A visual analysis method and system for adolescent physical fitness data

Abstract

The present invention relates to the field of electronic digital data processing technology, specifically to a method and system for visual analysis of adolescent physical fitness data, the method comprising: obtaining the number of physical fitness data collection times, geographical environment statistical data and adolescent physical fitness data collected in each target area, comprehensively analyzing to obtain data collection demand evaluation indicators for each target area, and screening to obtain each data collection area; comprehensively analyzing to obtain data collection demand evaluation indicators for each data collection area; collecting data for each data collection area, and providing data collection quality evaluation feedback. The present invention provides a method and system for visual analysis of adolescent physical fitness data, which comprehensively incorporates regional environmental changes, data collection demand degree and demand, and data quality into a data visualization analysis system, which helps to improve the accuracy and reliability of data collection and ensure the comprehensiveness and timeliness of data collection.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a visual analysis method and system for teenagers' physical fitness data. Background Art

[0002] With the progress of society and the improvement of people's living standards, people pay more and more attention to health. Parents, schools and all sectors of society are paying more and more attention to the physical health of teenagers, and are using scientific methods to understand and improve the physical fitness of teenagers. The rapid development of computer technology, especially the rise of big data, cloud computing, artificial intelligence and other technologies, has provided strong technical support for the collection, processing and analysis of physical fitness data of teenagers.

[0003] For example, the invention patent with announcement number: CN112256789B is a method and device for intelligent data visualization analysis, which includes: building a chart knowledge base, which contains knowledge structure information of multiple chart types; obtaining a data set to be analyzed, extracting field information of samples in the data set and relationship information between fields; calculating the matching quality score of the data set to be analyzed and any chart type in the chart knowledge base based on the field information of samples in the data set and relationship information between fields; and outputting the chart configuration of the data set to be analyzed based on the matching quality score.

[0004] For example, the invention patent with announcement number: CN116304249B is a data visualization analysis method and system, which includes: displaying first target data of a first area, the first target data is obtained based on transformation information of the target object in the first area, the transformation information is used to characterize that the first target object in the target object changes from a limited working state in the second area to a non-limited working state, and the first area includes multiple second areas; obtaining first user operation information of the user on the first target data, and locating several second areas adjacent to the first target object according to the first user operation information and the operation range of the first active target, and the first active target is related to the transformation information.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, it was found that the above-mentioned technology has at least the following technical problems: the current visualization analysis method still remains at the basic descriptive statistical analysis level, lacks more in-depth data mining and association analysis, and often in the analysis of adolescent physical fitness data, the monitoring and analysis of the adolescent physical fitness data collection process is not comprehensive, resulting in data collection work may be carried out without clear requirements, which may cause waste of resources or inaccurate data analysis. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method and system for visualizing the physical fitness data of adolescents, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a method for visual analysis of teenagers' physical fitness data, including: S1. Obtaining teenagers' physical fitness data analysis areas, marking them as target areas, and obtaining the number of physical data collection times, geographical environment statistical data and the physical fitness data of teenagers collected each time for each target area.

[0008] S2. Based on the number of physical fitness data collection in each target area, geographical environment statistical data and the physical fitness data of teenagers collected each time, a comprehensive analysis is conducted to obtain the data collection demand level assessment indicators for each target area.

[0009] S3. Based on the data collection demand evaluation indicators of each target area, each data collection area is screened, and based on the number of physical data collections in each data collection area and the physical data of teenagers collected each time, a comprehensive analysis is performed to obtain the data collection demand evaluation indicators of each data collection area.

[0010] S4. Collect data for each data collection area according to the data collection demand evaluation index of each data collection area, and provide data collection quality evaluation feedback.

[0011] As a further method, the number of physical data collections for each target area, geographical environment statistical data and the physical data of teenagers collected at each time, wherein the geographical environment statistical data include the historical average temperature, the historical average suspended particle concentration and the historical average vegetation coverage rate, as well as the temperature at the current monitoring time point, the suspended particle concentration at the current monitoring time point and the vegetation coverage rate at the current monitoring time point.

[0012] The physical fitness data of the teenagers collected each time include the physical fitness index values ​​of each teenager and the average values ​​of the physical fitness index values ​​of the teenagers collected each time. The physical fitness indicators include vital capacity, vision, body fat percentage and sports performance.

[0013] As a further method, the comprehensive analysis obtains the data collection demand degree evaluation index of each target area. The specific analysis process is: based on the historical average temperature, historical average suspended particle concentration and historical average vegetation coverage of each target area and the temperature at the current monitoring time point, the suspended particle concentration at the current monitoring time point and the vegetation coverage at the current monitoring time point, the allowable deviation temperature, the allowable deviation suspended particle concentration and the allowable deviation vegetation coverage are extracted from the visualization system database, and the environmental change characteristic values ​​of each target area are obtained through comprehensive analysis. Based on the number of physical data collections of each target area and the physical data of teenagers collected each time, the critical number of collections is extracted from the visualization system database, and the data collection demand degree evaluation index of each target area is obtained through comprehensive analysis.

[0014] As a further method, the screening obtains each data collection area, and the specific screening process is: extracting the data collection need degree assessment index threshold of each target area from the visualization system database, comparing the data collection need degree assessment index of each target area with the data collection need degree assessment index threshold of each target area, if the data collection need degree assessment index of a target area is higher than or equal to the data collection need degree assessment index threshold of the target area, then the target area is marked as a data collection area, if the data collection need degree assessment index of a target area is lower than the data collection need degree assessment index threshold of the target area, then data collection is not performed on the target area.

[0015] As a further method, the comprehensive analysis obtains data collection demand evaluation indicators for each data collection area. The specific analysis process is: obtaining the number of teenagers in each data collection area, based on the number of physical data collection times of each data collection area and the physical data of teenagers collected each time, and extracting the critical number of teenagers from the visualization system database, the comprehensive analysis obtains the data collection demand evaluation indicators for each data collection area. The data collection demand evaluation indicators for each data collection area are used to quantitatively evaluate the data collection demand of each data collection area, and provide data basis for data collection in each data collection area.

[0016] As a further method, data collection is performed on each data collection area, and the specific process is: extracting the data collection demand evaluation index interval of each data collection area from the visualization system database, comparing the data collection demand evaluation index of each data collection area with the data collection demand evaluation index interval of each data collection area one by one, matching to obtain the data collection amount of each data collection area, and performing data collection on each data collection area according to the data collection amount of each data collection area.

[0017] As a further method, the data collection quality assessment feedback specifically includes: obtaining the data collection time and actual data collection amount of each data collection area, and obtaining the estimated data collection time of each data collection area according to the data collection amount of each data collection area, extracting the allowable collection time difference and the allowable deviation data collection amount from the visualization system database, and obtaining the data quality assessment index of each data collection area through comprehensive analysis based on the physical index values ​​of each adolescent and the data collection amount of each data collection area. The data quality assessment index of each data collection area is used to quantitatively evaluate the data quality of data collected in each data collection area, providing a basis for data collection quality assessment feedback.

[0018] As a further method, the data collection quality assessment feedback also includes: extracting a data quality assessment index threshold for each data collection area from a visualization system database, comparing the data quality assessment index of each data collection area with the data quality assessment index threshold for each data collection area; if the data quality assessment index of a data collection area is higher than or equal to the data quality assessment index threshold for the data collection area, the data quality of the data collection area is assessed as qualified, and feedback is provided for the data of each data collection area; if the data quality assessment index of a data collection area is lower than the data quality assessment index threshold for the data collection area, the data quality of the data collection area is assessed as unqualified, and data collection is performed again for the data collection area.

[0019] As a further method, the data collection demand evaluation index of each data collection area is specifically expressed as:

[0020] ;

[0021] In the formula, represents the data collection demand evaluation index of the t-th data collection area, t represents the number of each data collection area, , g represents the total number of data collection areas, represents the number of physical data collection times in the tth data collection area, represents the critical acquisition times, represents the number of teenagers in the tth data collection area, represents the critical number of teenagers, It represents the value of the nth physical index of the tth data collection area in the rth data collection, and r represents the number of each data collection time. , h represents the total number of data collection, n represents the number of each type of physical index, , m represents the total number of types of physical indicators, Indicates the data collection demand influencing factor of the data collection area corresponding to the preset number of physical fitness data collection times, Indicates the data collection demand influencing factor of the data collection area corresponding to the preset number of teenagers, Indicates the data collection demand influencing factor of the data collection area corresponding to the preset physical data value.

[0022] The second aspect of the present invention provides a system for a method for visually analyzing physical fitness data of adolescents, characterized in that it includes: a data acquisition module, a data acquisition requirement assessment module, a data acquisition requirement assessment module and a data acquisition quality assessment feedback module.

[0023] The data collection module is used to obtain the analysis areas of teenagers' physical fitness data, mark them as target areas, and obtain the number of physical fitness data collections, geographical environment statistical data and the physical fitness data of teenagers collected each time for each target area.

[0024] The data collection need assessment module is used to obtain data collection need assessment indicators for each target area through comprehensive analysis based on the number of physical fitness data collections for each target area, geographical environment statistical data and physical fitness data of teenagers collected each time.

[0025] The data collection demand assessment module is used to screen each data collection area according to the data collection demand assessment index of each target area, and to obtain the data collection demand assessment index of each data collection area through comprehensive analysis based on the number of physical data collections in each data collection area and the physical data of teenagers collected each time.

[0026] The data collection quality assessment feedback module is used to collect data from each data collection area according to the data collection demand assessment index of each data collection area, and to provide data collection quality assessment feedback.

[0027] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0028] (1) The present invention provides a visualization analysis method and system for adolescent physical fitness data, which comprehensively incorporates regional environmental changes, the degree and amount of data collection needs, and the data quality of the data collection area into the visualization analysis system of adolescent physical fitness data in various regions, thereby helping to improve the accuracy and reliability of data collection, ensure the comprehensiveness and timeliness of data collection, and provide strong support for subsequent visualization display.

[0029] (2) This invention demonstrates the relationship between regional environmental changes and the physical health of adolescents through visual analysis. This helps to understand how environmental factors affect the growth, development and physical condition of adolescents, thereby helping to formulate corresponding environmental improvement and health promotion strategies.

[0030] (3) The present invention incorporates the deviation value between the data collection time and the expected data collection time, the deviation value between the data collection amount and the actual data collection amount, and the deviation value of each adolescent physical fitness index value into the data quality evaluation system, which can discover the weak links and potential problems in the data collection process, and can also encourage data collection personnel to pay more attention to the accuracy and timeliness of data collection, so that the data can more accurately and reliably reflect the actual situation.

[0031] (4) The present invention visualizes the degree and amount of demand for regional data collection, and can clearly show which regions and time periods have more important and urgent data. This helps guide the rational layout and priority setting of future data collection work and ensure the effective use of data resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0033] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0034] Figure 2 It is a schematic diagram of system module connection of the present invention.

[0035] Figure 3 It is a schematic diagram of the functional relationship between the data collection deviation duration of each data collection area and the data quality evaluation index of each data collection area involved in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0037] Reference Figure 1 As shown, the first aspect of the present invention provides a method for visualizing physical fitness data of teenagers, including: S1. Obtaining analysis areas for physical fitness data of teenagers, marking them as target areas, and obtaining the number of physical fitness data collection times, geographical environment statistical data and physical fitness data of teenagers collected each time for each target area.

[0038] Specifically, the number of physical data collections for each target area, geographical environment statistical data and the physical data of teenagers collected at each time, wherein the geographical environment statistical data include the historical average temperature, the historical average suspended particle concentration and the historical average vegetation coverage rate, as well as the temperature at the current monitoring time point, the suspended particle concentration at the current monitoring time point and the vegetation coverage rate at the current monitoring time point.

[0039] It should be explained that the historical average temperature, suspended particle concentration and vegetation coverage refer to the average values ​​of temperature, suspended particle concentration and vegetation coverage of each historical monitoring point.

[0040] It should be explained that in this embodiment, temperature data can be obtained by real-time monitoring using meteorological monitoring stations in various regions. The concentration of suspended particles can be monitored in real time using air quality monitoring stations in various regions. Satellite remote sensing images of various target areas can be obtained through a geographic information system, and the reflectance values ​​of the red band (RED) and the near infrared band (Near Infrared) can be extracted from them to calculate the normalized vegetation index (NDVI) to represent the vegetation coverage rate. The expression of the normalized vegetation index is: , where RED represents the red light band, Represents the near-infrared band.

[0041] The physical fitness data of the teenagers collected each time include the physical fitness index values ​​of each teenager and the average values ​​of the physical fitness index values ​​of the teenagers collected each time. The physical fitness indicators include vital capacity, vision, body fat percentage and sports performance.

[0042] It should be explained that, in this embodiment, the lung capacity of the adolescent can be measured by a spirometer, the vision of the adolescent can be obtained by a vision test conducted by a professional ophthalmological institution, the body fat percentage of the adolescent can be obtained by calculating the adolescent's weight (kg) divided by the square of the height (m), and the adolescent's sports performance can be obtained through school physical examination data.

[0043] S2. Based on the number of physical fitness data collection in each target area, geographical environment statistical data and the physical fitness data of teenagers collected each time, a comprehensive analysis is conducted to obtain the data collection demand level assessment indicators for each target area.

[0044] Specifically, the comprehensive analysis obtains the data collection demand degree evaluation index of each target area. The specific analysis process is: according to the historical average temperature, historical average suspended particle concentration and historical average vegetation coverage of each target area and the temperature at the current monitoring time point, the suspended particle concentration at the current monitoring time point and the vegetation coverage at the current monitoring time point, the allowable deviation temperature, the allowable deviation suspended particle concentration and the allowable deviation vegetation coverage are extracted from the visualization system database, and the environmental change characteristic values ​​of each target area are obtained through comprehensive analysis. According to the number of physical data collections of each target area and the physical data of teenagers collected each time, and the critical number of collections is extracted from the visualization system database, the data collection demand degree evaluation index of each target area is obtained through comprehensive analysis.

[0045] It should be understood that the environmental change characteristic values ​​of each target area are quantitative assessment data obtained by comprehensively analyzing the historical average temperature, historical average suspended particle concentration and historical average vegetation coverage of each target area, as well as the temperature at the current monitoring time point, the suspended particle concentration at the current monitoring time point and the vegetation coverage at the current monitoring time point. They are used to quantitatively assess the degree of environmental change in each target area within a preset time monitoring period, and provide a data basis for assessing the degree of data collection needs for each target area.

[0046] In a specific embodiment, the numerical expression of the environmental change characteristic value of each target area is:

[0047] ;

[0048] In the formula, represents the environmental change characteristic value of the i-th target area, i represents the number of each target area, , s represents the total number of target areas, e represents a natural constant, represents the temperature of the monitoring point in the i-th target area at the current time, represents the suspended matter concentration of the monitoring point in the i-th target area at the current time, represents the vegetation coverage rate of the i-th target area at the monitoring point at the current time, represents the historical average temperature of the i-th target area, represents the historical average suspended matter concentration of the i-th target area, represents the historical average vegetation coverage of the i-th target area, Indicates the allowable deviation temperature, Indicates the allowable deviation of suspended solids concentration, Indicates the allowable deviation vegetation coverage, Indicates the environmental change characteristic influencing factor corresponding to the preset temperature, Indicates the environmental change characteristic influencing factor corresponding to the preset suspended matter particle concentration, Indicates the influencing factor of environmental change characteristics corresponding to the preset vegetation coverage rate.

[0049] It needs to be explained that, in this embodiment, the greater the deviation between the temperature at the current monitoring time point, the suspended particle concentration at the current monitoring time point and the vegetation coverage rate at the current monitoring time point and the historical average temperature, the historical average suspended particle concentration and the historical average vegetation coverage rate, the greater the corresponding environmental change characteristic value of the target area, indicating that the greater the degree of environmental change in the target area, that is, the greater the impact on the physical fitness of young people in the area.

[0050] It should be explained that in this embodiment is the environmental change characteristic influencing factor corresponding to the preset temperature, is the environmental change characteristic influencing factor corresponding to the preset suspended matter particle concentration, The environmental change characteristic influencing factors corresponding to the preset vegetation coverage rate respectively represent the numerical values ​​of the influence degree of the unit values ​​of temperature, suspended particle concentration and vegetation coverage rate on the regional environmental change. When used, the environmental change characteristic influencing factors corresponding to the temperature, the environmental change characteristic influencing factors corresponding to the suspended particle concentration and the environmental change characteristic influencing factors corresponding to the vegetation coverage rate can be directly obtained from the visualization system database. The corresponding relationship can be a pre-set mapping relationship. For example, the temperature, suspended particle concentration and vegetation coverage rate of the monitoring point at the current time are respectively mapped with the regional environmental influencing factors corresponding to the temperature, suspended particle concentration and vegetation coverage rate preset in the visualization system database to form a mapping set. The real-time temperature, suspended particle concentration and vegetation coverage rate are input into the mapping set to obtain the environmental change characteristic influencing factors corresponding to the temperature, the environmental change characteristic influencing factors corresponding to the suspended particle concentration and the environmental change characteristic influencing factors corresponding to the vegetation coverage rate. The mapping relationship can be one-to-one or many-to-one. The value range of the above-mentioned influencing factors is between 0 and 1.

[0051] It needs to be explained that temperature is an important factor affecting the rate of atmospheric chemical reactions. Under high temperature conditions, the chemical reaction rate between certain pollutants in the atmosphere (such as volatile organic compounds (VOCs), nitrogen oxides (NOx), etc.) will accelerate, and more secondary pollutants (such as ozone O3, particulate matter PM, etc.) may be generated. Temperature also affects the diffusion capacity of suspended particulate matter. In hot weather, the concentration of suspended particles may rise, aggravating the degree of regional air pollution and having a negative impact on residents' health, ecological environment and climate system. Temperature is one of the key factors affecting plant growth and development. The appropriate temperature range helps plants to carry out physiological activities such as photosynthesis, respiration and transpiration, thereby promoting the growth of vegetation and the increase of coverage. The increase in vegetation coverage can reduce the surface temperature through transpiration and regulate the local climate. At the same time, vegetation can also absorb particulate matter in the air, and the leaves and branches of plants can intercept particulate matter in the atmosphere (such as PM10, PM2.5, etc.). The increase in vegetation coverage can expand the spatial scope and time scale of this purification effect, thereby effectively reducing the concentration of suspended particles and reducing the harm of air pollution to residents' health and the ecological environment. Comprehensive analysis of the temperature, suspended particle concentration and vegetation coverage of the target area can make the analysis of regional environmental changes more comprehensive and accurate.

[0052] It should be understood that the data collection need level assessment index for each target area is a quantitative assessment data obtained by comprehensively analyzing the environmental change characteristic values, the number of physical data collection times and the physical data of teenagers collected each time for each target area. It is used to quantitatively assess the data collection need level for each target area and provide a data basis for analyzing whether data collection is needed in each target area.

[0053] In a specific embodiment, the numerical expression of the data collection demand evaluation index of each target area is:

[0054] ;

[0055] In the formula, represents the data collection demand evaluation index of the i-th target area, represents the characteristic value of environmental change in the i-th target area, represents the number of times physical fitness data is collected in the i-th target area, It represents the average value of the nth physical index of the i-th target area in the r-th data collection, and r represents the number of each data collection time. , h represents the total number of data collection, n represents the number of each type of physical index, , m represents the total number of types of physical indicators, represents the critical acquisition times, Indicates the impact factor of the data collection demand level of the target area corresponding to the preset environmental change characteristic value, Indicates the impact factor of the data collection demand level of the target area corresponding to the preset number of physical fitness data collection times. Indicates the impact factor of the data collection demand level of the target area corresponding to the preset physical data value.

[0056] It should be explained that, in this embodiment, the larger the environmental change characteristic value of the target area and the numerical deviation value of the physical index of each data collection, and the smaller the number of physical data collection times, the larger the corresponding target area's data collection demand evaluation index, indicating that the target area has a greater data collection demand.

[0057] It should be explained that the number of times the physical fitness data of each target area is collected in this embodiment refers to the number of times the visualization system collects the physical fitness data of teenagers in each target area in the historical period.

[0058] It should be explained that in this embodiment The impact factor of the data collection demand level of the target area corresponding to the preset environmental change characteristic value, The influencing factor of the data collection demand level of the target area corresponding to the preset number of physical fitness data collection times, The data collection requirement degree influencing factors of the target area corresponding to the preset physical data values ​​respectively represent the values ​​of the influence of the environmental change characteristic value, the number of physical data collection times and the physical data unit value on the data collection requirement degree of the target area. When used, the data collection requirement degree influencing factors of the target area corresponding to the environmental change characteristic value, the data collection requirement degree influencing factors of the target area corresponding to the number of physical data collection times and the data collection requirement degree influencing factors of the target area corresponding to the physical data value can be directly obtained from the visualization system database. The corresponding relationship can be a preset mapping relationship. For example, the environmental change characteristic value, the number of physical data collection times and the physical data value respectively form a mapping set with the environmental change characteristic value, the number of physical data collection times and the data collection requirement degree influencing factors of the target area corresponding to the physical data value preset in the visualization system database. The real-time environmental change characteristic value, the number of physical data collection times and the physical data value are input into the mapping set to obtain the data collection requirement degree influencing factors of the target area corresponding to the environmental change characteristic value, the data collection requirement degree influencing factors of the target area corresponding to the number of physical data collection times and the data collection requirement degree influencing factors of the target area corresponding to the physical data value. The mapping relationship can be one-to-one or a many-to-one relationship. The value range of the above impact factors is between 0 and 1.

[0059] It should be explained that environmental changes in the target areas include deterioration in air quality, especially the increase in the concentration of particulate matter such as PM2.5 and PM10, which will significantly increase the risk of adolescents suffering from respiratory diseases and seriously affect their physical health. The immune system of adolescents has not yet fully developed. Air pollution will increase their immune burden, leading to frequent susceptible diseases. For adolescents who are exposed to air pollution for a long time, their growth and development indicators such as height, weight, and chest circumference may be adversely affected, and physiological functions such as lung capacity and muscle strength may also decline. Extreme temperatures (such as high or low temperatures) may affect adolescents' physiological functions, such as temperature regulation and cardiovascular function. In hot weather, adolescents are prone to health problems such as heat stroke and heat stroke; while low temperatures may cause colds, joint pain, etc. Temperature changes may also affect adolescents' participation in sports activities. In extreme weather conditions, outdoor sports activities may be restricted, resulting in reduced exercise for adolescents, which in turn affects their physical health. Increasing vegetation coverage can help improve air quality and have a positive impact on adolescents' respiratory health and immune system. Green vegetation can also provide young people with a comfortable learning environment and living space, help relieve study pressure and psychological tension, and promote mental health development.

[0060] S3. Based on the data collection demand evaluation indicators of each target area, each data collection area is screened, and based on the number of physical data collections in each data collection area and the physical data of teenagers collected each time, a comprehensive analysis is performed to obtain the data collection demand evaluation indicators of each data collection area.

[0061] Specifically, the screening obtains each data collection area, and the specific screening process is: extracting the data collection need degree assessment index threshold of each target area from the visualization system database, comparing the data collection need degree assessment index of each target area with the data collection need degree assessment index threshold of each target area, if the data collection need degree assessment index of a target area is higher than or equal to the data collection need degree assessment index threshold of the target area, then the target area is marked as a data collection area, if the data collection need degree assessment index of a target area is lower than the data collection need degree assessment index threshold of the target area, then data collection is not performed on the target area.

[0062] Furthermore, the comprehensive analysis obtains data collection demand evaluation indicators for each data collection area. The specific analysis process is: obtaining the number of teenagers in each data collection area, based on the number of physical data collection times of each data collection area and the physical data of teenagers collected each time, and extracting the critical number of teenagers from the visualization system database, and comprehensively analyzing to obtain the data collection demand evaluation indicators for each data collection area. The data collection demand evaluation indicators for each data collection area are used to quantitatively evaluate the data collection demand of each data collection area, and provide data basis for data collection in each data collection area.

[0063] It should be explained that the critical number of teenagers refers to the lowest number of teenagers in the data collection area. If the number of teenagers in a data collection area is lower than this value, data will not be collected for this area. In this embodiment, the number of teenagers in each data collection area is higher than the critical number of teenagers.

[0064] In a specific embodiment, the numerical expression of the data collection demand evaluation index of each data collection area is:

[0065] ;

[0066] In the formula, represents the data collection demand evaluation index of the t-th data collection area, t represents the number of each data collection area, , g represents the total number of data collection areas, represents the number of physical data collection times in the tth data collection area, represents the critical acquisition times, represents the number of teenagers in the tth data collection area, represents the critical number of teenagers, It represents the value of the nth physical index of the tth data collection area in the rth data collection, and r represents the number of each data collection time. , h represents the total number of data collection, n represents the number of each type of physical index, , m represents the total number of types of physical indicators, Indicates the data collection demand influencing factor of the data collection area corresponding to the preset number of physical fitness data collection times, Indicates the data collection demand influencing factor of the data collection area corresponding to the preset number of teenagers, Indicates the data collection demand influencing factor of the data collection area corresponding to the preset physical data value.

[0067] It should be explained that, in this embodiment, when the number of physical data collection in the data collection area is smaller, the number of teenagers in the data collection area is greater and the deviation value of the physical index values ​​of each data collection is greater, the data collection demand evaluation index of the corresponding data collection area is greater, indicating that the data collection demand of the data collection area is greater.

[0068] It should be explained that in this embodiment The data collection demand influencing factor of the data collection area corresponding to the preset number of physical data collection times, The data collection demand influencing factor for the data collection area corresponding to the preset number of teenagers, The data collection demand influencing factors of the data collection area corresponding to the preset physical data values ​​respectively represent the numerical values ​​of the influence of the physical data collection times, the number of teenagers and the physical data unit value on the data collection demand of the data collection area. When used, the data collection demand influencing factors of the data collection area corresponding to the physical data collection times, the data collection demand influencing factors of the data collection area corresponding to the number of teenagers and the data collection demand influencing factors of the data collection area corresponding to the physical data values ​​can be directly obtained from the visualization system database. The corresponding relationship can be a preset mapping relationship. For example, the physical data collection times, the number of teenagers and the physical data value respectively form a mapping set with the physical data collection times, the number of teenagers and the data collection demand influencing factors of the data collection area corresponding to the physical data collection times, the number of teenagers and the physical data values ​​preset in the visualization system database. The real-time physical data collection times, the number of teenagers and the physical data values ​​are input into the mapping set to obtain the data collection demand influencing factors of the data collection area corresponding to the physical data collection times, the data collection demand influencing factors of the data collection area corresponding to the number of teenagers and the data collection demand influencing factors of the data collection area corresponding to the physical data values. The mapping relationship can be one-to-one or many-to-one. The value range of the above impact factors is between 0 and 1.

[0069] S4. Collect data for each data collection area according to the data collection demand evaluation index of each data collection area, and provide data collection quality evaluation feedback.

[0070] Specifically, the data collection for each data collection area is carried out, and the specific process is: extracting the data collection demand assessment index interval of each data collection area from the visualization system database, comparing the data collection demand assessment index of each data collection area with the data collection demand assessment index interval of each data collection area one by one, matching to obtain the data collection amount of each data collection area, and collecting data for each data collection area according to the data collection amount of each data collection area.

[0071] Furthermore, the process of matching to obtain the data collection volume of each data collection area is to construct a mapping set between the data collection demand evaluation index interval and the data collection volume of each data collection area, and the corresponding relationship is a one-to-one relationship. The data collection demand evaluation index of a data collection area is compared with the data collection demand evaluation index interval of the data collection area to obtain the interval in which the data collection demand evaluation index of the data collection area is located, and the data collection volume of the data collection area is obtained according to the mapping set.

[0072] Specifically, the data collection quality assessment feedback is performed, and the specific assessment process is: obtaining the data collection time and actual data collection amount of each data collection area, and matching the data collection amount of each data collection area to obtain the estimated data collection time of each data collection area, extracting the allowable collection time difference and the allowable deviation data collection amount from the visualization system database, and according to the physical index values ​​of each adolescent and the data collection amount of each data collection area, a comprehensive analysis is performed to obtain the data quality assessment index of each data collection area. The data quality assessment index of each data collection area is used to quantitatively evaluate the data quality of data collection in each data collection area, and provide a basis for data collection quality assessment feedback.

[0073] In a specific embodiment, the numerical expression of the data quality evaluation index of each data collection area is:

[0074] ;

[0075] In the formula, represents the data quality assessment index of the tth data collection area, represents the data collection time of the tth data collection area, represents the estimated data collection time for the tth data collection area, Indicates that the time difference of collection is allowed. represents the value of the nth physical index of the qth adolescent in the tth data collection area, q represents the number of each adolescent, , d represents the total number of adolescents, n represents the number of each type of physical fitness index, , m represents the total number of types of physical indicators, represents the data collection amount of the tth data collection area, represents the actual data collection amount of the tth data collection area, Indicates the amount of allowable deviation data collection, Indicates the influencing factor of the data quality of the data collection area corresponding to the preset data collection time, Indicates the influencing factor of the data quality of the data collection area corresponding to the preset adolescent physical fitness data value, Indicates the influencing factor of the data quality of the data collection area corresponding to the preset data collection amount.

[0076] It should be explained that, in this embodiment, the greater the deviation between the data collection time of the data collection area and the expected data collection time, the numerical deviation between the physical fitness indicators of each teenager, and the deviation between the data collection amount of the data collection area and the actual data collection amount of the data collection area, the smaller the data quality evaluation index of the corresponding data collection area, indicating that the data quality of the data collected in the data collection area is lower.

[0077] It should be explained that in this embodiment The influencing factor of the data quality of the data collection area corresponding to the preset data collection time, The influencing factor of the data quality of the data collection area corresponding to the preset adolescent physical fitness data value, The influencing factors of the data quality of the data collection area corresponding to the preset data collection amount respectively represent the numerical values ​​of the degree of influence of the data collection time, the physical fitness data of the teenagers and the unit value of the data collection amount on the data quality of the data collection area. When used, the influencing factors of the data quality of the data collection area corresponding to the data collection time, the influencing factors of the data quality of the data collection area corresponding to the physical fitness data value of the teenagers and the influencing factors of the data quality of the data collection area corresponding to the data collection amount can be directly obtained from the visualization system database. The corresponding relationship can be a preset mapping relationship. For example, the data collection time, the physical fitness data value of the teenagers and the data collection amount are respectively mapped with the data collection time, the physical fitness data value of the teenagers and the data collection amount preset in the visualization system database to form a mapping set. The real-time data collection time, the physical fitness data value of the teenagers and the data collection amount are input into the mapping set to obtain the influencing factors of the data quality of the data collection area corresponding to the data collection time, the influencing factors of the data quality of the data collection area corresponding to the physical fitness data value of the teenagers and the influencing factors of the data quality of the data collection area corresponding to the data collection amount. The mapping relationship can be one-to-one or many-to-one. The value range of the above influencing factors is between 0 and 1.

[0078] In a specific embodiment, the allowed collection time difference is set to 1 day, the allowed deviation data collection amount is set to 10, the impact factor of the data quality of the data collection area corresponding to the data collection time is set to 0.6, the impact factor of the data quality of the data collection area corresponding to the adolescent physical fitness data value is set to 0.3, and the impact factor of the data quality of the data collection area corresponding to the data collection amount is set to 1.

[0079] It should be understood that if Figure 3As shown, curve a represents the relationship between the data collection deviation time of each data collection area and the data quality evaluation index of each data collection area when the physical fitness value deviation value of each teenager in each data collection area is 0.51 and the data collection amount deviation value is 1. Curve b represents the relationship between the data collection deviation time of each data collection area and the data quality evaluation index of each data collection area when the physical fitness value deviation value of each teenager in each data collection area is 0.76 and the data collection amount deviation value is 2. Curve c represents the relationship between the data collection deviation time of each data collection area and the data quality evaluation index of each data collection area when the physical fitness value deviation value of each teenager in each data collection area is 0.99 and the data collection amount deviation value is 3.

[0080] It needs to be explained that the accuracy of data collection time and collection volume is the basis of data quality. When the deviation between the data collection time and the expected collection time and the deviation between the data collection volume and the actual data collection volume are large, it may mean that there are delays, irregularities or recording errors in the data collection process. Large time deviations may cause the data to fail to reflect the current situation in a timely manner, reducing the timeliness and value of the data. In time series data or data that need to be monitored regularly, time deviation values ​​can help identify potential problems in the data collection process, such as equipment failure, human negligence, etc. The deviation value of the physical fitness index of adolescents directly reflects the accuracy of data measurement. Large deviation values ​​may mean problems such as inaccurate calibration of measuring equipment, non-standard measurement methods or data recording errors. These problems will directly affect the scientificity and credibility of the data, and thus affect the conclusions and decisions based on these data.

[0081] It should be explained that this embodiment incorporates the deviation value between the data collection time and the expected data collection time, the deviation value between the data collection amount and the actual data collection amount, and the deviation value of each adolescent physical fitness index value into the data quality evaluation system, which can discover the weak links and potential problems in the data collection process and can also prompt data collectors to pay more attention to the accuracy and timeliness of data collection. By reducing these three deviation values, the quality of the data can be significantly improved, making the data more accurately and reliably reflect the actual situation.

[0082] Furthermore, the data collection quality assessment feedback is further evaluated as follows: a data quality assessment index threshold of each data collection area is extracted from the visualization system database, and the data quality assessment index of each data collection area is compared with the data quality assessment index of each data collection area; if the data quality assessment index of a data collection area is higher than or equal to the data quality assessment index threshold of the data collection area, the data quality of the data collection area is evaluated as qualified, and feedback is given to the data of the data collection area; if the data quality assessment index of a data collection area is lower than the data quality assessment index threshold of the data collection area, the data quality of the data collection area is evaluated as unqualified, and data collection is performed again on the data collection area.

[0083] The process of providing feedback on the data of each data collection area is as follows: for a data collection area that is evaluated as qualified, feedback information can be sent to relevant data collection personnel through the system interface, email, text message, etc., indicating that the data quality of the area has met the standards.

[0084] In a specific embodiment, the visualization system database is used to store relevant data in the process of visual analysis and evaluation of the physical fitness data of teenagers in the target area, specifically including the allowable deviation temperature, the allowable deviation suspended particle concentration and the allowable deviation vegetation coverage, the data collection requirement degree influencing factor of the target area corresponding to the environmental change characteristic value, the data collection requirement degree influencing factor of the target area corresponding to the number of physical data collection times and the data collection requirement degree influencing factor of the target area corresponding to the physical data value, as well as the data collection requirement evaluation index threshold of each data collection area and the data quality evaluation index threshold of each data collection area. The data extracted from the above embodiments can be integrated into the automation process of the visualization system through the application program interface to automatically obtain and update the data in the visualization system database.

[0085] It should be explained that the present invention shows the relationship between regional environmental changes (such as air quality, pollution index) and the physical health of adolescents through visual analysis. It helps to understand how environmental factors affect the growth and physical condition of adolescents, so as to help formulate corresponding environmental improvement and health promotion strategies. The present invention conducts a visual analysis of the demand degree and demand for regional data collection, and can clearly show which regions and time periods have more important and urgent data. This helps to guide the reasonable layout and priority setting of future data collection work and ensure the effective use of data resources. In the visualization process, by displaying the numerical deviation value of physical index, data quality problems such as measurement errors and missing data can be discovered in time. This helps to take timely measures to correct and improve, improve the accuracy and reliability of data, and provide strong support for subsequent analysis and decision-making. Through visual analysis, it is easier to find potential problems and trends in the physical data of adolescents in various regions, such as rising obesity rates and increased detection rates of poor eyesight. These findings help to take timely intervention measures to prevent the occurrence and development of physical health problems in adolescents.

[0086] Reference Figure 2 As shown, the second aspect of the present invention provides a visual analysis system for teenagers' physical fitness data, including: a system of a visual analysis method for teenagers' physical fitness data, characterized in that it includes: a data acquisition module, a data acquisition requirement degree assessment module, a data acquisition demand quantity assessment module and a data acquisition quality assessment feedback module.

[0087] The data collection module is used to obtain the analysis areas of teenagers' physical fitness data, mark them as target areas, and obtain the number of physical fitness data collections, geographical environment statistical data and the physical fitness data of teenagers collected each time for each target area.

[0088] The data collection need assessment module is used to obtain data collection need assessment indicators for each target area through comprehensive analysis based on the number of physical fitness data collections for each target area, geographical environment statistical data and physical fitness data of teenagers collected each time.

[0089] The data collection demand assessment module is used to screen each data collection area according to the data collection demand assessment index of each target area, and to obtain the data collection demand assessment index of each data collection area through comprehensive analysis based on the number of physical data collections in each data collection area and the physical data of teenagers collected each time.

[0090] The data collection quality assessment feedback module is used to collect data from each data collection area according to the data collection demand assessment index of each data collection area, and to provide data collection quality assessment feedback.

[0091] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A visual analysis method for adolescent physical fitness data, characterized in that: include: S1. Obtain the areas for analyzing physical fitness data of teenagers, mark them as target areas, and obtain the number of physical fitness data collection, geographical environment statistics and physical fitness data of teenagers collected in each time for each target area; S2. Based on the number of physical fitness data collection in each target area, geographical environment statistical data and physical fitness data of adolescents collected in each time, a comprehensive analysis is conducted to obtain the data collection demand assessment index for each target area; The comprehensive analysis obtains the data collection demand evaluation index of each target area. The specific analysis process is as follows: Based on the historical average temperature, historical average suspended particle concentration and historical average vegetation coverage of each target area, as well as the temperature, suspended particle concentration and vegetation coverage at the current monitoring time point, the allowable deviation temperature, allowable deviation suspended particle concentration and allowable deviation vegetation coverage are extracted from the visualization system database, and the environmental change characteristic values ​​of each target area are obtained through comprehensive analysis. Based on the number of physical fitness data collections of each target area and the physical fitness data of teenagers collected each time, the critical number of collections is extracted from the visualization system database, and the data collection demand evaluation index of each target area is obtained through comprehensive analysis. S3. According to the data collection demand evaluation index of each target area, each data collection area is screened, and according to the number of physical fitness data collection in each data collection area and the physical fitness data of teenagers collected in each time, a comprehensive analysis is performed to obtain the data collection demand evaluation index of each data collection area; The comprehensive analysis obtains the data collection demand evaluation index of each data collection area. The specific analysis process is as follows: The number of teenagers in each data collection area is obtained, and the critical number of teenagers is extracted from the visualization system database according to the number of physical fitness data collection in each data collection area and the physical fitness data of teenagers collected each time, and a data collection demand evaluation index of each data collection area is obtained through comprehensive analysis. The data collection demand evaluation index of each data collection area is used to quantitatively evaluate the data collection demand of each data collection area, and provide a data basis for data collection in each data collection area; The specific numerical expression of the data collection demand evaluation index of each data collection area is: ； In the formula, represents the data collection demand evaluation index of the t-th data collection area, t represents the number of each data collection area, , g represents the total number of data collection areas, represents the number of physical data collection times in the tth data collection area, represents the critical acquisition times, represents the number of teenagers in the tth data collection area, represents the critical number of teenagers, It represents the value of the nth physical index of the tth data collection area in the rth data collection, and r represents the number of each data collection time. , h represents the total number of data collection, n represents the number of each type of physical index, , m represents the total number of types of physical indicators, Indicates the data collection demand influencing factor of the data collection area corresponding to the preset number of physical fitness data collection times, Indicates the data collection demand influencing factor of the data collection area corresponding to the preset number of teenagers, Indicating the data collection demand influencing factor of the data collection area corresponding to the preset physical fitness data value; S4. Collect data from each data collection area according to the data collection demand evaluation index of each data collection area, and provide data collection quality evaluation feedback; The data collection quality assessment feedback specifically includes: The data collection time and actual data collection amount of each data collection area are obtained, and the estimated data collection time of each data collection area is obtained according to the data collection amount of each data collection area, and the allowable collection time difference and the allowable deviation data collection amount are extracted from the visualization system database, and the data quality evaluation index of each data collection area is obtained by comprehensive analysis according to the values ​​of each physical index of each adolescent and the data collection amount of each data collection area. The data quality evaluation index of each data collection area is used to quantitatively evaluate the data quality of data collected in each data collection area, and provide a basis for data collection quality evaluation feedback; The numerical expression of the data quality evaluation index of each data collection area is: ； In the formula, represents the data quality assessment index of the tth data collection area, represents the data collection time of the tth data collection area, represents the estimated data collection time for the tth data collection area, Indicates that the time difference of collection is allowed. represents the value of the nth physical index of the qth adolescent in the tth data collection area, q represents the number of each adolescent, , d represents the total number of adolescents, n represents the number of each type of physical fitness index, , m represents the total number of types of physical indicators, represents the data collection amount of the tth data collection area, represents the actual data collection amount of the tth data collection area, Indicates the amount of allowable deviation data collection, Indicates the influencing factor of the data quality of the data collection area corresponding to the preset data collection time, Indicates the influencing factor of the data quality of the data collection area corresponding to the preset adolescent physical fitness data value, Indicates the influencing factor of the data quality of the data collection area corresponding to the preset data collection amount.

2. The method for visualizing and analyzing the physical fitness data of adolescents according to claim 1, characterized in that: The number of times physical fitness data are collected in each target area, the geographical environment statistical data and the physical fitness data of teenagers collected at each time, wherein the geographical environment statistical data include the historical average temperature, the historical average suspended particle concentration and the historical average vegetation coverage rate, as well as the temperature at the current monitoring time point, the suspended particle concentration at the current monitoring time point and the vegetation coverage rate at the current monitoring time point; The physical fitness data of the teenagers collected each time include the physical fitness index values ​​of each teenager and the average values ​​of the physical fitness index values ​​of the teenagers collected each time. The physical fitness indicators include vital capacity, vision, body fat percentage and sports performance.

3. The method for visualizing and analyzing the physical fitness data of adolescents according to claim 1, characterized in that: The screening obtains each data collection area, and the specific screening process is: The data collection need degree assessment index threshold of each target area is extracted from the visualization system database, and the data collection need degree assessment index of each target area is compared with the data collection need degree assessment index threshold of each target area. If the data collection need degree assessment index of a target area is higher than or equal to the data collection need degree assessment index threshold of the target area, the target area is marked as a data collection area; if the data collection need degree assessment index of a target area is lower than the data collection need degree assessment index threshold of the target area, data collection will not be performed on the target area.

4. The method for visualizing and analyzing the physical fitness data of adolescents according to claim 1, characterized in that: The specific process of collecting data in each data collection area is as follows: Extract the data collection demand evaluation index interval of each data collection area from the visualization system database, compare the data collection demand evaluation index of each data collection area with the data collection demand evaluation index interval of each data collection area one by one, match the data collection amount of each data collection area, and collect data for each data collection area according to the data collection amount of each data collection area; The process of matching and obtaining the data collection volume of each data collection area is to construct a mapping set between the data collection demand evaluation index interval and the data collection volume of each data collection area, and the corresponding relationship is a one-to-one relationship. The data collection demand evaluation index of a data collection area is compared with the data collection demand evaluation index interval of the data collection area to obtain the interval in which the data collection demand evaluation index of the data collection area is located, and the data collection volume of the data collection area is obtained according to the mapping set.

5. The method for visualizing and analyzing the physical fitness data of adolescents according to claim 1, characterized in that: The data collection quality assessment feedback also includes: The data quality assessment index threshold of each data collection area is extracted from the visualization system database, and the data quality assessment index of each data collection area is compared with the data quality assessment index threshold of each data collection area. If the data quality assessment index of a data collection area is higher than or equal to the data quality assessment index threshold of the data collection area, the data quality of the data collection area is assessed as qualified, and feedback is given to the data of each data collection area. If the data quality assessment index of a data collection area is lower than the data quality assessment index threshold of the data collection area, the data quality of the data collection area is assessed as unqualified, and data collection is performed again on the data collection area.

6. A system for visual analysis of adolescent physical fitness data according to any one of claims 1 to 5, characterized in that: include: Data collection module, data collection requirement assessment module, data collection requirement assessment module and data collection quality assessment feedback module; The data collection module is used to obtain the areas for analyzing the physical fitness data of teenagers, mark them as target areas, and obtain the number of physical fitness data collections, geographical environment statistical data and the physical fitness data of teenagers collected each time for each target area; The data collection demand assessment module is used to obtain the data collection demand assessment index of each target area through comprehensive analysis based on the number of physical fitness data collection in each target area, geographical environment statistical data and the physical fitness data of teenagers collected in each time; The data collection demand assessment module is used to screen each data collection area according to the data collection demand assessment index of each target area, and to obtain the data collection demand assessment index of each data collection area through comprehensive analysis based on the number of physical fitness data collections of each data collection area and the physical fitness data of teenagers collected each time; The data collection quality assessment feedback module is used to collect data from each data collection area according to the data collection demand assessment index of each data collection area, and to provide data collection quality assessment feedback.

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