CN111403031A - Physique test data acquisition and detection system and method - Google Patents
Physique test data acquisition and detection system and method Download PDFInfo
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Abstract
The invention belongs to the technical field of physical fitness test, and discloses a physical fitness test data acquisition and detection system and method, wherein the physical fitness test data acquisition and detection method comprises the following steps: collecting physical testing data information, and processing the collected physical testing data; performing constitution evaluation and health condition evaluation according to the acquired constitution data, and performing early warning notification on abnormal constitution data; and generating and printing a constitution report. The invention can realize accurate evaluation of personal health information, thereby achieving the purpose of providing related genetic consultation suggestions for users; the method comprises the steps of carrying out contrastive analysis on body measurement data of monitoring personnel and body measurement data of previous cycle years, and carrying out contrastive analysis on the body measurement data of the monitoring personnel and body measurement data of other monitoring personnel of the same age and the same sex, and calculating historical comparison singularity and sample comparison singularity, and aims to judge the rationality of the body measurement data and generate early warning information on unreasonable body measurement data.
Description
Technical Field
The invention belongs to the technical field of physical fitness test, and particularly relates to a physical fitness test data acquisition and detection system and method.
Background
Constitutions are the health conditions of the human body and the adaptability to the outside. It is a comprehensive and relatively stable characteristic of morphological structure, physiological function and psychological factors of the human body expressed on the basis of heredity and acquirement. Heredity is the basis of the formation of constitutions, provides possibility for the development of constitutions, and is restricted by internal and external environments (nature and society), which indicates that the formation and development process of constitutions are largely related to acquired environment. Meanwhile, the chronic diseases in the current society are high, and many people have sub-health problems including many students at school. In fact, everyone has a need to perform physical examination tests regularly. However, the existing physical fitness test data acquisition and detection systems are inaccurate in health assessment; meanwhile, the health abnormality cannot be warned in time.
In summary, the problems of the prior art are as follows: the existing physique test data acquisition and detection system is inaccurate in health assessment; meanwhile, the health abnormality cannot be warned in time.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a physical fitness test data acquisition and detection system and a physical fitness test data acquisition and detection method.
The invention is realized in such a way that a physical fitness test data acquisition and detection method comprises the following steps:
firstly, acquiring physical testing data to be processed;
secondly, detecting abnormal data in the data to be processed based on a preset training model;
thirdly, determining the abnormal type of the abnormal data according to the data characteristics of the abnormal data, and correcting the abnormal data according to the abnormal type;
fourthly, randomly dividing the physical testing data corrected in the third step into a plurality of sections as testing data and training data;
fifthly, preprocessing each section of test data and training data in the fourth step;
sixthly, taking the training data preprocessed in the fifth step as each input of the convolutional neural network model, and extracting each group of characteristic data by using an unsupervised learning algorithm;
seventhly, fitting each group of characteristic data obtained in the sixth step, and judging the number of mixed distribution; calculating the weight and the mean value of each mixed distribution by using an EM (effective velocity) algorithm according to each group of characteristic data;
eighthly, establishing a three-level evaluation model, and obtaining grades and scores of the calculation result in the seventh step;
step nine, presetting the grade and the score of the step eight into a database, and acquiring health information through health detection equipment;
step ten, inquiring a health evaluation value corresponding to the health information in a preset database according to the health information;
step eleven, generating and displaying a health risk report according to the health assessment value;
and a twelfth step of carrying out early warning notification on the abnormal physique data by using an abnormal judgment program through an acousto-optic early warning device: in the body measurement process of the t period, after the monitoring object with the age of h and the sex of x performs the body measurement of the ith test item, body measurement data xi (t) which is the body condition monitoring data of the monitoring object about the ith test item in the t period is obtained; wherein denotes a male or female; i is 1,2, …, I represents the ith test item, I represents that the body test comprises I test items in total;
the thirteenth step, comparing and analyzing the body test data xi (t) of the monitoring object in the ith period and the body test data xi (t) of the monitoring object in the ith period before the tth period and the body test data of the ith test item in the nth period, and calculating the historical comparison singularity a of the xi (t);
comparing and analyzing the body measurement data xi (t) of the ith test item of the monitoring object in the t-th period with the body measurement data xi (t) of the ith test item of the M monitoring objects with the same ages and sexes, and calculating the sample comparison singularity b of the xi (t);
fourteenth, each test item is provided with a corresponding historical comparison singularity threshold Ti1 and a sample comparison singularity threshold Ti 2;
if a is larger than Ti1, an early warning is sent to the body test data of the monitoring object in the t period about the ith test item;
if b is greater than Ti2, giving an early warning to the testing data xi (t) of the ith test item of the monitoring object in the tth period;
fifthly, generating a constitution report according to the constitution data acquired in the fourteenth step through a report program, and printing the constitution report through a printer; the physical data, the analysis result, the evaluation result and the physical report of the set are stored through a micro memory; and displaying the acquired physical data, the analysis result, the evaluation result and the physical report through a display.
Further, the first step of acquiring the physical fitness test data to be processed comprises: marking data sources and data volumes of the physical testing data to be processed, and acquiring the physical testing data to be processed in batches according to the marks;
the detecting abnormal data in the data to be processed based on the preset training model comprises the following steps:
inputting data points in the data to be processed to the preset training model; and if the output result mark of the preset training model is not normal, determining that the data to be processed is abnormal data.
Further, the second step of determining the abnormal type of the abnormal data according to the data characteristics of the abnormal data includes:
if the abnormal data is a single data instance, determining that the abnormal data is a point abnormality;
and if the abnormal data is a set of a plurality of data instances, determining that the abnormal data is set abnormal.
Further, the method for preprocessing the test data and the training data in the five steps is as follows:
preprocessing each section of test data and training data by a 0-1 standardized method; wherein, the data normalized formula is as follows:
in which X is an input data item, XmaxFor the maximum term in this set of data, XminThe min term for this set of data.
Further, the convolutional neural network comprises two convolutional layers, two active layers and two pooling layers, wherein in the convolutional layers, the size of a convolutional core is set to be 3 × 1, the step length is designed to be 1, the active layers adopt Re L u active functions, a pooling layer filter is set to be 2 × 1, and the maximum pooling function is used;
and fitting each group of characteristic data by utilizing a Python fitting function, observing the data distribution condition, and recording the distribution number of the mixed distribution.
Further, the ninth step of collecting health information by the health detection device includes:
receiving a detection report uploaded by a user, wherein the detection report comprises carried gene defect information;
the querying of the health assessment value corresponding to the health information in a preset database according to the health information includes: inquiring a health evaluation value corresponding to the gene defect information in a preset database according to the gene defect information;
the collected health information further comprises: facial phenotype information;
the querying of the health assessment value corresponding to the health information in a preset database according to the health information includes: the database stores the corresponding relation between the facial phenotype information and the health risk; and matching one or more health risks corresponding to the facial phenotype information in a preset database according to the facial phenotype information.
Further, the eleventh step of generating and displaying a health risk report based on the health assessment value comprises:
comparing the facial phenotype information with a preset health risk model to determine the similarity between the facial phenotype information and the preset health risk model;
and generating and displaying a health risk report according to one or more health risks corresponding to the facial phenotype information and the similarity between the facial phenotype information and a preset health risk model.
Another objective of the present invention is to provide a physical fitness test data acquisition and detection system using the physical fitness test data acquisition and detection method, wherein the physical fitness test data acquisition and detection system comprises:
the physical constitution monitoring system comprises a physical constitution data acquisition module, a data processing module, a central control module, a physical constitution evaluation module, a health evaluation module, an abnormity early warning module, a physical constitution report generation module, a printing module, a data storage module and a display module.
The physique data acquisition module is connected with the central control module and used for acquiring physique test data information through physique detection equipment;
the data processing module is connected with the central control module and used for processing the acquired physical fitness test data through a data processing program;
the central control module is connected with the physical quality data acquisition module, the data processing module, the physical quality evaluation module, the health evaluation module, the abnormity early warning module, the physical quality report generation module, the printing module, the data storage module and the display module and is used for controlling each module to normally work through the main control computer;
the constitution evaluation module is connected with the central control module and used for evaluating the constitution according to the collected constitution data through a constitution evaluation program;
the health assessment module is connected with the central control module and used for assessing the health condition according to the physical assessment data through a health assessment program;
the abnormity early warning module is connected with the central control module and is used for carrying out early warning notification on abnormal physique data by utilizing an abnormity judgment program through the acousto-optic early warning device;
the physique report generation module is connected with the central control module and used for generating a physique report according to the collected physique data through a report program;
the printing module is connected with the central control module and used for printing the physique report through a printer;
the data storage module is connected with the central control module and used for storing the physique data, the analysis result, the evaluation result and the physique report form of the set through the micro memory;
and the display module is connected with the central control module and used for displaying the acquired physical data, the analysis result, the evaluation result and the physical report through the display.
Another object of the present invention is to provide a computer program product stored on a computer readable medium, comprising a computer readable program, which when executed on an electronic device, provides a user input interface to implement the physical fitness test data acquisition and detection method.
Another objective of the present invention is to provide a computer-readable storage medium storing instructions, which when executed on a computer, cause the computer to execute the physical fitness test data acquisition and detection method.
The invention has the advantages and positive effects that: according to the invention, the personal health information of the user is acquired through the health assessment module, and is matched with the related data in the database, so that the accurate assessment of the personal health information is realized, and the purpose of providing related genetic consultation suggestions for the user is achieved; the abnormal data is detected and rapidly corrected by the data processing module according to the abnormal value in the data to be processed according to the preset training model, the abnormal type of the abnormal data is judged according to the data characteristic of the abnormal data, and the abnormal data is corrected according to the abnormal type. Meanwhile, the body measurement data of the monitoring personnel and the body measurement data of the past period year are compared and analyzed through the early warning module, the body measurement data of the monitoring personnel and the body measurement data of other monitoring personnel of the same age and the same sex are compared and analyzed, and the historical comparison singularity and the sample comparison singularity are calculated, so that the reasonability of the body measurement data can be judged, and the early warning information is generated on unreasonable body measurement data to warn the working personnel to further confirm.
Drawings
Fig. 1 is a flowchart of a method for acquiring and detecting physical fitness test data according to an embodiment of the present invention.
Fig. 2 is a block diagram of a physical fitness test data acquisition and detection system according to an embodiment of the present invention;
in the figure: 1. a physique data acquisition module; 2. a data processing module; 3. a central control module; 4. a health assessment module; 5. a health assessment module; 6. an anomaly early warning module; 7. a physique report generation module; 8. a printing module; 9. a data storage module; 10. and a display module.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the method for acquiring and detecting physical fitness test data according to the embodiment of the present invention includes the following steps:
s101, collecting physical fitness test data information through physical fitness detection equipment; and processing the collected physical testing data through a data processing program.
S102, controlling the normal work of the physique test data acquisition and detection system through a main control computer; and (4) evaluating the constitution according to the collected constitution data by a constitution evaluating program.
S103, evaluating the health condition according to the physical evaluation data through a health evaluation program; and carrying out early warning notification on the abnormal physique data by using an abnormal judgment program through an acousto-optic early warning device.
S104, generating a physique report according to the collected physique data through a report program; and printing the physique report through a printer.
S105, storing the collected physical data, the analysis result, the evaluation result and the physical report by the micro memory; and displaying the acquired physical data, the analysis result, the evaluation result and the physical report through a display.
As shown in fig. 2, the physical fitness test data acquisition and detection system provided by the embodiment of the invention includes: the physical constitution data processing system comprises a physical constitution data acquisition module 1, a data processing module 2, a central control module 3, a physical constitution evaluation module 4, a health evaluation module 5, an abnormity early warning module 6, a physical constitution report generation module 7, a printing module 8, a data storage module 9 and a display module 10.
The physique data acquisition module 1 is connected with the central control module 3 and used for acquiring physique test data information through physique detection equipment;
the data processing module 2 is connected with the central control module 3 and is used for processing the acquired physical fitness test data through a data processing program;
the central control module 3 is connected with the constitution data acquisition module 1, the data processing module 2, the constitution evaluation module 4, the health evaluation module 5, the abnormity early warning module 6, the constitution report generation module 7, the printing module 8, the data storage module 9 and the display module 10 and is used for controlling each module to normally work through the main control computer;
the constitution evaluation module 4 is connected with the central control module 3 and is used for evaluating the constitution according to the collected constitution data through a constitution evaluation program;
the health evaluation module 5 is connected with the central control module 3 and used for evaluating the health condition according to the physical evaluation data through a health evaluation program;
the abnormity early warning module 6 is connected with the central control module 3 and is used for carrying out early warning notification on abnormal physique data by utilizing an abnormity judgment program through an acousto-optic early warning device;
the physique report generation module 7 is connected with the central control module 3 and used for generating a physique report according to the collected physique data through a report program;
the printing module 8 is connected with the central control module 3 and used for printing the physique report through a printer;
the data storage module 9 is connected with the central control module 3 and used for storing the physique data, the analysis result, the evaluation result and the physique report form of the set through the micro memory;
and the display module 10 is connected with the central control module 3 and used for displaying the acquired physical data, the analysis result, the evaluation result and the physical report through a display.
The invention is further described with reference to specific examples.
Example 1
Fig. 1 shows a method for acquiring and detecting physical fitness test data according to an embodiment of the present invention, and as a preferred embodiment, the method for processing the acquired physical fitness test data through a data processing program controlled by a main control computer according to the embodiment of the present invention includes:
(I) and acquiring physical testing data to be processed.
And (II) detecting abnormal data in the data to be processed based on a preset training model.
(III) determining the abnormal type of the abnormal data according to the data characteristics of the abnormal data, and correcting the abnormal data according to the abnormal type.
The embodiment of the invention provides a method for acquiring physical fitness test data to be processed, which comprises the following steps: marking data sources and data volumes of the physical testing data to be processed, and acquiring the physical testing data to be processed in batches according to the marks;
the method for detecting abnormal data in the data to be processed based on the preset training model provided by the embodiment of the invention comprises the following steps:
inputting data points in the data to be processed to the preset training model; and if the output result mark of the preset training model is not normal, determining that the data to be processed is abnormal data.
The determining the abnormal type of the abnormal data according to the data characteristics of the abnormal data provided by the embodiment of the invention comprises the following steps:
if the abnormal data is a single data instance, determining that the abnormal data is a point abnormality;
and if the abnormal data is a set of a plurality of data instances, determining that the abnormal data is set abnormal.
Example 2
Fig. 1 shows a method for acquiring and detecting physical fitness test data according to an embodiment of the present invention, and as a preferred embodiment, the method for evaluating physical fitness according to the acquired physical fitness data by a physical fitness evaluation program according to the embodiment of the present invention includes:
(1) and randomly dividing the processed physical testing data into a plurality of sections as testing data and training data.
(2) And (3) carrying out preprocessing operation on the test data and the training data in the step (1).
(3) And (3) taking the training data preprocessed in the step (2) as each input of the convolutional neural network model, and extracting each group of characteristic data by using an unsupervised learning algorithm.
(4) Fitting each group of characteristic data obtained in the step (3) and judging the number of mixed distribution; and calculating the weight and the mean value of each mixed distribution by using an EM algorithm according to each group of characteristic data.
(5) And (4) establishing a three-level evaluation model, and substituting the observation and calculation results obtained in the step (4) into the three-level evaluation model and the group constitution evaluation quantification formula to obtain a grade and a grading result.
The method for preprocessing the test data and the training data in the step (2) provided by the embodiment of the invention comprises the following steps:
preprocessing each section of test data and training data by a 0-1 standardized method; wherein, the data normalized formula is as follows:
in which X is an input data item, XmaxFor the maximum term in this set of data, XminThe min term for this set of data.
The convolutional neural network provided by the embodiment of the invention comprises two convolutional layers, two active layers and two pooling layers, wherein in the convolutional layers, the size of a convolutional core is set to be 3 × 1, the step length is designed to be 1, the active layers adopt Re L u active functions, a pooling layer filter is set to be 2 × 1, and the maximum pooling function is used;
and fitting each group of characteristic data by utilizing a Python fitting function, observing the data distribution condition, and recording the distribution number of the mixed distribution.
Example 3
Fig. 1 shows a method for collecting and detecting physical fitness test data according to an embodiment of the present invention, and as a preferred embodiment, the method for evaluating a health condition according to physical fitness evaluation data by a health evaluation program according to the embodiment of the present invention includes:
a) health information is collected by a health detection device.
b) And inquiring a health evaluation value corresponding to the health information in a preset database according to the health information.
c) And generating and displaying a health risk report according to the health assessment value.
The step a) of acquiring health information through health detection equipment provided by the embodiment of the invention comprises the following steps:
receiving a detection report uploaded by a user, wherein the detection report comprises carried gene defect information;
the querying of the health assessment value corresponding to the health information in a preset database according to the health information includes: inquiring a health evaluation value corresponding to the gene defect information in a preset database according to the gene defect information;
the collected health information further comprises: facial phenotype information;
the querying of the health assessment value corresponding to the health information in a preset database according to the health information includes: the database stores the corresponding relation between the facial phenotype information and the health risk; and matching one or more health risks corresponding to the facial phenotype information in a preset database according to the facial phenotype information.
The step c) of generating and displaying a health risk report according to the health assessment value provided by the embodiment of the invention comprises:
comparing the facial phenotype information with a preset health risk model to determine the similarity between the facial phenotype information and the preset health risk model;
and generating and displaying a health risk report according to one or more health risks corresponding to the facial phenotype information and the similarity between the facial phenotype information and a preset health risk model.
Example 4
As shown in fig. 1, the method for acquiring and detecting physical fitness test data according to the embodiment of the present invention is as follows, and as a preferred embodiment, the method for performing early warning notification on abnormal physical fitness data by using an abnormality determination program through an acousto-optic early warning device according to the embodiment of the present invention includes:
1) in the body measurement process of the t period, after the monitoring object with the age of h and the sex of x performs the body measurement of the ith test item, body measurement data xi (t) which is the body condition monitoring data of the monitoring object about the ith test item in the t period is obtained; wherein denotes a male or female; i-1, 2, …, I denotes the ith test item, I denotes that the physical test comprises a total of I test items.
2) Comparing and analyzing the body test data xi (t) of the monitoring object in the ith period relative to the ith test item with the body test data xi (t) of the monitoring object in the n periods before the tth period relative to the ith test item, and calculating the historical comparison singularity a relative to xi (t).
And comparing and analyzing the body measurement data xi (t) of the monitoring object in the ith period, which is related to the ith test item, with the body measurement data xi (t) of the M monitoring objects with the same ages and sexes, which are related to the ith test item, and calculating the sample comparison singularity b related to xi (t).
3) Each test item is provided with a corresponding historical comparison singularity threshold Ti1 and a corresponding sample comparison singularity threshold Ti 2;
if a is larger than Ti1, an early warning is sent to the body test data of the monitoring object in the t period about the ith test item;
if b is greater than Ti2, an early warning is sent to the testing data xi (t) of the monitoring object in the t period relative to the i test item.
The computer instructions may be stored on or transmitted from one computer-readable storage medium to another computer-readable storage medium, e.g., from one website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DS L) or wireless (e.g., infrared, wireless, microwave, etc.) means to another website site, computer, server, or data center via a solid state storage medium, such as a solid state Disk, or the like, (e.g., a solid state Disk, a magnetic storage medium, such as a DVD, a SSD, etc.), or any combination thereof.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (10)
1. A physical fitness test data acquisition and detection method is characterized by comprising the following steps:
firstly, acquiring physical testing data to be processed;
secondly, detecting abnormal data in the data to be processed based on a preset training model;
thirdly, determining the abnormal type of the abnormal data according to the data characteristics of the abnormal data, and correcting the abnormal data according to the abnormal type;
fourthly, randomly dividing the physical testing data corrected in the third step into a plurality of sections as testing data and training data;
fifthly, preprocessing each section of test data and training data in the fourth step;
sixthly, taking the training data preprocessed in the fifth step as each input of the convolutional neural network model, and extracting each group of characteristic data by using an unsupervised learning algorithm;
seventhly, fitting each group of characteristic data obtained in the sixth step, and judging the number of mixed distribution; calculating the weight and the mean value of each mixed distribution by using an EM (effective velocity) algorithm according to each group of characteristic data;
eighthly, establishing a three-level evaluation model, and obtaining grades and scores of the calculation result in the seventh step;
step nine, presetting the grade and the score of the step eight into a database, and acquiring health information through health detection equipment;
step ten, inquiring a health evaluation value corresponding to the health information in a preset database according to the health information;
step eleven, generating and displaying a health risk report according to the health assessment value;
and a twelfth step of carrying out early warning notification on the abnormal physique data by using an abnormal judgment program through an acousto-optic early warning device: in the body measurement process of the t period, after the monitoring object with the age of h and the sex of x performs the body measurement of the ith test item, body measurement data xi (t) which is the body condition monitoring data of the monitoring object about the ith test item in the t period is obtained; wherein denotes a male or female; i is 1,2, …, I represents the ith test item, I represents that the body test comprises I test items in total;
the thirteenth step, comparing and analyzing the body test data xi (t) of the monitoring object in the ith period and the body test data xi (t) of the monitoring object in the ith period before the tth period and the body test data of the ith test item in the nth period, and calculating the historical comparison singularity a of the xi (t);
comparing and analyzing the body measurement data xi (t) of the ith test item of the monitoring object in the t-th period with the body measurement data xi (t) of the ith test item of the M monitoring objects with the same ages and sexes, and calculating the sample comparison singularity b of the xi (t);
fourteenth, each test item is provided with a corresponding historical comparison singularity threshold Ti1 and a sample comparison singularity threshold Ti 2;
if a is larger than Ti1, an early warning is sent to the body test data of the monitoring object in the t period about the ith test item;
if b is greater than Ti2, giving an early warning to the testing data xi (t) of the ith test item of the monitoring object in the tth period;
fifthly, generating a constitution report according to the constitution data acquired in the fourteenth step through a report program, and printing the constitution report through a printer; the physical data, the analysis result, the evaluation result and the physical report of the set are stored through a micro memory; and displaying the acquired physical data, the analysis result, the evaluation result and the physical report through a display.
2. The fitness test data collection and testing method according to claim 1, wherein the first step of obtaining the fitness test data to be processed comprises: marking data sources and data volumes of the physical testing data to be processed, and acquiring the physical testing data to be processed in batches according to the marks;
the detecting abnormal data in the data to be processed based on the preset training model comprises the following steps:
inputting data points in the data to be processed to the preset training model; and if the output result mark of the preset training model is not normal, determining that the data to be processed is abnormal data.
3. The fitness test data collection and detection method of claim 1, wherein the second step of determining the abnormal type of the abnormal data according to the data characteristics of the abnormal data comprises:
if the abnormal data is a single data instance, determining that the abnormal data is a point abnormality;
and if the abnormal data is a set of a plurality of data instances, determining that the abnormal data is set abnormal.
4. The method for collecting and testing physical fitness test data according to claim 1, wherein the five steps of preprocessing the test data and the training data are as follows:
preprocessing each section of test data and training data by a 0-1 standardized method; wherein, the data normalized formula is as follows:
in which X is an input data item, XmaxFor the maximum term in this set of data, XminThe min term for this set of data.
5. The fitness test data acquisition and detection method according to claim 1, wherein the convolutional neural network in the sixth step comprises two convolutional layers, two active layers and two pooling layers, wherein in the convolutional layers, the size of a convolutional kernel is set to be 3 × 1, the step size is designed to be 1, the active layers adopt Re L u activation functions, a pooling layer filter is set to be 2 × 1, and the maximum pooling function is used;
and fitting each group of characteristic data by utilizing a Python fitting function, observing the data distribution condition, and recording the distribution number of the mixed distribution.
6. The fitness test data collection and testing method of claim 1, wherein the ninth step of collecting health information via the health testing device comprises:
receiving a detection report uploaded by a user, wherein the detection report comprises carried gene defect information;
the querying of the health assessment value corresponding to the health information in a preset database according to the health information includes: inquiring a health evaluation value corresponding to the gene defect information in a preset database according to the gene defect information;
the collected health information further comprises: facial phenotype information;
the querying of the health assessment value corresponding to the health information in a preset database according to the health information includes: the database stores the corresponding relation between the facial phenotype information and the health risk; and matching one or more health risks corresponding to the facial phenotype information in a preset database according to the facial phenotype information.
7. The fitness test data collection and testing method of claim 1, wherein the eleventh step of generating and displaying a health risk report based on the health assessment value comprises:
comparing the facial phenotype information with a preset health risk model to determine the similarity between the facial phenotype information and the preset health risk model;
and generating and displaying a health risk report according to one or more health risks corresponding to the facial phenotype information and the similarity between the facial phenotype information and a preset health risk model.
8. A physical fitness test data acquisition and detection system applying the physical fitness test data acquisition and detection method according to any one of claims 1 to 7, wherein the physical fitness test data acquisition and detection system comprises:
the system comprises a constitution data acquisition module, a data processing module, a central control module, a constitution evaluation module, a health evaluation module, an abnormity early warning module, a constitution report generation module, a printing module, a data storage module and a display module;
the physique data acquisition module is connected with the central control module and used for acquiring physique test data information through physique detection equipment;
the data processing module is connected with the central control module and used for processing the acquired physical fitness test data through a data processing program;
the central control module is connected with the physical quality data acquisition module, the data processing module, the physical quality evaluation module, the health evaluation module, the abnormity early warning module, the physical quality report generation module, the printing module, the data storage module and the display module and is used for controlling each module to normally work through the main control computer;
the constitution evaluation module is connected with the central control module and used for evaluating the constitution according to the collected constitution data through a constitution evaluation program;
the health assessment module is connected with the central control module and used for assessing the health condition according to the physical assessment data through a health assessment program;
the abnormity early warning module is connected with the central control module and is used for carrying out early warning notification on abnormal physique data by utilizing an abnormity judgment program through the acousto-optic early warning device;
the physique report generation module is connected with the central control module and used for generating a physique report according to the collected physique data through a report program;
the printing module is connected with the central control module and used for printing the physique report through a printer;
the data storage module is connected with the central control module and used for storing the physique data, the analysis result, the evaluation result and the physique report form of the set through the micro memory;
and the display module is connected with the central control module and used for displaying the acquired physical data, the analysis result, the evaluation result and the physical report through the display.
9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement the fitness test data acquisition and detection method according to any one of claims 1-7 when executed on an electronic device.
10. A computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to perform the fitness test data acquisition and detection method according to any one of claims 1 to 7.
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| CN202010140511.9A CN111403031A (en) | 2020-03-03 | 2020-03-03 | Physique test data acquisition and detection system and method |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115620802A (en) * | 2022-09-02 | 2023-01-17 | 蔓之研（上海）生物科技有限公司 | Method and system for processing gene data |
| CN116350203A (en) * | 2023-06-01 | 2023-06-30 | 广州华夏汇海科技有限公司 | Physical testing data processing method and system |
| CN117292836A (en) * | 2023-11-27 | 2023-12-26 | 广州华夏汇海科技有限公司 | Body measurement score monitoring method and system |
-
2020
- 2020-03-03 CN CN202010140511.9A patent/CN111403031A/en not_active Withdrawn
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115620802A (en) * | 2022-09-02 | 2023-01-17 | 蔓之研（上海）生物科技有限公司 | Method and system for processing gene data |
| CN115620802B (en) * | 2022-09-02 | 2023-12-05 | 蔓之研（上海）生物科技有限公司 | Gene data processing method and system |
| CN116350203A (en) * | 2023-06-01 | 2023-06-30 | 广州华夏汇海科技有限公司 | Physical testing data processing method and system |
| CN116350203B (en) * | 2023-06-01 | 2023-08-18 | 广州华夏汇海科技有限公司 | Physical testing data processing method and system |
| CN117292836A (en) * | 2023-11-27 | 2023-12-26 | 广州华夏汇海科技有限公司 | Body measurement score monitoring method and system |
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