CN116168800B - Fat reducing optimization system based on optimal carbon technology - Google Patents

Fat reducing optimization system based on optimal carbon technology Download PDF

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CN116168800B
CN116168800B CN202310425290.3A CN202310425290A CN116168800B CN 116168800 B CN116168800 B CN 116168800B CN 202310425290 A CN202310425290 A CN 202310425290A CN 116168800 B CN116168800 B CN 116168800B
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user
strategy
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motion
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CN116168800A (en
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逯明福
张秀平
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Beijing Doctor Lu Behavioral Medicine Science And Technology Research Institute Co ltd
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Beijing Doctor Lu Behavioral Medicine Science And Technology Research Institute Co ltd
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/84—Greenhouse gas [GHG] management systems

Abstract

The invention relates to the technical field of medical care scientific analysis, in particular to a fat reduction optimization system based on a carbon optimization technology, which comprises the following steps: the motion sensing modules are used for monitoring the motion of a user and generating corresponding user motion data; the analysis module is used for analyzing the user data in a preset analysis mode; the strategy matching module is used for recommending the optimal carbon strategy according to the analysis data; the feedback module is used for adjusting each optimal carbon strategy in a preset adjustment mode through each analysis data so as to form an adjusted optimal carbon strategy; according to the invention, by setting the modules, optimal carbon strategy matching is performed according to the motion process of the user, and the matched user is adjusted, so that the accuracy of motion suggestion is effectively improved while the detection of the motion consumption of the user is effectively improved.

Description

Fat reducing optimization system based on optimal carbon technology
Technical Field
The invention relates to the technical field of medical care scientific analysis, in particular to a fat reduction optimization system based on a carbon optimization technology.
Background
The carbon optimization technology is used as a low-carbon medical technology, and compared with traditional medicine, the low-carbon medicine has great auxiliary advantages in the aspect of health management.
Chinese patent grant bulletin number: CN114708941B discloses a health data-based management method and device, and the application sends at least one piece of raw health data of the obtained user to a data processing system through an input party; the data processing system optimizes the original health data to obtain the processed health data corresponding to the user and sends the processed health data to the data management platform; the data management platform carries out data modeling on the processed health data to obtain a star model of logic data of a user and carries out data classification on the data in the star model to obtain health data of different levels corresponding to the user, configures different data application management and control strategies for the health data of different levels, and outputs the corresponding health data of different levels to a data application side based on the different data application management and control strategies, so that the safety, usability and use convenience and use efficiency of the health data are improved.
It can be seen that the above technical solution has the following problems: an effective metering cannot be provided for the course of motion.
Disclosure of Invention
Therefore, the invention provides a fat reduction optimization system based on a carbon optimization technology, which is used for solving the problem that the prior art cannot provide effective metering for a motion process, so that the motion suggestion is inaccurate.
In order to achieve the above object, the present invention provides an online collective provisioning system based on a big data cloud platform, comprising:
the motion sensing modules are used for sensing the motion of a user and generating corresponding user motion data;
the analysis module is connected with the motion sensing module and used for analyzing the user data in a preset analysis mode and forming corresponding first analysis data and second analysis data;
the strategy matching module is connected with the analysis module and each motion sensing module, and is used for recommending the optimal carbon strategy according to the analysis data and transmitting each recommended optimal carbon strategy to the corresponding motion sensing module;
the feedback module is connected with the analysis module and the strategy matching module and is used for adjusting each optimal carbon strategy in a preset adjustment mode through each analysis data so as to form an adjusted optimal carbon strategy;
wherein the user movement data is movement trend and movement consumption of the user,
the preset analysis mode is an energy consumption model corresponding to the energy consumption of each user according to the motion data of each user, the first analysis data is the energy consumption model output by the analysis module under the trend judgment condition, the second analysis data is the energy consumption model output by the analysis module under the trend adjustment condition, the optimal carbon strategy is a motion mode of recommending the user according to the first analysis data and the preset optimal carbon data of the corresponding user, and the preset adjustment mode is to adjust the preset optimal carbon data of the corresponding user according to the second analysis data;
the preset optimal carbon data is an energy consumption model preset by the strategy matching module according to the user attributes of the users, the trend judging condition is that the strategy matching module does not conduct optimal carbon strategy recommendation on the users, and the trend adjusting condition is that the strategy matching module completes optimal carbon strategy recommendation on the users;
wherein the user attributes are weight data and height data of the user.
Further, the single motion sensing module includes:
the potential energy detection units are used for detecting the pressure generated by the corresponding user in the movement process;
the vector detection units are used for detecting trends of the motion states of the corresponding users;
the display processing unit is respectively connected with the corresponding potential energy detection units and the corresponding vector detection units and is connected with the strategy matching module to record and display information;
wherein the trend is the direction of center of gravity shift of the user completing a single movement process.
Further, the potential energy detection unit is mounted on a pressure-receiving surface which is contacted with the ground in the motion of each user;
for a single user, the single user corresponds to at least one potential energy detection unit, the single motion process completed by the single user corresponds to the single potential energy detection unit, and the motion sensing module records the corresponding pressure change in the motion process as potential energy data of the potential energy detection unit.
Further, the vector detection unit is mounted on the center of gravity of each user, and for a single vector detection unit, corresponds to the single user, and detects the user center of gravity shift direction of the user in each single movement process;
wherein the gravity center is any position which accords with the geometric center of motion balance on the trunk of the user; and the motion sensing module records the gravity center offset direction of the user corresponding to the motion process as vector data of the user.
Further, the analysis module collects potential energy data and vector data recorded by the motion sensing modules, and for the single user, the analysis module analyzes the potential energy data and the vector data of the user in a single preset period to form the first analysis data;
wherein the single preset period is a preset time interval in the analysis module, and at least comprises a preset minimum number of potential energy data and vector data;
wherein the preset minimum number is related to the user attribute.
Further, a preset adjustment value and a preset target value are arranged in the strategy matching module, the strategy matching module performs strategy matching on the first analysis data of the single user in a strategy matching state, and adjusts the first analysis data according to the preset adjustment value so that the first analysis data approaches to the preset target value under the adjustment of the preset adjustment value;
wherein, the preset adjustment value is related to the user attribute of the single user, and the preset target value is a set value in the policy matching module and is related to the average value of all the user attributes;
the policy matching state forms the first analysis data for the analysis module.
Further, the strategy matching module outputs the preset adjustment value to each motion sensing module of the single user under the strategy output condition, and prompts the user to perform motion adjustment;
the policy output condition is that the policy matching module completes the policy matching for the single user.
Further, the motion sensing module analyzes each piece of adjustment potential energy data and each piece of adjustment vector data of a corresponding user in a single preset period under the strategy output condition to form second analysis data;
the adjustment potential energy data are the potential energy data measured by the potential energy detection unit under the strategy output condition, and the adjustment vector data are the vector data measured by the vector detection unit under the strategy output condition.
Further, the feedback module compares the second analysis data with the preset target value under the strategy output condition, and adjusts the preset target value in a preset target adjustment mode according to the comparison result;
the preset target adjustment mode is to adjust the preset target value to be smaller according to the ratio of the first analysis data to the second analysis data.
Further, for a single motion sensing module, a target pressure corresponding to the single user is set, the maximum continuous times are set in the motion sensing module, and if the maximum pressure of the user in the single motion process of the maximum continuous times is the target pressure, the motion sensing module judges that the user finishes the optimization of fat reduction.
Compared with the prior art, the method has the beneficial effects that by means of arranging the plurality of motion sensing modules, the analysis module, the strategy matching module and the feedback module, optimal carbon strategy matching is carried out according to the motion process of the user, and the matched user is adjusted, so that the accuracy of motion suggestion is effectively improved while the detection of the motion consumption of the user is effectively improved.
Further, the motion consumption of the user is judged by using the pressure detection and the motion trend detection of the motion process, and the accuracy of the motion suggestion is further improved while the accuracy of the detection of the motion consumption of the user is effectively improved.
Furthermore, by means of continuously correcting the motion detection of the user, the motion behavior of the user is suggested and adjusted, so that the accuracy of monitoring the fat-reducing effect of the user is effectively improved, and meanwhile, the accuracy of the motion suggestion is further improved.
Further, the optimal carbon strategy for the user is dynamically corrected, the target value of the optimal carbon strategy is continuously adjusted, and the accuracy of the sport suggestion is further improved while the step adjustment of the fat reducing effect of the user is effectively improved.
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FIG. 1 is a schematic diagram of the connection of a fat reduction optimization system based on the optimal carbon technology of the present invention;
fig. 2 is a schematic connection diagram of a detection module according to an embodiment of the invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, which is a schematic connection diagram of a fat reduction optimization system based on a carbon optimization technology according to the present invention, the fat reduction optimization system based on the carbon optimization technology includes:
the motion sensing modules are used for sensing the motion of a user and generating corresponding user motion data;
the analysis module is connected with the motion sensing module and used for analyzing the user data in a preset analysis mode and forming corresponding first analysis data and second analysis data;
the strategy matching module is connected with the analysis module and each motion sensing module, and is used for recommending the optimal carbon strategy according to the analysis data and transmitting the recommended optimal carbon strategy to the corresponding motion sensing module;
the feedback module is connected with the analysis module and the strategy matching module and is used for adjusting each optimal carbon strategy in a preset adjustment mode through each analysis data so as to form an adjusted optimal carbon strategy;
wherein, the user movement data is the movement trend and the movement consumption of the user,
the preset analysis mode is to analyze the corresponding energy consumption model of the energy consumed by each user according to the motion data of each user, the first analysis data is the energy consumption model output by the analysis module under the trend judgment condition, the second analysis data is the energy consumption model output by the analysis module under the trend adjustment condition, the optimal carbon strategy is to recommend the motion mode of the user according to the first analysis data and the preset optimal carbon data of the corresponding user, and the preset adjustment mode is to adjust the preset optimal carbon data of the corresponding user according to the second analysis data;
the method comprises the steps that preset optimal carbon data is an energy consumption model preset by a policy matching module according to user attributes of all users, trend judging conditions are that the policy matching module does not conduct optimal carbon policy recommendation on the users, and trend adjusting conditions are that the policy matching module completes optimal carbon policy recommendation on the users;
wherein the user attributes are weight data and height data of the user.
According to the invention, by means of arranging the motion sensing modules, the analysis modules, the strategy matching modules and the feedback modules, optimal carbon strategy matching is performed according to the motion process of the user, and the matched user is adjusted, so that the accuracy of motion suggestion is effectively improved while the detection of the motion consumption of the user is effectively improved.
Fig. 2 is a schematic connection diagram of a detection module according to an embodiment of the invention, where a single motion sensing module includes:
the potential energy detection units are used for detecting the pressure generated by the corresponding user in the movement process;
the vector detection units are used for detecting trends of the motion states of the corresponding users;
the display processing unit is respectively connected with the corresponding potential energy detection units and the corresponding vector detection units and is connected with the strategy matching module for recording and displaying information;
wherein the trend is the direction of center of gravity shift of the user completing a single movement process.
The motion consumption of the user is judged by using the pressure detection and the motion trend detection of the motion process, and the accuracy of the detection of the motion consumption of the user is effectively improved, and meanwhile, the accuracy of the motion suggestion is further improved.
Specifically, the potential energy detection unit is mounted on a pressure-receiving surface which is contacted with the ground in the motion of each user;
for a single user, the single user corresponds to at least one potential energy detection unit, a single motion process completed by the single user corresponds to the single potential energy detection unit, and the motion sensing module records the corresponding pressure change in the motion process as potential energy data of the potential energy detection unit.
For the ith user, the corresponding jth potential energy detecting unit detects a pressure of Lji, and the corresponding pressure change curve of time is F (Lji), wherein i=1, 2,3, …, n, j=1, 2,3, …, m, n is the maximum number of users, m is the number of potential energy detecting units of a single user, and for a single movement, the pressure change curve F (Lji) contains two maximum values, and the interval time t is the movement time of the single movement.
Specifically, the vector detection unit is mounted on the center of gravity of each user, and for a single vector detection unit, it corresponds to a single user, and it detects the user center of gravity shift direction of the user during each single movement;
wherein, the gravity center is any position which accords with the geometric center of motion balance on the trunk of the user; the motion sensing module records the gravity center offset direction of the corresponding user in the motion process as vector data of the user.
For the ith user, the decomposed vertical direction of the gravity center offset direction of the motion is Hi, the gravity center offset vertical direction change curve corresponding to the time is F (Hi), wherein the upward direction is a negative direction, and the vertical direction is a direction vertical to the horizontal plane.
Specifically, the analysis module collects potential energy data and vector data recorded by each motion sensing module, and for a single user, the analysis module analyzes the potential energy data and the vector data of the user in a single preset period to form first analysis data;
the single preset period is a preset time interval in the analysis module, and at least comprises potential energy data and vector data with a preset minimum number;
wherein the preset minimum number is related to the user attribute.
For a user with a weight Gi and a height Ti, setting the start time of his single movement to 0, the energy Ei (0-t) it consumes in this single movement is determined by formula (1):
（1），
specifically, a preset adjustment value and a preset target value are arranged in the strategy matching module, the strategy matching module performs strategy matching on first analysis data of a single user in a strategy matching state, and adjusts the first analysis data according to the preset adjustment value so that the first analysis data approaches to the preset target value under the adjustment of the preset adjustment value;
the preset adjustment value is related to the user attribute of the single user, and the preset target value is a set value in the strategy matching module and is related to the average value of the user attributes;
the policy matching state forms first analysis data for the analysis module.
Specifically, under the condition of outputting the strategy, the strategy matching module outputs a preset adjustment value to each motion sensing module of a single user and prompts the user to perform motion adjustment;
the policy output condition is that the policy matching module completes policy matching for a single user.
The method has the advantages that the user's movement behavior is suggested and regulated in a continuously-corrected mode for the user's movement detection, and the accuracy of the movement suggestion is further improved while the accuracy of the prediction of the fat-reducing effect is effectively improved by monitoring the user's energy consumption data.
Taking an hour as an example, for the ith user, performing several single movements in one hour, the total consumed energy is Ei (1 h), the target consumption value is E0 (1 h), and the difference between the target consumption value and the actual consumption value is，The strategy matching module is provided with a first preset consumption difference Ealpha and a second preset consumption difference Ebeta, wherein Ealpha is more than 0 and less than Ebeta, and the strategy matching module is used for matching->Comparing with Eα and Eβ to determine a matching carbon optimization strategy,
if it isThe strategy matching module judges that optimal carbon strategy matching is not carried out, and sends a target value to a motion sensing module of an ith user;
if it isThe strategy matching module performs conventional optimal carbon strategy matching and transmits a first optimal carbon strategy to the motion sensing module of the ith user;
if it isPolicy matching modulePerforming decrement optimal carbon strategy matching, and transmitting a second optimal carbon strategy to a motion sensing module of the ith user;
wherein the first preset consumption difference Eα is a preset error value related to the weight of the ith user, and the second preset consumption difference Eβ is a maximum consumption threshold value related to the weight of the ith user;
the first optimal carbon strategy is a linear strategy for increasing the motion quantity to a preset value according to the following conditionsDetermining by increasing the movement rate or lifting the movement height difference;
the second optimal carbon strategy is a stage strategy, the motion quantity is subjected to stage decomposition in a manner of taking a second preset consumption difference Ebeta as a target consumption value, and secondary calculation is performed.
Specifically, the motion sensing module analyzes each piece of adjustment potential energy data and each piece of adjustment vector data of a corresponding user in a single preset period under a strategy output condition to form second analysis data;
the potential energy data are measured by the potential energy detection unit under the strategy output condition, and the vector data are measured by the vector detection unit under the strategy output condition.
Specifically, the feedback module compares the second analysis data with a preset target value under the strategy output condition, and adjusts the preset target value in a preset target adjustment mode according to the comparison result;
the preset target adjustment mode is to reduce the preset target value according to the ratio of the first analysis data to the second analysis data.
The optimal carbon strategy for the user is dynamically corrected, the target value of the optimal carbon strategy is continuously adjusted, and the accuracy of the exercise advice is further improved while the step adjustment of the fat reducing effect of the user is effectively improved.
Specifically, for a single motion sensing module, a target pressure corresponding to a single user is set, the maximum continuous times are set in the motion sensing module, and if the maximum pressure of the user in a single motion process of the maximum continuous times is the target pressure, the motion sensing module judges that the user finishes the fat reduction optimization.
Example 1: the embodiment provides a fat reduction optimization system based on a carbon optimization technology, wherein:
the motion sensing module comprises a vector detection unit which is arranged below an insole capable of putting on and taking off shoes, and is fixed on a waistband buckle in a hanging mode, and the display processing unit is arranged in a watch mode.
Example 2: the present embodiment provides a fat reduction optimization system based on a carbon optimization technique, which is different from embodiment 1 in that:
the motion sensing module comprises a vector detection unit which is arranged on the sole of a foot and fixed on the abdomen in a pasting mode, and the display processing unit is arranged in a screen mode.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A fat reduction optimization system based on a carbon optimization technique, comprising:
the motion sensing modules are used for sensing the motion of a user and generating corresponding user motion data;
the analysis module is connected with the motion sensing module and used for analyzing the user data in a preset analysis mode and forming corresponding first analysis data and second analysis data;
the strategy matching module is connected with the analysis module and each motion sensing module, and is used for recommending the optimal carbon strategy according to the analysis data and transmitting each recommended optimal carbon strategy to the corresponding motion sensing module;
the feedback module is connected with the analysis module and the strategy matching module and is used for adjusting each optimal carbon strategy in a preset adjustment mode through each analysis data so as to form an adjusted optimal carbon strategy;
wherein the user movement data is movement trend and movement consumption of the user,
the preset analysis mode is an energy consumption model corresponding to the energy consumption of each user according to the motion data of each user, the first analysis data is the energy consumption model output by the analysis module under the trend judgment condition, the second analysis data is the energy consumption model output by the analysis module under the trend adjustment condition, the optimal carbon strategy is a motion mode of recommending the user according to the first analysis data and the preset optimal carbon data of the corresponding user, and the preset adjustment mode is to adjust the preset optimal carbon data of the corresponding user according to the second analysis data;
the preset optimal carbon data is an energy consumption model preset by the strategy matching module according to the user attributes of the users, the trend judging condition is that the strategy matching module does not conduct optimal carbon strategy recommendation on the users, and the trend adjusting condition is that the strategy matching module completes optimal carbon strategy recommendation on the users;
wherein the user attribute is weight data and height data of the user;
the single motion sensing module includes:
the potential energy detection units are used for detecting the pressure generated by the corresponding user in the movement process;
the vector detection units are used for detecting trends of the motion states of the corresponding users;
the display processing unit is respectively connected with the corresponding potential energy detection units and the corresponding vector detection units and is connected with the strategy matching module to record and display information;
wherein the trend is the direction of gravity center deviation of the user completing the single movement process;
the potential energy detection unit is mounted on a pressure-bearing surface which is contacted with the ground in the motion of each user;
for a single user, the single user corresponds to at least one potential energy detection unit, the single motion process completed by the single user corresponds to the single potential energy detection unit, and the motion sensing module records the corresponding pressure change in the motion process as potential energy data of the potential energy detection unit;
for the ith user, the corresponding jth potential energy detecting unit detects a pressure of Lji, and the corresponding time pressure change curve of F (Lji) is F, wherein i=1, 2,3, …, n, j=1, 2,3, …, m, n is the maximum number of users, m is the number of potential energy detecting units of a single user, and for a single movement, the pressure change curve F (Lji) contains two maximum values, and the interval time t is the movement time of the single movement;
for the ith user, the decomposition vertical direction of the gravity center offset direction of the motion is Hi, the gravity center offset vertical direction change curve of the corresponding time is F (Hi), wherein the upward direction is a negative direction, and the vertical direction is a direction vertical to a horizontal plane;
for a user with a weight Gi and a height Ti, setting the start time of his single movement to 0, the energy Ei (0-t) it consumes in this single movement is determined by formula (1):
（1）
for the ith user, performing several single movements in one hour, and setting the total consumed energy as Ei (1 h), the target consumed value as E0 (1 h), and the difference between the target consumed value and the actual consumed value as，The strategy matching module is provided with a first preset consumption difference Ealpha and a second preset consumption difference Ebeta, wherein Ealpha is more than 0 and less than Ebeta, and the strategy matching module is used for matching->Comparing with Eα and Eβ to determine a matching carbon optimization strategy,
if it isThe strategy matching module judges that optimal carbon strategy matching is not carried out, and sends a target value to a motion sensing module of an ith user;
if it isThe strategy matching module performs conventional optimal carbon strategy matching and transmits a first optimal carbon strategy to the motion sensing module of the ith user;
if it isThe strategy matching module performs decrement optimal carbon strategy matching and transmits a second optimal carbon strategy to the motion sensing module of the ith user;
wherein the first preset consumption difference Eα is a preset error value related to the weight of the ith user, and the second preset consumption difference Eβ is a maximum consumption threshold value related to the weight of the ith user;
the first optimal carbon strategy is a linear strategy for increasing the motion quantity to a preset value according toDetermining by increasing the movement rate or lifting the movement height difference;
and the second optimal carbon strategy is a stage strategy, the motion quantity is decomposed in a stage manner, and the decomposition mode is to take the second preset consumption difference Ebeta as the target consumption value to perform secondary calculation.
2. The optimal carbon technology-based fat reduction system according to claim 1, wherein the vector detection unit is mounted on the center of gravity of each user, and for a single vector detection unit, it corresponds to the single user, and it detects the user center of gravity shift direction of the user during each single movement;
wherein the gravity center is any position which accords with the geometric center of motion balance on the trunk of the user; and the motion sensing module records the gravity center offset direction of the user corresponding to the motion process as vector data of the user.
3. The optimal carbon technology-based fat reduction system according to claim 2, wherein the analysis module collects potential energy data and vector data recorded by the motion sensing modules, and for the single user, the analysis module analyzes the potential energy data and the vector data of the user in a single preset period to form the first analysis data;
wherein the single preset period is a preset time interval in the analysis module, and at least comprises a preset minimum number of potential energy data and vector data;
wherein the preset minimum number is related to the user attribute.
4. The optimal carbon technology-based fat reduction optimization system according to claim 3, wherein a preset adjustment value and a preset target value are set in the strategy matching module, the strategy matching module performs strategy matching on the first analysis data of the single user in a strategy matching state, and adjusts the first analysis data according to the preset adjustment value so that the first analysis data approaches to the preset target value under the adjustment of the preset adjustment value;
wherein, the preset adjustment value is related to the user attribute of the single user, and the preset target value is a set value in the policy matching module and is related to the average value of all the user attributes;
the policy matching state forms the first analysis data for the analysis module.
5. The optimal carbon technology-based fat reduction optimization system according to claim 4, wherein the policy matching module outputs the preset adjustment value to each motion sensing module of the single user under a policy output condition, and prompts the user to perform motion adjustment;
the policy output condition is that the policy matching module completes the policy matching for the single user.
6. The optimal carbon technology-based fat reduction optimization system according to claim 5, wherein the motion sensing module analyzes each adjustment potential energy data and each adjustment vector data of a corresponding user in a single preset period under the strategy output condition to form the second analysis data;
the adjustment potential energy data are the potential energy data measured by the potential energy detection unit under the strategy output condition, and the adjustment vector data are the vector data measured by the vector detection unit under the strategy output condition.
7. The optimal carbon technology-based fat reduction system according to claim 6, wherein the feedback module compares the second analysis data with the preset target value under the strategy output condition, and adjusts the preset target value in a preset target adjustment manner according to the comparison result;
the preset target adjustment mode is to adjust the preset target value to be smaller according to the ratio of the first analysis data to the second analysis data.
8. The optimal carbon technology-based fat reduction system according to claim 7, wherein for a single motion sensing module, a target pressure corresponding to the single user is set, a maximum continuous number of times is set in the motion sensing module, and if the maximum pressure of the single motion process of the user in the maximum continuous number of times is the target pressure, the motion sensing module determines that the user completes fat reduction optimization.
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