An individual behavior and physiological health correlation system based on geographic information technology

Abstract

The present invention provides a system for associating individual behavior with physiological health based on geographic information technology, which relates to the field of electrical digital data processing. The system includes an information collection module, a geographic integration module, a data analysis module, and an association display module. The information collection module is used to collect behavior and health information of each region. The geographic integration module is used to integrate the collected information based on geographic information. The data analysis module analyzes and processes the correlation between behavior data and health data. The association display module is used to fuse and display the association results with geographic information. This system can analyze the impact of different behaviors on health according to geographic information, obtain information on appropriate and inappropriate behaviors in different regions, and help users better plan their life behaviors.

Description

Technical Field

[0001] The present invention relates to the field of electrical digital data processing, and more particularly to a system for associating individual behavior with physiological health based on geographic information technology. Background Art

[0002] An individual's life behavior can have an impact on physiological health. In most cases, the direction of the impact of life behavior is consistent and does not change with the region. However, there are some behaviors whose impact on physiological health changes with the environment. Therefore, an analysis system is needed to identify these behaviors and establish the association between these behaviors, the geographical environment, and physiological health, so as to better help users adopt appropriate behaviors in their local area.

[0003] The foregoing discussion of the background art is only intended to facilitate the understanding of the present invention. This discussion does not recognize or admit that any of the materials mentioned is a part of common general knowledge.

[0004] Many physiological health systems have now been developed. After a large amount of retrieval and reference, it is found that existing physiological health systems are like the system disclosed in CN105117583B. These systems generally include a personal physiological data collection device, a terminal device, and an analysis platform; the metadata collected by the personal physiological data collection device is transmitted to the analysis platform through the terminal device. After the analysis platform analyzes the data, the results are fed back to the terminal device or stored in the database of the analysis platform itself for retrieval; Step 1: The personal physiological data collection device only collects the physiological data of the person being measured and packs the physiological data according to the communication protocol and transmits it to the terminal device; in this step, the communication protocol is the communication protocol between the personal physiological data collection device and the terminal device; Step 2: After the terminal device unpacks the data packet from the personal physiological data collection device, it packs the data according to the communication protocol and transmits it to the analysis platform; in this step, the communication protocol is the common communication network protocol; Step 3: The analysis platform unpacks the data packet from the terminal device and analyzes the data using the analysis model. However, this system does not analyze the impact of individual behavior on physiological health and cannot provide effective behavior suggestions to improve the physical condition. Summary of the Invention

[0005] The object of the present invention is to propose a system for associating individual behavior with physiological health based on geographic information technology in view of the existing deficiencies.

[0006] The present invention adopts the following technical solutions:

[0007] A system for associating individual behavior with physiological health based on geographic information technology, comprising an information collection module, a geographic integration module, a data analysis module, and an association display module;

[0008] The information collection module is used to collect the behavior and health information of each region. The geographical integration module is used to integrate the collected information based on geographical information. The data analysis module analyzes and processes the correlation between the behavior data and the health data. The correlation display module is used to fuse and display the correlation results with geographical information;

[0009] The information collection module includes a behavior information collection unit, a health information collection unit, and an environmental information collection unit. The behavior information collection unit is used to collect the life behavior data of an individual. The health information collection unit is used to collect the health status data of an individual. The environmental information collection unit is used to collect the living environment data of a region;

[0010] The geographical integration module includes an individual allocation unit, an environmental feature analysis unit, and an information storage unit. The individual allocation unit is used to allocate the data of each individual to the corresponding region. The environmental feature analysis unit is used to analyze and obtain the environmental features of each region. The information storage unit is used to save the environmental feature information of each region and the allocated individual data;

[0011] The data analysis module includes a differentiation analysis unit, a correlation analysis unit, and a quantization mapping analysis unit. The differentiation analysis unit is used to analyze the data differences of each region. The correlation analysis unit is used to analyze the correlation characteristics between different behaviors and health statuses. The quantization mapping analysis unit is used to establish a quantitative mapping relationship between behaviors and health statuses;

[0012] The correlation display module includes a geographical positioning unit and a query interaction unit. The geographical positioning unit is used to locate the geographical position of the user. The query interaction unit is used to match the geographical position to the corresponding region and display inappropriate behaviors and appropriate behaviors;

[0013] Further, the differentiation analysis unit includes a behavior difference analysis processor and a regional difference analysis processor. The behavior difference analysis processor is used to statistically analyze the individual behavior differences within the same region or within regions with the same characteristic labels. The regional difference analysis processor is used to analyze and process the information differences within regions with different characteristic labels;

[0014] Further, the behavior difference analysis processor calculates the basic correlation value Va of each health item and behavior item, sorts them from large to small according to the basic correlation value, and obtains the weight sequence number Nw of each behavior item;

[0015] The regional difference analysis processor compares and obtains the label differences (H 1 , H 2 ) between two regions;

[0016] where H1 refers to the set of feature tags that exist in the first region but not in the second region, H 2 refers to the set of feature tags that exist in the second region but not in the first region;

[0017] The regional difference analysis processor compares the weight sequence number sets of two regions to obtain a weight difference set {ΔN(i) j};

[0018] where ΔN(i) j represents the weight sequence number deviation of the i-th behavior item in the j-th health item;

[0019] The regional difference analysis processor traverses any two regions to obtain the pairing information of the label difference and the weight difference set;

[0020] Further, the correlation analysis unit includes a pairing unification processor and a mapping screening processor. The pairing unification processor is used to perform unification processing on the pairing information, and the mapping screening processor is used to screen out the mapping relationships between feature tags, behavior items, and health items;

[0021] The pairing unification processor disassembles the pairing information into two one-way pairing information, and based on all the one-way pairing information, calculates the cumulative deviation value of each feature tag. Let A(i, j, k) represent the sum of the weight sequence number deviations of the i-th behavior item in the j-th health item paired with the k-th feature tag;

[0022] The mapping screening processor screens the cumulative deviation values. When the cumulative deviation value is greater than the threshold, the corresponding feature tag, behavior item, and health item are used as the screened mapping items;

[0023] Further, the quantization mapping analysis unit includes a mapping arrangement processor and a mapping quantization processor. The mapping arrangement processor is used to arrange the mapping items based on health items, and the mapping quantization processor is used to calculate the mapping parameter set corresponding to each health item;

[0024] The mapping quantization processor calculates the mapping parameter λ according to the following formula i，k :

[0025]

[0026] where Va i，j is the mean value of the basic correlation values.

[0027] The beneficial effects achieved by the present invention are:

[0028] This system collects the behavioral data, health data of individuals, and environmental data of various regions, and obtains the mapping relationship between environmental characteristics, behavioral items, and health items through multi-level differential analysis. Based on the mapping relationship, behavioral suggestions can be given to help users maintain a physiological health state with different living habits in different regions.

[0029] To enable a further understanding of the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the provided drawings are only for reference and illustration, and are not used to limit the present invention. Brief Description of the Drawings

[0030] Figure 1 It is a schematic diagram of the overall structural framework of the present invention;

[0031] Figure 2 It is a schematic diagram of the composition of the geographic integration module of the present invention;

[0032] Figure 3 It is a schematic diagram of the composition of the data analysis module of the present invention;

[0033] Figure 4 It is a schematic diagram of the composition of the differential analysis unit of the present invention;

[0034] Figure 5 It is a schematic diagram of the composition of the correlation analysis unit of the present invention. Detailed Embodiment

[0035] The following are specific embodiments to illustrate the implementation manners of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Additionally, the drawings of the present invention are only simple schematic illustrations and are not drawn according to actual sizes, which is stated in advance. The following embodiments will further detail the related technical content of the present invention, but the disclosed content is not used to limit the protection scope of the present invention.

[0036] Embodiment 1.

[0037] This embodiment provides an individual behavior and physiological health correlation system based on geographic information technology, combined with Figure 1 , including an information collection module, a geographic integration module, a data analysis module, and a correlation display module;

[0038] The information collection module is used to collect behavior and health information of each region. The geographical integration module is used to integrate the collected information based on geographical information. The data analysis module analyzes and processes the correlation between behavior data and health data. The correlation display module is used to fuse and display the correlation results with geographical information;

[0039] The information collection module includes a behavior information collection unit, a health information collection unit, and an environmental information collection unit. The behavior information collection unit is used to collect the life behavior data of individuals. The health information collection unit is used to collect the health status data of individuals. The environmental information collection unit is used to collect the living environment data of a region;

[0040] The geographical integration module includes an individual allocation unit, an environmental feature analysis unit, and an information storage unit. The individual allocation unit is used to allocate the data of each individual to the corresponding region. The environmental feature analysis unit is used to analyze and obtain the environmental features of each region. The information storage unit is used to save the environmental feature information of each region and the allocated individual data;

[0041] The data analysis module includes a differentiation analysis unit, a correlation analysis unit, and a quantization mapping analysis unit. The differentiation analysis unit is used to analyze the data differences of each region. The correlation analysis unit is used to analyze the correlation characteristics between different behaviors and health statuses. The quantization mapping analysis unit is used to establish a quantitative mapping relationship between behaviors and health statuses;

[0042] The correlation display module includes a geographical positioning unit and a query interaction unit. The geographical positioning unit is used to locate the geographical position of the user. The query interaction unit is used to match the geographical position to the corresponding region and display inappropriate behaviors and appropriate behaviors;

[0043] The differentiation analysis unit includes a behavior difference analysis processor and a regional difference analysis processor. The behavior difference analysis processor is used to statistically analyze the individual behavior differences within the same region or regions with the same characteristic labels. The regional difference analysis processor is used to analyze and process the information differences between regions with different characteristic labels;

[0044] The behavior difference analysis processor calculates the basic correlation value Va of each health item and behavior item, sorts them from large to small according to the basic correlation value, and obtains the weight serial number Nw of each behavior item;

[0045] The regional difference analysis processor compares and obtains the label differences (H 1 , H 2 ) between two regions;

[0046] Among them, H 1Refers to the set of feature tags that exist in the first region but not in the second region, H 2 Refers to the set of feature tags that exist in the second region but not in the first region;

[0047] The regional difference analysis processor compares the weight sequence number sets of two regions to obtain a weight difference set {ΔN(i) j};

[0048] Among them, ΔN(i) j Represents the weight sequence number deviation of the i-th behavior item in the j-th health item;

[0049] The regional difference analysis processor traverses any two regions to obtain the pairing information of the label difference and the weight difference set;

[0050] The correlation analysis unit includes a pairing unification processor and a mapping screening processor. The pairing unification processor is used to perform unification processing on the pairing information, and the mapping screening processor is used to screen out the mapping relationships between feature tags, behavior items, and health items;

[0051] The pairing unification processor disassembles the pairing information into two one-way pairing information, and based on all the one-way pairing information, calculates the cumulative deviation value of each feature tag. Let A(i, j, k) represent the sum of the weight sequence number deviations of the i-th behavior item in the j-th health item paired with the k-th feature tag;

[0052] The mapping screening processor screens the cumulative deviation values. When the cumulative deviation value is greater than the threshold, the corresponding feature tag, behavior item, and health item are used as the screened mapping items;

[0053] The quantization mapping analysis unit includes a mapping arrangement processor and a mapping quantization processor. The mapping arrangement processor is used to arrange the mapping items based on the health items, and the mapping quantization processor is used to calculate the mapping parameter set corresponding to each health item;

[0054] The mapping quantization processor calculates the mapping parameter λ according to the following formula i，k :

[0055]

[0056] Among them, Is the mean value of the basic correlation values.

[0057] Example two.

[0058] This embodiment includes all the contents of Embodiment 1 and provides an individual behavior and physiological health association system based on geographic information technology, including an information collection module, a geographic integration module, a data analysis module, and an association display module;

[0059] The information collection module is used to collect the behavior and health information of each region. The geographical integration module is used to integrate the collected information based on geographical information. The data analysis module analyzes and processes the correlation between the behavior data and the health data. The correlation display module is used to fuse and display the correlation results with the geographical information;

[0060] The information collection module includes a behavior information collection unit, a health information collection unit, and an environmental information collection unit. The behavior information collection unit is used to collect the life behavior data of an individual. The health information collection unit is used to collect the health status data of an individual. The environmental information collection unit is used to collect the living environment data of a region;

[0061] Combined with Figure 2 , the geographical integration module includes an individual allocation unit, an environmental feature analysis unit, and an information storage unit. The individual allocation unit is used to allocate the data of each individual to the corresponding region. The environmental feature analysis unit is used to analyze the environmental features of each region. The information storage unit is used to save the environmental feature information of each region and the allocated individual data;

[0062] Combined with Figure 3 , the data analysis module includes a differentiation analysis unit, a correlation analysis unit, and a quantization mapping analysis unit. The differentiation analysis unit is used to analyze the data differences of each region. The correlation analysis unit is used to analyze the correlation features between different behaviors and health statuses. The quantization mapping analysis unit is used to establish a quantitative mapping relationship between behaviors and health statuses;

[0063] The correlation display module includes a geographical positioning unit and a query interaction unit. The geographical positioning unit is used to locate the geographical position of the user. The query interaction unit is used to match the geographical position to the corresponding region and display inappropriate behaviors and appropriate behaviors;

[0064] The behavior information collection unit includes a behavior item memory, a behavior interval memory, and an information classification processor. The behavior item memory is used to save the content of each behavior item. The behavior interval memory is used to save the classification interval range of each behavior item. The information classification processor is used to classify each behavior item after obtaining the behavior data of an individual;

[0065] The behavior items are divided into three states: often, occasionally, and never;

[0066] The health information collection unit includes a health item register and a health classification processor. The health item register is used to save the content of each health item. The health classification processor is used to classify each health item after obtaining the health data of an individual;

[0067] The health items are divided into two states: abnormal and normal;

[0068] The environmental information collection unit includes a water quality collection processor, an air quality collection processor, and a temperature collection processor. The water quality collection processor is used to collect water quality data, the air quality collection processor is used to collect air quality data, and the temperature collection processor is used to collect temperature data;

[0069] The individual allocation unit includes an individual information buffer and a geographical area allocation processor. The individual information buffer is used to receive and cache the behavior information and health information of a single individual. The geographical area allocation processor allocates the data to the corresponding geographical area based on the address information of a single individual and stores it in the information storage unit;

[0070] The environmental feature analysis unit includes an environmental information buffer and a feature extraction processor. The environmental information buffer is used to receive and cache the environmental information of each geographical area. The feature extraction processor is used to analyze the environmental information of each geographical area to obtain environmental features and store them in the information storage unit;

[0071] The feature extraction processor is provided with processing methods for multiple feature items. When the environmental data is processed using the corresponding processing method and a result that meets the requirements is obtained, the corresponding feature label is assigned to the geographical area;

[0072] For example, when the average annual humidity in the air is higher than a certain specific value, a humid feature label is assigned. When the average annual humidity in the air is lower than another specific value, a dry feature label is assigned;

[0073] Combined with Figure 4 , the differential analysis unit includes a behavior difference analysis processor and a geographical area difference analysis processor. The behavior difference analysis processor is used to statistically analyze the individual behavior differences within the same geographical area or within geographical areas with the same feature label. The geographical area difference analysis processor is used to analyze and process the information differences between geographical areas with different feature labels;

[0074] The analysis process of the behavior difference analysis processor includes the following steps:

[0075] S1. Determine a target health item and a target behavior item;

[0076] S2. Statistically obtain the basic matrix A of the target health item and the target behavior item:

[0077]

[0078] Among them, the first row of the base matrix represents the number of individuals with normal target health items, the second row of the base matrix represents the number of individuals with abnormal target health items, the first column of the base matrix represents the number of individuals with frequent target behavior items, the second column of the base matrix represents the number of individuals with occasional target behavior items, and the third column of the base matrix represents the number of individuals with never target behavior items;

[0079] S3. Calculate the base correlation value Va of the base matrix:

[0080]

[0081] S4. Repeat steps S1 to S3 until all target behavior items are traversed;

[0082] S5. Sort the target behavior items in descending order according to the base correlation value to obtain the weight serial number Nw of each target behavior item;

[0083] S6. Repeat steps S1 to S5 until all target health items are traversed;

[0084] The behavior difference analysis processor obtains the behavior difference information of each region as the base correlation value set {Va i，j} and the weight serial number set {Nw i，j}, where Va i，j represents the base correlation value between the i-th behavior item and the j-th health item; Nw i，j represents the weight serial number of the i-th behavior item in the j-th health item;

[0085] The analysis process of the regional difference analysis processor includes the following steps:

[0086] S21. Determine two regions with different feature labels;

[0087] S22. Compare to obtain the label difference (H 1 , H 2 );

[0088] Among them, H 1 refers to the set of feature labels that the first region has and the second region does not have, and H 2 refers to the set of feature labels that the second region has and the first region does not have;

[0089] S23. Compare the weight serial number sets of the two regions to obtain the weight difference set {ΔN(i) j};

[0090] Among them, ΔN(i) j represents the weight serial number deviation of the i-th behavior item in the j-th health item;

[0091] S24. Repeat steps S21 to S23 to traverse any two regions and obtain the pairing information of the label differences and weight differences sets;

[0092] In step S23, the calculation process of the weight sequence number deviation is as follows:

[0093] S31. Obtain two weight sequence number sets in the same health item, which are respectively called the first target set and the second target set;

[0094] S32. Calculate the sequence number difference Δn(i) of each behavior item:

[0095] Δn(i) j =(Nw i，j ) B1 -(Nw i，j ) B2 ;

[0096] where B1 and B2 are two regions with different characteristic labels;

[0097] S33. If all the sequence number differences are 0, exit this process; otherwise, obtain the behavior item number i' corresponding to the sequence number difference with the largest absolute value, and let ΔN(i') j =Δn(i') j ;

[0098] S34. Delete the weight sequence numbers of the i'-th behavior item in the first target set and the second target set, re-number the weight sequence numbers of the remaining behavior items, and return to step S32;

[0099] It should be noted that the weight difference set {ΔN(i) j} does not include the weight sequence number deviation with a value of 0;

[0100] Combined with Figure 5 , the correlation analysis unit includes a pairing and unifying processor and a mapping and screening processor. The pairing and unifying processor is used to unify the pairing information, and the mapping and screening processor is used to screen out the mapping relationships between the characteristic labels, behavior items, and health items;

[0101] The pairing and unifying processor disassembles the pairing information ((H 1 , H 2 ), {ΔN(i0 j}) into (H 1 , {ΔN(i) j}) and (H 2 , {-ΔN(i) j}) Two one-way pairing messages. Based on all the one-way pairing messages, the cumulative deviation value of each feature label is statistically calculated. Let A(i, j, k) represent the sum of the weight sequence number deviations of the i-th behavior item in the j-th health item paired with the k-th feature label;

[0102] The mapping and screening processor screens the cumulative deviation values. When the cumulative deviation value is greater than the threshold, the corresponding feature label, behavior item, and health item are used as the screened mapping items;

[0103] The quantization mapping analysis unit includes a mapping arrangement processor and a mapping quantization processor. The mapping arrangement processor is used to arrange the mapping items based on the health items, and the mapping quantization processor is used to calculate the mapping parameter set corresponding to each health item;

[0104] The mapping quantization processor calculates the mapping parameter λ according to the following formula:

[0105]

[0106] Where, is the mean value of the basic correlation value;

[0107] The query and interaction unit includes an association information register and an association display processor. The association information register is used to save the mapping item information and the mapping parameters. The association display processor displays the appropriate behaviors and inappropriate behaviors according to the mapping parameters. When the mapping parameter is positive, it represents an appropriate behavior, and when it is negative, it represents an inappropriate behavior;

[0108] In the above text, i is the sequence number of the behavior item, j is the sequence number of the health item, and k is the sequence number of the feature label.

[0109] The content disclosed above is only the preferred feasible embodiment of the present invention, and does not limit the protection scope of the present invention. Therefore, all equivalent technical changes made by using the content of the specification and drawings of the present invention are included in the protection scope of the present invention. In addition, the elements therein can be updated with the development of technology.

Claims

1. A system for associating individual behavior with physiological health based on geographic information technology, characterized in that: It includes information collection module, geographic integration module, data analysis module and association display module; The information collection module is used to collect behavior and health information of each region, the geographic integration module is used to integrate the collected information based on geographic information, the data analysis module analyzes and processes the correlation between behavior data and health data, and the correlation display module is used to integrate the correlation results with geographic information for display; The information collection module includes a behavior information collection unit, a health information collection unit and an environment information collection unit. The behavior information collection unit is used to collect individual life behavior data, the health information collection unit is used to collect individual health status data, and the environment information collection unit is used to collect living environment data of a region; the geographic integration module includes an individual allocation unit, an environmental feature analysis unit and an information storage unit. The individual allocation unit is used to allocate each individual's data to a corresponding region, the environmental feature analysis unit is used to analyze and obtain the environmental features of each region, and the information storage unit is used to save the environmental feature information of each region and the allocated individual data; the data analysis module includes a difference analysis unit, a correlation analysis unit and a quantitative mapping analysis unit. The difference analysis unit is used to analyze the data difference of each region, the correlation analysis unit is used to analyze the correlation features between different behaviors and health status, and the quantitative mapping analysis unit is used to establish a quantitative mapping relationship between behavior and health status; The association display module includes a geographic positioning unit and a query interaction unit, wherein the geographic positioning unit is used to locate the user's geographic location, and the query interaction unit is used to match the geographic location to the corresponding region and display inappropriate behaviors and appropriate behaviors; The difference analysis unit includes a behavior difference analysis processor and a regional difference analysis processor. The behavior difference analysis processor is used to perform statistical analysis on individual behavior differences in the same region or in regions with the same feature labels. The regional difference analysis processor is used to analyze and process information differences in regions with different feature labels. The behavior difference analysis processor calculates the basic association value Va of each health item and behavior item, sorts them from large to small according to the basic association value, and obtains the weight sequence number Nw of each behavior item; the regional difference analysis processor compares and obtains the label difference of the two regions. ; Wherein, H1 refers to a set of feature labels that exist in the first region but not in the second region, and H2 refers to a set of feature labels that exist in the second region but not in the first region; the regional difference analysis processor compares the weight sequence number sets of the two regions to obtain a weight difference set ;in, represents the weight sequence number deviation of the i-th behavior item in the j-th health item; the regional difference analysis processor traverses any two regions to obtain the pairing information of the label difference and the weight difference set; The calculation process of the weighted sequence deviation is as follows: Get two weighted sequence number sets in the same health item, called the first target set and the second target set respectively; calculate the sequence number difference of each behavior item : ; Nw i,j is the weighted serial number, where B1 and B2 are two regions with different feature labels; if all serial number differences are 0, then exit the process, otherwise obtain the behavior item number corresponding to the serial number difference with the largest absolute value ,make ; Delete the first target set and the second target set The weight sequence number of the behavior items is re-arranged, and the weight sequence number of the remaining behavior items is recalculated to calculate the sequence difference of each behavior item. ;Depend on The weight difference set The weight sequence number deviation whose value is 0 is not included; the correlation analysis unit includes a pairing unified processor and a mapping screening processor, the pairing unified processor is used to unify the pairing information, and the mapping screening processor is used to screen out the mapping relationship between the feature label and the behavior item and the health item; the pairing unified processor Disassemble into and Two one-way pairing information, based on all the one-way pairing information, the cumulative deviation value of each feature label is calculated, and the Represents the sum of the weighted sequence number deviations of the ith behavior item in the jth health item paired with the kth feature tag; the mapping screening processor screens the cumulative deviation value, and when the cumulative deviation value is greater than a threshold, the corresponding feature tag, behavior item and health item are used as the screened mapping items.

2. The individual behavior and physiological health association system based on geographic information technology as claimed in claim 1, characterized in that: The quantitative mapping analysis unit includes a mapping arrangement processor and a mapping quantization processor, wherein the mapping arrangement processor is used to arrange the mapping items based on the health items, and the mapping quantization processor is used to calculate the mapping parameter set corresponding to each health item; The mapping quantization processor calculates the mapping parameter according to the following formula : ； in, is the mean of the basic correlation values.

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