability of completing an intended fitness plan; and 
 correct specified energy consumption of the user and a specified body weight change of the user in the preset time interval in the intended fitness plan based on the result. 
   
     
     
         20 . The fitness robot of  claim 19 , further comprising:
 an input apparatus coupled to the data processing apparatus and configured to:
 obtain the exercise data; and 
 send the exercise data to the data processing apparatus; and 
   an execution apparatus coupled to the data processing apparatus and configured to:
 receive a movement correction instruction from the data processing apparatus; and 
 execute the movement correction instruction to correct a movement of the user.