CN117352119A - Student physical health monitoring data analysis system and method - Google Patents
Student physical health monitoring data analysis system and method Download PDFInfo
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Abstract
The invention relates to the technical field of health monitoring and discloses a student physical health monitoring data analysis system and a student physical health monitoring data analysis method.
Description
Technical Field
The invention relates to the technical field of health monitoring, in particular to a student physical health monitoring data analysis system and method.
Background
Physical health is a monitoring of the quality of a human body, and people keep physical health by performing various activities in daily living. Students are a group of great concern, and it is necessary for students to monitor physical health.
However, the traditional collection mode of physical data of students mostly adopts physical examination table entry, which has the problems of data singleization and immobilization, and is difficult to realize comprehensive analysis of physical health conditions of different students by combining the data of the students in daily activities, and the reliability of the obtained physical health analysis results of the students is not high.
The foregoing is provided merely for the purpose of facilitating understanding of the technical scheme of the present invention and is not intended to represent an admission that the foregoing is related art.
Disclosure of Invention
The invention mainly aims to provide a student physical health monitoring data analysis system and method, and aims to solve the technical problem that the traditional physical health data collection mode of students is recorded by adopting a physical examination table, and comprehensive analysis of physical health conditions of different students is difficult to realize by combining multiple types of data.
In order to achieve the above object, the present invention provides a student physical health monitoring data analysis system, the system comprising: the device comprises a data acquisition module, a data cache module and a data analysis module;
The data acquisition module is used for acquiring real-time physical examination data of the current user based on the information bar code corresponding to the current user and sending the real-time physical examination data to the data caching module;
the data caching module is used for generating a personal constitution data set according to the real-time physical examination data, the general activity data of the current user and the activity scene information, and sending the personal constitution data set to the data analysis module;
the data analysis module is used for dividing the health type of the current user when the personal physique data set is received;
the data analysis module is further used for sending the personal physique data set to a professional doctor end corresponding to the health type according to the division result, and generating a health guidance strategy according to evaluation information fed back by the professional doctor end;
the data analysis module is further used for determining a physical health analysis result of the current user according to the health guidance strategy and the personal physical data set.
Optionally, the system further comprises: a visual display module;
the data analysis module is further used for sending the physical health analysis result of the current user to the visual display module;
The visual display module is used for determining the authority level according to the source of the query instruction when the query instruction is received, and acquiring a chart template of the authority level;
the visual display template is further used for displaying the physical health analysis result of the current user based on the chart template.
Optionally, the visual display module is further configured to extract a field identifier in the query instruction when the query instruction is received;
the visual display module is also used for matching the field identification in a standard authority library;
the visual display module is further used for determining the authority level of the query instruction according to the matching item when the matching item exists in the standard authority library;
the visual display module is further used for determining a corresponding initial chart template according to the authority level;
the visual display module is further used for updating the initial chart template according to the physical health analysis result of the current user, and generating and displaying a physical health analysis table of the current user.
Optionally, the data caching module comprises a data query sub-module and a data encryption sub-module which are connected;
The data query sub-module is used for querying general activity data and activity scene information of the current user when the real-time physical examination data are received;
the data query sub-module is used for forming the real-time physical examination data, the general activity data and the activity scene information into a personal constitution data set of the current user and sending the personal constitution data set to the data encryption sub-module;
and the data encryption sub-module is used for encrypting the personal constitution data set by adopting an encryption algorithm when the personal constitution data set is acquired, and sending the encrypted personal constitution data set to the data analysis module.
Optionally, the data analysis module is further configured to determine a corresponding decryption algorithm when the encrypted personal constitution data set is received, and obtain the decrypted personal constitution data set based on the decryption algorithm;
the data analysis module is further configured to obtain an activity scene category when the personal constitution data set is received, and obtain a personal constitution standard score of the current user through a preset standard parameter generation model, where the activity scene category includes: safety scenes, hidden danger scenes and risk scenes;
The data analysis module is further used for determining the health type of the current user according to the personal physique standard score and the activity scene category.
Optionally, the preset standard parameter generating model is trained through a training set composed of historical physical examination data and historical activity data, and the preset standard parameter generating model is used for extracting the embedded features of the current user and the personal physical standard score corresponding to the embedded features from the personal physical data set.
Optionally, the health type includes a standard type, a type to be attended to, and a risk type;
the data analysis module is further used for sending the personal physique data set to a professional doctor end corresponding to the health type according to the health type of the current user;
the data analysis module is further used for receiving evaluation information fed back by the professional doctor end when the health type of the current user is a standard type, and generating a health behavior mode of the current user;
the data analysis module is further used for receiving evaluation information fed back by the professional doctor end when the health type of the current user is the type to be attended to, and obtaining an attended health risk item of the current user and a corresponding prevention strategy;
And the data analysis module is also used for receiving the evaluation information fed back by the professional doctor end when the health type of the current user is a risk type, obtaining the health risk item of the current user and generating warning information.
Optionally, the data acquisition module is further configured to acquire each item of physical examination data of the current user based on the information bar code of the current user;
the data acquisition module is also used for unifying the formats of all acquired physical examination data and deleting blank data to obtain real-time physical examination data of the current user;
the data acquisition module is further used for sending the real-time physical examination data to the data caching module.
Optionally, the system further comprises: a bar code generation module;
the bar code generation module is used for acquiring an input information table of the user to be acquired in the current scene and generating an information bar code of each user to be acquired;
the bar code generation module is further used for determining a current user and acquiring an information bar code corresponding to the current user when the user to be acquired passes the face authentication.
In addition, in order to achieve the above purpose, the invention also provides a student physical health monitoring data analysis method based on the student physical health monitoring data analysis system, which comprises the following steps:
The data acquisition module acquires real-time physical examination data of the current user based on an information bar code corresponding to the current user, and sends the real-time physical examination data to the data caching module;
the data caching module generates a personal constitution data set according to the real-time physical examination data, the general activity data of the current user and the activity scene information, and sends the personal constitution data set to the data analysis module;
the data analysis module divides the health type of the current user when receiving the personal physique data set;
the data analysis module sends the personal physique data set to a professional doctor end corresponding to the health type according to the division result, and generates a health guidance strategy according to evaluation information fed back by the professional doctor end;
and the data analysis module combines the personal physique data set according to the health guidance strategy to determine the physique health analysis result of the current user.
Firstly, a data acquisition module acquires real-time physical examination data of a current user based on an information bar code corresponding to the current user, and sends the real-time physical examination data to a data cache module; the data caching module generates a personal constitution data set according to the real-time physical examination data, the general activity data of the current user and the activity scene information, and sends the personal constitution data set to the data analysis module; the data analysis module divides the health type of the current user when receiving the personal physique data set; the data analysis module sends the personal physique data set to a professional doctor end corresponding to the health type according to the division result, and generates a health guidance strategy according to evaluation information fed back by the professional doctor end; and the data analysis module combines the personal physique data set according to the health guidance strategy to determine the physique health analysis result of the current user. According to the invention, after the real-time physical examination data of the current user is obtained according to the information bar code, the personal physical constitution data set is generated by combining the general activity data and the activity scene data, the current user can be divided into health types based on the personal physical constitution data set, and the health type is distributed to the professional doctor end corresponding to the health type according to the division result so as to obtain a health guidance strategy, a personalized health guidance scheme is provided for different users, and finally, the physical health analysis result of the current user is obtained by combining the personal physical constitution data set.
Drawings
FIG. 1 is a schematic diagram of a first embodiment of a student physical health monitoring data analysis system according to the present invention;
fig. 2 is a schematic structural diagram of a student physical health monitoring data analysis system including a bar code generation module;
FIG. 3 is a schematic diagram of a second embodiment of the student physical health monitoring data analysis system according to the present invention;
FIG. 4 is a schematic diagram of a third embodiment of the student physical health monitoring data analysis system according to the present invention;
fig. 5 is a flowchart of a first embodiment of the student physical health monitoring data analysis method according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
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.
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present invention are merely used to explain the relative positional relationship, movement, etc. between the components in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly.
Furthermore, the description of "first," "second," etc. in this disclosure is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the technical solutions should be considered that the combination does not exist and is not within the scope of protection claimed by the present invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a first embodiment of a student physical health monitoring data analysis system according to the present invention. A first embodiment of the student physical health monitoring data analysis system according to the present invention is presented based on fig. 1.
In this embodiment, the student physical health monitoring data analysis system includes: a data acquisition module 10, a data caching module 20 and a data analysis module 30.
It can be appreciated that the system provided in this embodiment may be applied in a scenario where physical health monitoring is required for students, or in other scenarios where physical health monitoring is required for different people and analysis of monitoring data is required. The present embodiment and the following embodiments will be specifically described with reference to the above-described student physical fitness monitoring data analysis system (hereinafter referred to as "system").
The data acquisition module 10 is configured to obtain real-time physical examination data of a current user based on an information barcode corresponding to the current user, and send the real-time physical examination data to the data caching module.
It should be understood that the current user may be a student or other crowd who needs to obtain a physical health analysis result, and the information bar code of the current user may be a unique information bar code for identifying the current user in a distinguishing manner, and since the number of the current user may be one or more in a system application scene, an entry list of users to be acquired, which need to obtain real-time physical examination data in the system application scene, may be collected in advance, an entry information table of the users to be acquired is determined, and then the information bar codes of each user to be acquired are generated and allocated according to the entry information table.
Further, referring to fig. 2, a barcode generating module may be disposed in the system, and fig. 2 is a schematic structural diagram of a student physical health monitoring data analysis system including the barcode generating module.
In fig. 2, the system further comprises: and a bar code generation module 01.
The bar code generation module 01 is used for acquiring an input information table of the user to be acquired in the current scene and generating information bar codes of the users to be acquired.
It should be understood that the current scene may be a scene of a system application, and the user to be acquired may be all users including the current user who need to acquire real-time physical examination data in the scene of the system application.
It can be understood that the entry information table may be a table that is uploaded in advance to a database of the system and contains identity information of all users to be collected, and the identity information of the users to be collected may include age, gender, identification card number, credentials of the users, and the like.
It should be understood that the information bar code may be pushed to the personal mobile terminal of each user to be acquired in the current scene based on a picture form, or may be printed to generate a bar code sticker to be distributed to each user to be acquired in the current scene in a physical form, which is not limited in this embodiment.
The bar code generation module 01 is further configured to determine a current user when the user to be acquired passes the face authentication, and acquire an information bar code corresponding to the current user.
It should be understood that when each user to be acquired acquires the information bar code in the current scene, based on the personal mobile terminal scanning the information bar code, the identity information contained in the information bar code can be acquired and the face recognition authentication can be performed. The user to be acquired through face recognition authentication can be determined to be the current user needing real-time physical examination data acquisition.
In a specific implementation, the bar code generation module 01 acquires the input information list of all the users to be acquired in the current scene, generates the information bar codes of all the users to be acquired, and distributes the bar codes to all the users to be acquired so that the users to be acquired can finish face recognition authentication based on the personal mobile terminal. The system determines the user to be acquired which passes the face authentication as the current user needing to acquire real-time physical examination data, and acquires the information bar code corresponding to the current user, so that the situation that data is not matched with the user in the subsequent data analysis result can be prevented.
It should be understood that the real-time physical examination data may be physical examination data including physical examination of the current user last time, where the physical examination data may include data of conventional physical examination items, for example: physical examination items (height, weight, etc.) for physical body type and physical function (vital capacity, sit-ups, sitting-forward flexion, jump, etc.). Considering that the data of each physical examination item is acquired by the current user by sequentially carrying out each physical examination item, that is, not acquired simultaneously, irregular data entry and invalid data may exist.
Further, the data acquisition module 10 is further configured to acquire various physical examination data of the current user based on the information bar code of the current user.
The data acquisition module 10 is further configured to perform format unification on each item of acquired physical examination data, and delete blank data to obtain real-time physical examination data of the current user.
It may be appreciated that, when the data acquisition module 10 collects each physical examination item data of the current user, the data may be subjected to a unified format specification, for example, each physical examination item data may be unified and organized according to a corresponding relationship between physical examination items and physical examination values, and a standard numerical item standard of different physical examination item data may be preset, so as to ensure that a normalized physical examination value is obtained, and further generate a physical examination table including the corresponding relationship.
It should be understood that, when the data acquisition module 10 obtains the physical examination table with the standard format, each physical examination item in the physical examination table may be reviewed, the corresponding physical examination item with the blank physical examination value is deleted, the real-time physical examination data of the current user is obtained, and the real-time physical examination data is sent to the data buffer module 20.
The data caching module 20 is configured to generate a personal physical constitution data set according to the real-time physical examination data, the general activity data of the current user, and the activity scene information, and send the personal physical constitution data set to the data analysis module.
It should be understood that the general activity data of the current user may include data of the current user in daily activities, such as exercise time, speed, step number, etc., and may further include daily behavior pattern data of the current user, including habit data such as sleep time, exercise frequency, etc.
It should be appreciated that the activity scenario information may include information about the environment in which the current user is staying long during the daily activity, such as: the current climate conditions of the environment in which the user is located, whether pollutant emissions exist in the surroundings, etc.
It can be understood that the real-time physical examination data can be assisted through the general activity data and the activity scene information of the current user, and the monitoring data is generated into a plurality of pieces, so that a comprehensive personal physical constitution data set is realized.
In a specific implementation, when receiving real-time physical examination data, the data caching module 20 combines general activity data including exercise data and behavior pattern data in daily life of the user and related information of the environment where the user is located, so as to generate a data-diversified and more comprehensive personal physical constitution data set.
The data analysis module 30 is configured to divide the health type of the current user when the personal physique dataset is received.
It is understood that the data analysis module 30 may divide the current user into different health types based on the personal fitness data set. The different health types may reflect the physical health grade of the current user.
The data analysis module 30 is further configured to send the personal physique dataset to a specialist end corresponding to the health type according to the division result, and generate a health guidance policy according to evaluation information fed back by the specialist end.
It should be understood that, a professional doctor end for receiving the personal physique data set may be pre-allocated, and the professional doctor end may be pre-allocated according to different health types, so that the personal physique data set may be directly sent to the professional doctor end corresponding to the health type of the current user based on the difference of the division results, so that the rate of obtaining the feedback evaluation information may be improved.
It can be understood that the health guiding policy may be a policy generated based on evaluation information fed back by a professional doctor, for guiding the current user to execute a behavioral habit pattern for enhancing physical health. The health guidance strategy can be used for carrying out personalized suggestion on physical health conditions of current users, and the limitation of the traditional health management mode in time and space can be broken.
The data analysis module 30 is further configured to determine a physical health analysis result of the current user according to the health guidance policy in combination with the personal physical data set.
In a specific implementation, the data analysis module 30 may further combine the above-mentioned personal physical health data set generated according to the real-time physical examination data, the general activity data of the current user, and the activity scene information according to a health guidance policy with personalized advice proposed for the current user, to obtain a physical health analysis result of the current user, where the analysis result includes both the actual physical condition of the current user and the health guidance scheme proposed for the actual physical condition.
In this embodiment, the system acquires real-time physical examination data based on the information bar code corresponding to the current user through the data acquisition module 10, generates a personal physical constitution data set according to the real-time physical examination data, general activity data and activity scene information through the data caching module 20, can divide the current user into health types based on the personal physical constitution data set, when the data analysis module 30 receives the personal physical constitution data set, divides the current user into health types, sends the personal physical constitution data set to a professional doctor end corresponding to the health types according to the division result to generate a health guidance strategy according to evaluation information fed back by the professional doctor end, can provide personalized health guidance schemes for different users, and finally the data analysis module 30 combines the personal physical constitution data set according to the health guidance strategy to determine the physical health analysis result of the current user. Compared with the traditional analysis result obtained by a single data source, the method realizes the comprehensive analysis of the physical health of students.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a second embodiment of the student physical health monitoring data analysis system according to the present invention.
As shown in fig. 3, the student physical health monitoring data analysis system further includes: the module 40 is visually displayed.
It should be understood that the visual display module 40 is connected to the data analysis module 30, and the data analysis module 30 is further configured to send the physical health analysis result of the current user to the visual display module 40.
The visual display module 40 is configured to determine a permission level according to a source of a query instruction when the query instruction is received, and obtain a chart template of the permission level.
It can be understood that the query instruction may be initiated by a user from a different source that needs to obtain the health analysis result of the current user from the system, where the user from the different source may be the current user, or may be another level that needs to obtain physical health data of the current user in the scenario of the system application, for example, when the current user is a student group, the query instruction may be an instruction sent by a parent, a teacher, or an upper inspection department of the student.
It should be understood that, in order to ensure the data privacy of the current user, the permission level corresponding to the query instruction can be determined when the query instruction is acquired, so as to prevent the unauthorized query condition.
Specifically, the visual display module 40 is further configured to extract, when a query instruction is received, a field identifier in the query instruction.
It should be understood that the query instruction may be an instruction encapsulated by field level data, where the query instruction may include a query source identifier and query details, and the field identifier in the query instruction may be obtained by field level splitting the query instruction. This field identification may be used to identify users of different origins.
The visual display module 40 is further configured to match the field identifier in a standard rights library; and when a matching item exists in the standard authority library, determining the authority level of the query instruction according to the matching item.
It should be appreciated that the standard rights library may be a pre-set database containing different field identifications and corresponding rights levels. By matching the field identification of the extracted query instruction in the standard authority library, it can be first determined whether the query instruction has a query authority for the data content in the current system, and the authority level of the query instruction. For example, a field identifier #010 exists in the standard authority library, and the corresponding authority level is one level. Then when the field identification in the extracted query instruction is #01003, the field identification has a matching item in the standard authority library, and the authority level of the query instruction is one level.
It will be appreciated that the visual presentation template 40 is also used to determine a corresponding initial chart template based on the permission level.
It should be understood that the initial chart template may be a preset template displayed to users of different sources, and the visual display template 40 may visually display the physical health analysis result of the current user, which can be obtained by the query instruction under the permission level, based on different permission levels, so that the obtained analysis result is more visual, the analysis result is assisted to be understood, and the requirements of query instruction originators of different sources are met.
It should be appreciated that the initial chart template may include chart templates of graphs, pie charts, radar charts, and scatter charts of data, which may also include tabular and plain text sections. For the inquiry instructions of different authority levels, initial chart templates comprising different numbers of chart templates and different layout forms can be set according to the data volume size which can be acquired under different authority levels.
The visual display module 40 is further configured to update the initial chart template according to the physical health analysis result of the current user, and generate and display a physical health analysis table of the current user.
In a specific implementation, when the system obtains the physical health analysis result of the current user, the system can determine the display template of the analysis result, namely an initial chart template, according to the authority levels of different query instructions, extract data from the physical health analysis result and update the initial chart template, so as to obtain and display the physical health analysis table of the current user.
In this embodiment, when a query instruction is received, the system determines a permission level according to the source of the query instruction and obtains a chart template of the permission level through the visual display module 40; specifically, when a query instruction is received, extracting a field identifier in the query instruction, matching the field identifier in a standard authority library, determining the authority level of the query instruction according to the matching item when the matching item exists in the standard authority library, determining a corresponding initial chart template according to the authority level, updating the initial chart template according to the physical health analysis result of the current user, and generating and displaying a physical health analysis table of the current user. In the embodiment, the system can judge the authority of different inquiry instructions, prevent the occurrence of unauthorized inquiry conditions, ensure the data privacy of the current user, and display the physical health analysis table of the current user through the chart template in consideration of the difference of the data volume which can be acquired under different authority levels, thereby providing a concise and clear data presentation effect and being beneficial to intuitively displaying the physical health condition of the current user.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a third embodiment of the student physical health monitoring data analysis system according to the present invention.
As shown in fig. 4, in the student physical health monitoring data analysis system, the data caching module 20 includes a data query sub-module 201 and a data encryption sub-module 202 that are connected.
The data query sub-module 201 is configured to query, when the real-time physical examination data is received, general activity data and activity scene information of the current user.
It should be understood that the system may be connected to a historical activity database of each user to be collected in the current scenario, where the historical activity database may include general activity data of the user recorded by a device such as a bracelet, a mobile terminal sensor, and may also include information about the environment in which the user is located when performing the above-mentioned historical activities. The historical activity databases of different users may be differentiated based on information barcodes. The historical activity database may be stored in another cloud server connected to the system, or may be built in the system, which is not limited in this embodiment.
In a specific implementation, when receiving the real-time physical examination data sent by the data acquisition module 10, the data query sub-module 201 may query the historical activity database for general activity data and activity scene information of the current user based on the information bar code of the current user as a query identifier.
The data query sub-module 201 is configured to compose the real-time physical examination data, the general activity data, and the activity scene information into a personal physical constitution data set of the current user, and send the personal physical constitution data set to the data encryption sub-module 202.
The data encryption sub-module 202 is configured to encrypt the personal constitution data set by using an encryption algorithm when the personal constitution data set is acquired, and send the encrypted personal constitution data set to the data analysis module.
It should be appreciated that, in order to protect the information privacy of the user, the transmission of the personal physique dataset may be implemented based on an encryption algorithm, taking into account the risk that the user data may be subject to data leakage during the transmission process. The encryption algorithm may be, for example, a symmetric encryption algorithm, an asymmetric encryption algorithm, a hash function, a digital signature, etc., and the choice of the encryption algorithm is not limited in this embodiment.
Accordingly, the data analysis module 30 is further configured to determine a corresponding decryption algorithm when the encrypted personal constitution data set is received, and obtain the decrypted personal constitution data set based on the decryption algorithm.
The data analysis module 30 is further configured to obtain an activity scene category when the personal constitution data set is received, and obtain a personal constitution standard score of the current user through a preset standard parameter generation model.
It should be noted that, according to the activity scene information, the category of the activity scene may be determined, and the activity scene category may be divided into: safety scene, hidden danger scene and risk scene. The activity scene category can be classified by integrating the weather condition of the environment where the current user is located, whether the surrounding environment has pollutant emission and other relevant information.
It should be understood that the preset standard parameter generation model may be a model for extracting an embedded feature (embedding) of the current user and a personal physique standard score corresponding to the embedded feature from a personal physique data set. The preset standard parameter generation model may be constructed based on a common AI model, such as a neural network model including convolutional neural network (Convolutional Neural Network, CNN), cyclic neural network (Recurrent Neural Network, RNN), bi-directional coding (Bidirectional Encoder Representation from Transformers, bert), or a conventional machine learning model including Support Vector Machine (SVM) and random forest.
It should be appreciated that the pre-set standard parametric model may be trained from a training set of historical physical examination data, which may include physical examination data of different users, and historical activity data, which may include data of different users during daily activities. The preset standard model can learn the mapping relation between the embedded features and the personal physique standard score through the training set, the embedded features can be features representing the factors of the users and extracted from the personal physique data sets of different users, and the privacy of the users can be further prevented from being leaked due to the unreadable embedded features.
The data analysis module 30 is further configured to determine the health type of the current user according to the personal physique standard score and the activity scene category.
It should be understood that the above-mentioned standard score of the individual constitution outputted by the preset standard parameter model may be divided into stage scores, for example: the output personal fitness criteria may be set to be divided into percentile categories and scores of 50, 70 and 100 may be set to the division threshold. And the health type of the current user can be determined by combining the activity scene types of different types, and the health type of the current user can be finally determined by considering the self factors and the external environment factors. The health type of the current user determined according to the individual physical standard score and the activity scene category may be exemplified with reference to table 1.
In Table 1, the individual physical standards are divided into three divisions of 1-50, 50-70 and 70-100; the scene categories include: safety scenes, hidden danger scenes and risk scenes; the health type includes; standard type, type of interest, and risk type. The personal physique standard is divided into 1-50, and the health type of the current user with the scene category being a safety scene is the type to be focused; the personal physique standard is divided into 1-50, the scene category is hidden danger scenes or the health type of the current user of the risk scenes is risk type; the personal physique standard is divided into 50-70, and the health type of the current user with the scene category of a safety scene or a hidden danger scene is the type to be focused; the personal physique standard is divided into 50-70, and the health type of the current user with the scene category being a risk scene is the risk type; the personal physique standard is divided into 70-100, and the health type of the current user with the scene category being a safety scene is the standard type; the personal physique standard is divided into 70-100, and the health type of the current user with the scene category of hidden danger scenes or risk scenes is the type to be focused.
TABLE 1
Further, in order to generate personalized health guidance policies according to different health types, the data analysis module 30 is further configured to send the personal physique dataset to a specialist end corresponding to the health type according to the health type of the current user.
The data analysis module 30 is further configured to receive evaluation information fed back by the professional physician end when the health type of the current user is a standard type, and generate a health behavior pattern of the current user.
It should be understood that, since the health type of the current user is a standard type, the health behavior pattern of the current user may be acquired when the feedback evaluation information is received, so that the subsequent current user may continue to execute the health behavior pattern to maintain good physical health.
The data analysis module 30 is further configured to receive evaluation information fed back by the professional physician end when the health type of the current user is a type to be attended to, and obtain an attended health risk item of the current user and a corresponding prevention policy.
It should be understood that, since the health type of the current user is the type to be focused, after receiving the feedback evaluation information, risk items which have potential influence on physical health and are needed to be focused by the current user in daily life behaviors and environments can be determined, and corresponding measures for prevention and treatment can be provided. For example, the health risk item to be noted of the current user may be a chemical plant where long-term sleep is insufficient and emission pollutants exist near the living environment, and the corresponding preventive strategy may be to adjust sleep time and change living environment, etc.
The data analysis module 30 is further configured to receive the evaluation information fed back by the professional physician end when the health type of the current user is a risk type, obtain a health risk item of the current user, and generate warning information.
It should be understood that, since the health type of the current user is a risk type, the physical fitness of the current user is not good at this time, and there may be disease items to be diagnosed. For example, a chemical plant that emits dust contaminants is present near the living environment of the current user, the health risk item may be a lung, and alert information may be generated to alert the current user that further treatment is needed.
In this embodiment, the data cache module 20 in the system includes a data query sub-module 201 and a data encryption sub-module 202 that are connected, so that encrypted transmission of a personal physique data set can be realized, and privacy disclosure risk is avoided. In addition, when the data analysis module 30 receives the encrypted personal constitution data set, it decrypts the encrypted personal constitution data set, then obtains the activity scene category, and combines the personal constitution standard score generated by the preset standard parameter generation model to obtain the health type of the current user considering the self factor and the external environment factor. The health type is further divided into a standard type, a type to be concerned and a risk type, based on different health types, the limitation of the traditional health management mode in time and space can be broken through by combining evaluation information fed back by a professional doctor end of the corresponding type, and personalized health guidance directions can be given to physical health conditions of different students.
In addition, the invention also provides a student physical health monitoring data analysis method based on the student physical health monitoring data analysis system, and referring to fig. 5, fig. 5 is a flow chart of a first embodiment of the student physical health monitoring data analysis method. The student physical health monitoring data analysis method comprises the following steps:
step S10: and the data acquisition module acquires real-time physical examination data of the current user based on the information bar code corresponding to the current user, and sends the real-time physical examination data to the data caching module.
Step S20: and the data caching module generates a personal constitution data set according to the real-time physical examination data, the general activity data of the current user and the activity scene information, and sends the personal constitution data set to the data analysis module.
Step S30: and the data analysis module divides the health type of the current user when receiving the personal physique data set.
Step S40: and the data analysis module sends the personal physique data set to a professional doctor end corresponding to the health type according to the division result, and generates a health guidance strategy according to evaluation information fed back by the professional doctor end.
Step S50: and the data analysis module combines the personal physique data set according to the health guidance strategy to determine the physique health analysis result of the current user.
According to the embodiment, after the real-time physical examination data of the current user are obtained according to the information bar codes, the personal physical constitution data set is generated by combining the general activity data and the activity scene data, the current user can be divided into health types based on the personal physical constitution data set, and the health type is distributed to the professional doctor end corresponding to the health type according to the division result so as to obtain a health guidance strategy, personalized health guidance schemes are provided for different users, finally, the physical health analysis result of the current user is obtained by combining the personal physical constitution data set.
Other embodiments or specific implementation manners of the student physical health monitoring data analysis method of the present invention may refer to the above method embodiments, and will not be described herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments. From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read-only memory/random-access memory, magnetic disk, optical disk), comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. A student physical health monitoring data analysis system, the system comprising: the device comprises a data acquisition module, a data cache module and a data analysis module;
the data acquisition module is used for acquiring real-time physical examination data of the current user based on the information bar code corresponding to the current user and sending the real-time physical examination data to the data caching module;
the data caching module is used for generating a personal constitution data set according to the real-time physical examination data, the general activity data of the current user and the activity scene information, and sending the personal constitution data set to the data analysis module;
the data analysis module is used for dividing the health type of the current user when the personal physique data set is received;
the data analysis module is further used for sending the personal physique data set to a professional doctor end corresponding to the health type according to the division result, and generating a health guidance strategy according to evaluation information fed back by the professional doctor end;
the data analysis module is further used for determining a physical health analysis result of the current user according to the health guidance strategy and the personal physical data set.
2. The student physical health monitoring data analysis system of claim 1, wherein the system further comprises: a visual display module;
the data analysis module is further used for sending the physical health analysis result of the current user to the visual display module;
the visual display module is used for determining the authority level according to the source of the query instruction when the query instruction is received, and acquiring a chart template of the authority level;
the visual display template is further used for displaying the physical health analysis result of the current user based on the chart template.
3. The student physical health monitoring data analysis system of claim 2, wherein the visual display module is further configured to extract a field identifier in a query instruction when the query instruction is received;
the visual display module is also used for matching the field identification in a standard authority library;
the visual display module is further used for determining the authority level of the query instruction according to the matching item when the matching item exists in the standard authority library;
the visual display module is further used for determining a corresponding initial chart template according to the authority level;
The visual display module is further used for updating the initial chart template according to the physical health analysis result of the current user, and generating and displaying a physical health analysis table of the current user.
4. The student physical health monitoring data analysis system of claim 1, wherein the data caching module comprises a data query sub-module and a data encryption sub-module which are connected;
the data query sub-module is used for querying general activity data and activity scene information of the current user when the real-time physical examination data are received;
the data query sub-module is used for forming the real-time physical examination data, the general activity data and the activity scene information into a personal constitution data set of the current user and sending the personal constitution data set to the data encryption sub-module;
and the data encryption sub-module is used for encrypting the personal constitution data set by adopting an encryption algorithm when the personal constitution data set is acquired, and sending the encrypted personal constitution data set to the data analysis module.
5. The student physical fitness monitoring data analysis system of claim 4, wherein the data analysis module is further configured to determine a corresponding decryption algorithm upon receipt of the encrypted personal physical fitness data set, and obtain the decrypted personal physical fitness data set based on the decryption algorithm;
The data analysis module is further configured to obtain an activity scene category when the personal constitution data set is received, and obtain a personal constitution standard score of the current user through a preset standard parameter generation model, where the activity scene category includes: safety scenes, hidden danger scenes and risk scenes;
the data analysis module is further used for determining the health type of the current user according to the personal physique standard score and the activity scene category.
6. The student physical health monitoring data analysis system of claim 5, wherein the preset standard parameter generation model is trained by a training set consisting of historical physical examination data and historical activity data, and the preset standard parameter generation model is used for extracting the embedded features of the current user and the individual physical standard score corresponding to the embedded features from the individual physical data set.
7. The student physical fitness monitoring data analysis system of claim 5, wherein the fitness type comprises a standard type, a type of interest, and a risk type;
the data analysis module is further used for sending the personal physique data set to a professional doctor end corresponding to the health type according to the health type of the current user;
The data analysis module is further used for receiving evaluation information fed back by the professional doctor end when the health type of the current user is a standard type, and generating a health behavior mode of the current user;
the data analysis module is further used for receiving evaluation information fed back by the professional doctor end when the health type of the current user is the type to be attended to, and obtaining an attended health risk item of the current user and a corresponding prevention strategy;
and the data analysis module is also used for receiving the evaluation information fed back by the professional doctor end when the health type of the current user is a risk type, obtaining the health risk item of the current user and generating warning information.
8. The student physical health monitoring data analysis system of claim 1, wherein the data acquisition module is further configured to acquire each item of physical examination data of a current user based on an information barcode of the current user;
the data acquisition module is also used for unifying the formats of all acquired physical examination data and deleting blank data to obtain real-time physical examination data of the current user;
the data acquisition module is further used for sending the real-time physical examination data to the data caching module.
9. The student physical health monitoring data analysis system of claim 1, wherein the system further comprises: a bar code generation module;
the bar code generation module is used for acquiring an input information table of the user to be acquired in the current scene and generating an information bar code of each user to be acquired;
the bar code generation module is further used for determining a current user and acquiring an information bar code corresponding to the current user when the user to be acquired passes the face authentication.
10. A student physical health monitoring data analysis method based on the student physical health monitoring data analysis system of any one of claims 1 to 7, characterized in that the method comprises the steps of:
the data acquisition module acquires real-time physical examination data of the current user based on an information bar code corresponding to the current user, and sends the real-time physical examination data to the data caching module;
the data caching module generates a personal constitution data set according to the real-time physical examination data, the general activity data of the current user and the activity scene information, and sends the personal constitution data set to the data analysis module;
the data analysis module divides the health type of the current user when receiving the personal physique data set;
The data analysis module sends the personal physique data set to a professional doctor end corresponding to the health type according to the division result, and generates a health guidance strategy according to evaluation information fed back by the professional doctor end; and the data analysis module combines the personal physique data set according to the health guidance strategy to determine the physique health analysis result of the current user.
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