CN115547491A - Health management method and device, electronic equipment and storage medium - Google Patents

Health management method and device, electronic equipment and storage medium Download PDF

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CN115547491A
CN115547491A CN202110723718.3A CN202110723718A CN115547491A CN 115547491 A CN115547491 A CN 115547491A CN 202110723718 A CN202110723718 A CN 202110723718A CN 115547491 A CN115547491 A CN 115547491A
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朱国康
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Anhui Huami Health Technology Co Ltd
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Anhui Huami Health Technology Co Ltd
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    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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Abstract

The disclosure provides a health management method, a health management device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The specific implementation scheme is as follows: obtaining various data source information of an object to be managed; determining the health attribute of an object to be managed according to various data source information; aiming at each dimension to be evaluated of the object to be managed, determining an evaluation result of the object to be managed on the dimension to be evaluated according to a health index related to the dimension to be evaluated in the health attributes; according to the method, the health of the object to be managed is managed according to the evaluation result of the object to be managed on each dimension to be evaluated, according to the health index related to the dimension to be evaluated in the health attribute, multiple dimensions to be evaluated of the object to be managed are evaluated and analyzed, then the object to be managed is managed according to the evaluation and analysis results of the multiple dimensions to be evaluated, the personalized active health management of the object to be managed can be achieved, and the health level of a user is effectively improved.

Description

Health management method and device, electronic equipment and storage medium
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to a health management method and apparatus, an electronic device, and a storage medium.
Background
With the development of medicine, many diseases can be dealt with by measures such as medicines or surgery, but in many cases, the negative experience of the diseases cannot be eradicated, resulting in a long-term decline in quality of life. Health is the basis for ensuring quality of life. In fact, many diseases can be prevented through scientific intervention, and effective health management can improve the health level of users and improve the quality of life. The health management is to monitor and manage the health state of individuals and groups and risk factors, so as to achieve the purpose of preventing diseases.
In the related art, the general health problems appearing in the user group are mainly evaluated by collecting health data and the like of the user group, and the user group is subjected to health management aiming at the general health problems appearing in the user group. For example, the health management is performed in the form of centralized intervention such as health lectures or expert consultation. However, on the premise that health problems occur, health management is performed after the user group is cured, and personalized active health management cannot be performed on the user, so that the health level of the user cannot be effectively improved.
Disclosure of Invention
The disclosure provides a method and a device for health management, electronic equipment and a storage medium.
According to an aspect of the present disclosure, there is provided a health management method including: acquiring various data source information of an object to be managed; determining health attributes of the object to be managed according to the various data source information, wherein the health attributes comprise a plurality of health indexes; aiming at each dimension to be evaluated of the object to be managed, determining an evaluation result of the object to be managed on the dimension to be evaluated according to a health index related to the dimension to be evaluated in the health attributes; and performing health management on the object to be managed according to the evaluation result of the object to be managed on each dimension to be evaluated.
In the technical scheme, various data source information of an object to be managed is acquired; determining health attributes of the object to be managed according to the information of the various data sources, wherein the health attributes comprise a plurality of health indexes; aiming at each dimension to be evaluated of the object to be managed, determining an evaluation result of the object to be managed on the dimension to be evaluated according to a health index related to the dimension to be evaluated in the health attributes; according to the method, the health management is carried out on the object to be managed according to the evaluation result of the object to be managed on each dimension to be evaluated, according to the health indexes related to the dimension to be evaluated in the health attributes, the multiple dimensions to be evaluated of the object to be managed are evaluated and analyzed, further, the object to be managed is subjected to the health management according to the evaluation and analysis results of the multiple dimensions to be evaluated, the personalized and active health management of the object to be managed can be achieved, and the health level of a user is effectively improved.
Optionally, the determining the health attribute of the object to be managed according to the information of the plurality of data sources includes: for each kind of data source information, determining a feature extraction mode of the data source information according to the acquisition cycle of the data source information; extracting features in the data source information according to the feature extraction mode; and determining the health attribute of the object to be managed according to the characteristics in various data source information.
Optionally, the determining, for each type of data source information, a feature extraction manner of the data source information according to an acquisition cycle of the data source information includes: for each kind of data source information, determining whether the data source information is real-time information according to the acquisition cycle of the data source information; when the data source information is real-time information, determining that a feature extraction mode of the data source information comprises short-term feature extraction in a first time period and long-term feature extraction in a second time period; wherein the length of the first time period is less than the length of the second time period; and when the data source is non-real-time information, determining that the feature extraction mode of the data source information comprises long-term feature extraction in a second time period.
Optionally, the dimension to be evaluated comprises a health status dimension, wherein the health status dimension comprises a plurality of health status sub-dimensions; the determining, for each dimension to be evaluated of the object to be managed, an evaluation result of the object to be managed on the dimension to be evaluated according to a health index related to the dimension to be evaluated in the health attribute includes: for each health state sub-dimension in the health state dimensions, determining a sub-evaluation result of the object to be managed on the health state sub-dimension according to a plurality of target health indexes related to the health state sub-dimension in the health attributes; and determining the evaluation result of the object to be managed on the health state dimension according to the sub-evaluation results of the object to be managed on the various health state sub-dimensions.
Optionally, when the health status sub-dimension is a mental health status sub-dimension, the determining, according to a plurality of target health indicators related to the health status sub-dimension in the health attributes, a sub-evaluation result of the object to be managed on the health status sub-dimension includes: carrying out weighted summation on numerical values of a plurality of target health indexes related to the psychological health state sub-dimension in the health attributes to obtain weighted summation results; and determining the weighted summation result as a sub-evaluation result of the object to be managed on the psychological health state sub-dimension.
Optionally, when the health status sub-dimension is a physiological health status sub-dimension, the determining, according to a plurality of target health indicators related to the health status sub-dimension in the health attributes, a sub-evaluation result of the object to be managed on the health status sub-dimension includes: determining a criticality of a plurality of target disease symptoms associated with the physiological health status sub-dimension and a correlation between each of the target health indicators and each of the target disease symptoms; for each of the target health indicators, determining a weight for the target health indicator based on the criticality of the plurality of target disease symptoms and the degree of correlation between the target health indicator and each of the target disease symptoms; and determining the sub-evaluation result of the object to be managed on the physiological health state sub-dimension according to the numerical value and the weight of each target health index.
Optionally, the dimension to be evaluated comprises a lifestyle dimension, wherein the lifestyle dimension comprises a plurality of lifestyle sub-dimensions; the determining, for each dimension to be evaluated of the object to be managed, an evaluation result of the object to be managed on the dimension to be evaluated according to a health index related to the dimension to be evaluated in the health attribute includes: for each lifestyle sub-dimension of the lifestyle dimensions, determining a degree of risk of a plurality of target disease symptoms associated with the lifestyle sub-dimension and a degree of correlation between each of the target health indicators and each of the target disease symptoms; for each of the target health indicators, determining a weight for the target health indicator based on the criticality of the plurality of target disease symptoms and the degree of correlation between the target health indicator and each of the target disease symptoms; determining a sub-evaluation result of the object to be managed on the life style sub-dimension according to the numerical value and the weight of each target health index; and determining the evaluation result of the object to be managed in the life style dimension according to the sub-evaluation result of the object to be managed in the plurality of life style sub-dimensions.
Optionally, the dimension to be evaluated comprises a health status dimension; the method further comprises the following steps: determining a reference object data set, wherein the reference object data set comprises: portrait information of a reference object, and an evaluation result of the reference object in a health status dimension in an adjacent third time period; predicting a target evaluation result of the object to be managed on the health state dimension according to the evaluation result and the similarity between the object to be managed and the reference object; and recommending the target health state of the object to be managed according to the target evaluation result.
Optionally, the predicting a target evaluation result of the object to be managed in the health status dimension according to the evaluation result and the similarity between the object to be managed and the reference object includes: determining an evaluation result difference value of the reference object in the health state dimension in a third adjacent time period; predicting the evaluation result difference of the object to be managed according to the evaluation result difference of the reference objects and the similarity between the reference objects and the object to be managed; and predicting a target evaluation result of the object to be managed on the health state dimension according to a historical evaluation result of the object to be managed on the health state dimension and an evaluation result difference value of the object to be managed on the health state dimension.
Optionally, the dimension to be evaluated further comprises a lifestyle dimension; the method further comprises the following steps: inputting a historical evaluation result of the object to be managed on the health state dimension, an evaluation result difference of the object to be managed on the health state dimension, a historical evaluation result of the object to be managed on the life style dimension and portrait information of the object to be managed into a preset machine learning model, and acquiring an evaluation result difference of the object to be managed on the life style dimension, which is output by the machine learning model; determining the adjustment amount of a target health index related to the life style dimension in the health attribute according to the evaluation result difference value of the object to be managed in the life style dimension; and guiding the health indexes of the objects to be managed according to the adjustment quantity of the target health indexes related to the life style dimension in the health attributes.
Optionally, the data source information includes at least two of the following information: basic data of the object to be managed, environment data of the environment where the object to be managed is located, questionnaire data of the object to be managed, and monitoring data of wearable equipment worn by the object to be managed.
According to another aspect of the present disclosure, there is provided a health management apparatus including: the acquisition module is used for acquiring various data source information of the object to be managed; the first determining module is used for determining the health attribute of the object to be managed according to the multiple data source information, wherein the health attribute comprises multiple health indexes; the second determining module is used for determining an evaluation result of the object to be managed on each dimension to be evaluated according to a health index related to the dimension to be evaluated in the health attributes; and the management module is used for carrying out health management on the object to be managed according to the evaluation result of the object to be managed on each dimension to be evaluated.
Optionally, the first determining module is specifically configured to: for each kind of data source information, determining a feature extraction mode of the data source information according to the acquisition cycle of the data source information; extracting the features in the data source information according to the feature extraction mode; and determining the health attribute of the object to be managed according to the characteristics in various data source information.
Optionally, the first determining module is further configured to: for each kind of data source information, determining whether the data source information is real-time information according to the acquisition cycle of the data source information; when the data source information is real-time information, determining that the feature extraction mode of the data source information comprises short-term feature extraction in a first time period and long-term feature extraction in a second time period; wherein the length of the first time period is less than the length of the second time period; and when the data source is non-real-time information, determining that the feature extraction mode of the data source information comprises long-term feature extraction in a second time period.
Optionally, the dimension to be evaluated comprises a health status dimension, wherein the health status dimension comprises a plurality of health status sub-dimensions; the second determining module is specifically configured to: for each health state sub-dimension in the health state dimensions, determining a sub-evaluation result of the object to be managed on the health state sub-dimension according to a plurality of target health indexes related to the health state sub-dimension in the health attributes; and determining the evaluation result of the object to be managed on the health state dimension according to the sub-evaluation results of the object to be managed on the various health state sub-dimensions.
Optionally, when the health status sub-dimension is a mental health status sub-dimension, the second determining module is further configured to: carrying out weighted summation on numerical values of a plurality of target health indexes related to the psychological health state sub-dimension in the health attributes to obtain weighted summation results; and determining the weighted summation result as a sub-evaluation result of the object to be managed on the mental health state sub-dimension.
Optionally, when the health status sub-dimension is a mental health status sub-dimension, the second determining module is further configured to: carrying out weighted summation on numerical values of a plurality of target health indexes related to the psychological health state sub-dimension in the health attributes to obtain weighted summation results; and determining the weighted summation result as a sub-evaluation result of the object to be managed on the psychological health state sub-dimension.
Optionally, when the health state sub-dimension is a physiological health state sub-dimension, the second determining module is further configured to: determining a criticality of a plurality of target disease symptoms associated with the physiological health status sub-dimension and a correlation between each of the target health indicators and each of the target disease symptoms; for each of the target health indicators, determining a weight for the target health indicator based on the criticality of the plurality of target disease symptoms and the correlation between the target health indicator and each of the target disease symptoms; and determining the sub-evaluation result of the object to be managed on the physiological health state sub-dimension according to the numerical value and the weight of each target health index.
Optionally, the dimension to be evaluated comprises a lifestyle dimension, wherein the lifestyle dimension comprises a plurality of lifestyle sub-dimensions; the second determining module is further configured to: determining, for each lifestyle sub-dimension of the lifestyle dimension, a degree of risk of a plurality of target disease symptoms associated with the lifestyle sub-dimension and a degree of correlation between each of the target health indicators and each of the target disease symptoms; for each of the target health indicators, determining a weight for the target health indicator based on the criticality of the plurality of target disease symptoms and the degree of correlation between the target health indicator and each of the target disease symptoms; determining a sub-evaluation result of the object to be managed on the life style sub-dimension according to the numerical value and the weight of each target health index; and determining the evaluation result of the object to be managed in the life style dimension according to the sub-evaluation result of the object to be managed in the plurality of life style sub-dimensions.
Optionally, the dimension to be evaluated includes a health status dimension, and the apparatus further includes: a third determining module, configured to determine a reference object data set, where the reference object data set includes: portrait information of a reference object, and an evaluation result of the reference object in a health state dimension in an adjacent third time period; the prediction module is used for predicting a target evaluation result of the object to be managed on the health state dimension according to the evaluation result and the similarity between the object to be managed and the reference object; and the recommending module is used for recommending the target health state of the object to be managed according to the target evaluation result.
Optionally, the prediction module is specifically configured to: determining an evaluation result difference value of the reference object in the health state dimension in a third adjacent time period; predicting the evaluation result difference of the object to be managed according to the evaluation result difference of the reference objects and the similarity between the reference objects and the object to be managed; and predicting a target evaluation result of the object to be managed in the health state dimension according to a historical evaluation result of the object to be managed in the health state dimension and an evaluation result difference value of the object to be managed in the health state dimension.
Optionally, the dimension to be evaluated further includes a lifestyle dimension, and the apparatus further includes: the input module is used for inputting a historical evaluation result of the object to be managed on the health state dimension, an evaluation result difference value of the object to be managed on the health state dimension, a historical evaluation result of the object to be managed on the life style dimension and portrait information of the object to be managed into a preset machine learning model, and obtaining the evaluation result difference value of the object to be managed on the life style dimension, which is output by the machine learning model; a fourth determining module, configured to determine, according to an evaluation result difference of the object to be managed in the lifestyle dimension, an adjustment amount of a target health indicator related to the lifestyle dimension in the health attribute; and the guidance module is used for guiding the health indexes of the object to be managed according to the adjustment quantity of the target health indexes related to the life style dimension in the health attributes.
Optionally, the data source information includes at least two of the following information: basic data of the object to be managed, environment data of the environment where the object to be managed is located, questionnaire data of the object to be managed, and monitoring data of wearable equipment worn by the object to be managed.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of the first aspect of the present disclosure.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method of an embodiment of the first aspect of the present disclosure.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic illustration according to a first embodiment of the present disclosure;
FIG. 2 is a schematic diagram according to a second embodiment of the present disclosure;
FIG. 3 is a schematic diagram according to a third embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a health dimension structure in accordance with an embodiment of the present disclosure;
FIG. 5 is a schematic diagram according to a fourth embodiment of the present disclosure;
fig. 6 is a schematic diagram of a lifestyle dimension structure according to an embodiment of the present disclosure;
FIG. 7 is a schematic diagram according to a fifth embodiment of the present disclosure;
FIG. 8 is a schematic diagram according to a sixth embodiment of the present disclosure;
FIG. 9 is a schematic view of health management according to an embodiment of the present disclosure;
FIG. 10 is a schematic diagram according to a seventh embodiment of the present disclosure;
FIG. 11 is a block diagram of an electronic device for implementing a health management method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
With the development of medicine, many diseases can be dealt with by measures such as medicines or surgery, but in many cases, the negative experience of the diseases cannot be eradicated, resulting in a long-term decline in quality of life. Health is the basis for ensuring quality of life. In fact, many diseases can be prevented through scientific intervention, and effective health management can improve the health level of users and improve the quality of life. Wherein, the health management is to monitor and manage the health status and risk factors of individuals and groups to achieve the purpose of preventing diseases.
In the related art, the general health problems appearing in the user group are mainly evaluated by collecting health data and the like of the user group, and the user group is subjected to health management aiming at the general health problems appearing in the user group. For example, the health management is performed in the form of centralized intervention such as health lectures or expert consultation. However, in order to ensure the safety of medical data, the hospitalization information of the electronic medical record needs to be authorized for use, and the practical obstacles in the development and utilization process are huge. On the other hand, a considerable part of the population in China still has no good regular physical examination habits, available data is very sparse, even for some users, health data can be obtained only after the users are admitted with diseases and is late, and on the premise that health problems occur, health management is performed after the user population is cured, personalized active health management cannot be performed on the users, and therefore the health level of the users cannot be effectively improved.
In order to solve the above problems, the present disclosure provides a health management method, an apparatus, an electronic device, and a storage medium.
Fig. 1 is a schematic diagram according to a first embodiment of the present disclosure. It should be noted that the health management method according to the embodiment of the present disclosure may be applied to a health management apparatus according to the embodiment of the present disclosure, and the apparatus may be configured in an electronic device. The electronic device may be a mobile terminal, for example, a mobile phone, a tablet computer, a personal digital assistant, and other hardware devices with various operating systems.
As shown in fig. 1, the health management method may include the steps of:
step 101, obtaining various data source information of an object to be managed.
In order to better perform health management on the object to be managed, various data source information of the object to be managed can be acquired through different acquisition modes. It should be noted that the data source information includes at least two of the following information: the management system comprises basic data of an object to be managed, environment data of the environment where the object to be managed is located, questionnaire data of the object to be managed and monitoring data of wearable equipment worn by the object to be managed.
For example, the basic data of the object to be managed may include, but is not limited to, the age of the object to be managed, the sex of the object to be managed, the height of the object to be managed, the weight of the object to be managed, and the like, and the basic data of the object to be managed may be obtained through the data filled in by the object to be managed; the environmental data of the environment where the object to be managed is located can include, but is not limited to, information such as city-level geography, climate, society and economy, and the environmental data of the environment where the object to be managed is located can be obtained through the internet of the environment where the object to be managed is located; questionnaire data of the object to be managed can include but is not limited to lifestyle (smoking, drinking and sports) data, occupation, family medical history, past medical history and the like of the object to be managed, and can be acquired in a questionnaire manner; the monitoring data of the wearable device worn by the object to be managed can include, but is not limited to, the heart rate of the object to be managed, the sleep of the object to be managed, the activity level of the object to be managed, and the like. Monitoring data of wearable equipment worn by the object to be managed can be acquired by monitoring the object to be managed through the wearable equipment worn by the object to be managed.
Step 102, determining health attributes of the object to be managed according to the information of the various data sources, wherein the health attributes comprise a plurality of health indexes.
In the disclosed embodiments, health attributes of an object to be managed may be determined from a variety of data information in a variety of data sources, where the health attributes may include a plurality of health indicators.
For example, for the monitoring data of the wearable device worn by the object to be managed in the multiple data source information, for example, the health index of the object to be managed may be determined according to the heart rate of the object to be managed, the sleep of the object to be managed, and the activity of the object to be managed in the monitoring data of the wearable device worn by the object to be managed, and includes: the highest heart rate of the day, the average heart rate of the subject to be managed, the deep sleep time of the subject to be managed, and the like. For another example, for the lifestyle (smoking, drinking, and exercise) data of the object to be managed in the questionnaire data of the object to be managed in the plurality of data source information, it may be determined that the health index of the object to be managed includes: a period of smoking (e.g., one month), a period of drinking, etc. For another example, for the basic data of the object to be managed in the multiple data source information, the health index of the object to be managed may be determined to include: sex, age, weight, and the like; for another example, for the environment data of the environment where the object to be managed is located, the health index of the object to be managed may be determined to include: the climate, economy and society of the environment, etc.
And 103, determining an evaluation result of the object to be managed on the dimension to be evaluated according to the health index related to the dimension to be evaluated in the health attributes for each dimension to be evaluated of the object to be managed.
Optionally, the dimension to be evaluated of the object to be managed may include, but is not limited to, a health status dimension and a lifestyle dimension.
As an example, for the health status dimension of the object to be managed, the evaluation result of the object to be managed on the health status dimension may be determined according to the health index related to the health status dimension in the health attribute.
As another example, for the life style dimension of the object to be managed, the evaluation result of the object to be managed in the life style dimension may be determined according to the health index related to the life style dimension in the health attribute.
And 104, performing health management on the object to be managed according to the evaluation result of the object to be managed on each dimension to be evaluated.
Further, the health management of the object to be managed can be performed according to the evaluation result of the object to be managed in each dimension to be evaluated, for example, the health management of the object to be managed can be performed according to the health state score value of the object to be managed in the health state dimension and the life style score value of the object to be managed in the life style dimension, for example, the life style of the object to be managed is adjusted to select a healthy life style, the healthy behavior habit is improved, the health state of the object to be managed is actively adjusted, and the like.
In summary, by acquiring various data source information of the object to be managed; determining health attributes of the object to be managed according to the information of the various data sources, wherein the health attributes comprise a plurality of health indexes; aiming at each dimension to be evaluated of the object to be managed, determining an evaluation result of the object to be managed on the dimension to be evaluated according to a health index related to the dimension to be evaluated in the health attributes; according to the method, the health management is carried out on the object to be managed according to the evaluation result of the object to be managed on each dimension to be evaluated, according to the health indexes related to the dimension to be evaluated in the health attributes, the multiple dimensions to be evaluated of the object to be managed are evaluated and analyzed, further, the object to be managed is subjected to the health management according to the evaluation and analysis results of the multiple dimensions to be evaluated, the personalized and active health management of the object to be managed can be achieved, and the health level of a user is effectively improved.
In order to accurately determine the health attribute of the object to be managed in various aspects, as shown in fig. 2, fig. 2 is a schematic diagram according to a second embodiment of the present disclosure, in the embodiment of the present disclosure, the features in the data source information are extracted according to the feature extraction manner of each data source information, and the health attribute of the object to be managed is determined according to the features in the extracted data source information, and the embodiment shown in fig. 2 may include the following steps:
step 201, obtaining various data source information of the object to be managed.
Step 202, for each data source information, determining a feature extraction mode of the data source information according to the acquisition cycle of the data source information.
Optionally, for each type of data source information, determining whether the data source information is real-time information according to an acquisition cycle of the data source information; when the data source information is real-time information, determining a feature extraction mode of the data source information to comprise short-term feature extraction in a first time period and long-term feature extraction in a second time period; wherein the length of the first time period is less than the length of the second time period; and when the data source is non-real-time information, determining a feature extraction mode of the data source information to comprise long-term feature extraction in a second time period.
That is to say, the real-time performance of the data information provided by different data source information is different, wherein the monitoring data of the wearable device worn by the object to be managed and the environment data of the environment where the object to be managed is located can be obtained in real time, the monitoring data of the wearable device worn by the object to be managed and the environment data of the environment where the object to be managed is located can provide information with higher time resolution, so that, for the monitoring data of the wearable device worn by the object to be managed and the environment data of the environment where the object to be managed is located, the short-term features of the object to be managed in a first time period (for example, within one day) and the long-term features of the object to be managed in a second time period (for example, one month) can be described, and the feature extraction modes of the monitoring data of the wearable device worn by the object to be managed and the environment data of the environment where the object to be managed are located include short-term feature extraction in the first time period and long-term feature extraction in the second time period; the basic data of the object to be managed and the questionnaire data of the object to be managed cannot be acquired in real time, the updating is performed actively and manually by a user, the updating time is usually long, the basic data of the object to be managed and the questionnaire data of the object to be managed can describe the long-term characteristics of the object to be managed in the second time period, and the characteristic extraction mode of the basic data of the object to be managed and the questionnaire data of the object to be managed can be determined to include the long-term characteristic extraction in the second time period. It should be noted that the length of the first time period is smaller than the length of the second time period.
And step 203, extracting the features in the data source information according to the feature extraction mode.
Optionally, for features in the monitoring data of the wearable device worn by the object to be managed and the environmental data of the environment where the object to be managed is located, a short-term feature in a first time period and a long-term feature in a second time period of the object to be managed may be extracted in a short-term feature extraction manner in the first time period and in a long-term feature extraction manner in the second time period, respectively; the characteristics in the basic data of the object to be managed and the questionnaire data of the object to be managed can acquire the long-term characteristics of the object to be managed in the second time period in a long-term characteristic extraction mode in the second time period. For example, the length of the first time period is one day, the length of the second time period is one month, the features in the monitoring data of the wearable device worn by the object to be managed and the environmental data of the environment where the object to be managed is located may be extracted in one day, the short-term features of the object to be managed in one day may be obtained, and the features in the monitoring data of the wearable device worn by the object to be managed and the environmental data of the environment where the object to be managed is located may be extracted in one month, and the long-term features of the object to be managed in one month may be obtained. For another example, the features in the basic data of the object to be managed and the questionnaire data of the object to be managed are extracted within one month, and the long-term features of the object to be managed within one month can be obtained.
Step 204, determining the health attribute of the object to be managed according to the characteristics in the various data source information.
It is understood that the features in the data source information may include short-term features in the first time period and long-term features in the second time period of the object to be managed, and according to the short-term features in the first time period and the long-term features in the second time period of the object to be managed, the corresponding determinable health attributes of the object to be managed may include short-term indicators in the first time period and long-term indicators in the second time period, for example, the short-term indicators in the first time period may include: the highest exercise heart rate, exercise intensity, yesternight deep sleep duration, average heart rate, day activity level, night activity level, sedentary times, calorie consumption, etc. on the day. The long-term indicators for the second time period may include: the regularity of the time to sleep in the last month, the total number of runs in the last month, the total mileage of runs in the last month, the local weather in the last month, the daily average intake of fat in the last month, the smoking amount in the last month, the alcohol intake in the last month, the drinking times in the last month and the like.
Step 205, determining an evaluation result of the object to be managed in the dimension to be evaluated according to the health index related to the dimension to be evaluated in the health attribute for each dimension to be evaluated of the object to be managed.
And step 206, performing health management on the object to be managed according to the evaluation result of the object to be managed on each dimension to be evaluated.
Steps 201, 205 to 206 may be implemented by any one of the embodiments of the present disclosure, which is not limited in the embodiments of the present disclosure and will not be described again.
In summary, by acquiring various data source information of the object to be managed; for each kind of data source information, determining a characteristic extraction mode of the data source information according to the acquisition cycle of the data source information; extracting features in the data source information according to a feature extraction mode; determining the health attribute of the object to be managed according to the characteristics in various data source information; aiming at each dimension to be evaluated of the object to be managed, determining an evaluation result of the object to be managed on the dimension to be evaluated according to a health index related to the dimension to be evaluated in the health attribute; and performing health management on the object to be managed according to the evaluation result of the object to be managed on each dimension to be evaluated. Therefore, according to different extraction modes of the data source information, the characteristics in various data source information are obtained, so that the health attribute of the object to be managed can be more accurately determined in multiple aspects, multiple dimensions to be evaluated of the object to be managed are evaluated and analyzed according to health indexes related to the dimensions to be evaluated in the health attribute, the object to be managed is further subjected to health management according to evaluation and analysis results of the multiple dimensions to be evaluated, personalized active health management of the object to be managed can be achieved, and the health level of a user is effectively improved.
Optionally, the dimension to be evaluated of the object to be managed may include, but is not limited to, a health status dimension and a lifestyle dimension, as shown in fig. 3, fig. 3 is a schematic diagram according to a third embodiment of the present disclosure, and in an embodiment of the present disclosure, in order to accurately obtain the evaluation result of the object to be managed in the health status dimension, the evaluation result of the object to be managed in the health status dimension may be determined according to sub-evaluation results of the object to be managed in various health sub-dimensions. The embodiment shown in fig. 3 may include the following steps:
step 301, obtaining various data source information of the object to be managed.
Step 302, determining a health attribute of the object to be managed according to the information of the plurality of data sources, wherein the health attribute comprises a plurality of health indexes.
Step 303, for each health status sub-dimension in the health status dimensions, determining a sub-evaluation result of the object to be managed on the health status sub-dimension according to a plurality of target health indicators related to the health status sub-dimension in the health attributes.
In the embodiment of the present disclosure, as shown in fig. 4, the health status may include a plurality of health status sub-dimensions, and the health status sub-dimension may be a mental health status sub-dimension or a physical health status sub-dimension, and the sub-evaluation results of the object to be managed in the mental health status sub-dimension and the physical health status sub-dimension may be determined respectively.
As an example, when the health status sub-dimension is a mental health status sub-dimension, performing weighted summation on numerical values of a plurality of target health indicators related to the mental health status sub-dimension in the health attributes to obtain a weighted summation result; and determining the weighted summation result as a sub-evaluation result of the object to be managed on the psychological health state sub-dimension.
For example, the values of a plurality of target Health indicators related to the mental Health status sub-dimension in the Health attributes of the subject to be managed, such as the time of no interest in doing things, the time of fatigue or lack of vitality, or the time of difficulty in falling asleep, etc., may be determined by the characteristics in Questionnaire data of the subject to be managed (e.g., PHQ-9 (Depression self-assessment Scale), HDRS-17 (Hamilton Depression Scale)). According to preset weights corresponding to a plurality of target health indexes, carrying out weighted summation on numerical values of the plurality of target health indexes related to the psychological health state sub-dimension in the health attributes to obtain a weighted summation result, and taking the weighted summation result as a sub-evaluation result of the object to be managed on the psychological health state sub-dimension.
As another example, when the health status sub-dimension is a physiological health status sub-dimension, a degree of risk of a plurality of target disease symptoms associated with the physiological health status sub-dimension and a degree of correlation between each target health indicator and each target disease symptom may be determined, and for each target health indicator, a weight of the target health indicator is determined according to the degree of risk of the plurality of target disease symptoms and the degree of correlation between the target health indicator and each target disease symptom; and determining the sub-evaluation result of the object to be managed on the physiological health state sub-dimension according to the numerical value and the weight of each target health index.
For example, taking the physiological health status sub-dimension as the cardiovascular health status as an example, the first step, which is to determine the cardiovascular health status related to the cardiovascular health status through meta-analysis in medical literatureThe target health indicator may include: sleep heart rate, resting heart rate, maximum heart rate, and post-exercise heart recovery rate (recovery rate = (exercise heart rate-1 minute post-heart rate) ÷ 10). Assuming that the set of symptoms of multiple target diseases is D = { D = { D j I j =1,2,. K }, where d j Representing symptoms of each target disease; the multiple target health indexes are set as X = { X = ×) i |i＝1,2,...N}，x i A numerical value representing each target health indicator; the correlation degree between each target health index and each target disease symptom is quantitatively evaluated by using an aOR (Adjusted Odds Ratio) value, and the correlation degree between various target health indexes and various target disease symptoms is A = { A = { i |i＝1,2,...N},A i Representing the degree of correlation between each target health indicator and each target disease symptom, wherein A i ＝{a ij |j＝1,2,...K}；
Second, the following formula may be determined to determine the criticality of various target disease symptoms associated with cardiovascular health status:
H＝{h j |j＝1,2,...K} (1)
wherein h is i Indicates the degree of harmfulness of the symptoms of each target disease, h i ＝log 2 (m j /p j *10 5 )，m j Denotes d j At an economic cost of p j Denotes d j Incidence of d j Representing symptoms of each target disease;
thirdly, according to the harmfulness H of the symptoms of the target diseases and the correlation degree A between the target health index and each symptom of the target diseases i Determining a weight (denoted as w) for the target health indicator i ) The concrete expression formula is as follows:
w i ＝log 2 (H*A i ) (2)
fourthly, according to the weight w of each target health index i Numerical value x of each target health index i And determining an evaluation result (marked as S) of the object to be managed on the cardiovascular health state, wherein the specific expression formula is as follows:
S＝∑w i x i /∑w i (3)
it should be noted that the evaluation results of the physiological health status sub-dimensions in other aspects, such as respiration, motion, etc., can be evaluated by redefining the target health index and then performing the above steps.
And step 304, determining the evaluation result of the object to be managed on the health state dimension according to the sub-evaluation results of the object to be managed on the multiple health state sub-dimensions.
In the embodiment of the present disclosure, weights corresponding to a plurality of health state sub-dimensions may be preset, the sub-evaluation results of the object to be managed in the plurality of health state sub-dimensions are subjected to weighted summation to obtain a weighted summation result, and the weighted summation result is used as the evaluation result of the object to be managed in the health state dimension.
And 305, performing health management on the object to be managed according to the evaluation result of the object to be managed on the health state dimension.
Steps 301 to 302 and 305 may be implemented by any one of the embodiments of the present disclosure, which is not limited in the embodiments of the present disclosure and will not be described again.
In summary, by acquiring various data source information of the object to be managed; determining health attributes of the object to be managed according to the information of the various data sources, wherein the health attributes comprise a plurality of health indexes; for each health state sub-dimension in the health state dimensions, determining a sub-evaluation result of the object to be managed on the health state sub-dimension according to a plurality of target health indexes related to the health state sub-dimension in the health attributes; determining the evaluation result of the object to be managed on the health state dimension according to the sub-evaluation results of the object to be managed on the multiple health state sub-dimensions; and performing health management on the object to be managed according to the evaluation result of the object to be managed on each dimension to be evaluated. Therefore, the evaluation result of the object to be managed in the health state dimension is determined according to the sub-evaluation results of the object to be managed in the various health sub-dimensions, the evaluation result of the object to be managed in the health state dimension can be accurately obtained, the object to be managed is subjected to health management according to the evaluation result of the object to be managed in the health state dimension, personalized and active health management of the object to be managed can be achieved, and the health level of a user is effectively improved.
In order to accurately obtain the evaluation result of the object to be managed in the life style dimension, as shown in fig. 5, fig. 5 is a schematic diagram according to a fourth embodiment of the present disclosure, in the embodiment of the present disclosure, the evaluation result of the object to be managed in the life style dimension may be determined according to the sub-evaluation results of the object to be managed in the sub-dimensions of multiple life styles, and the embodiment shown in fig. 5 includes the following steps:
step 501, obtaining various data source information of an object to be managed.
Step 502, determining health attributes of the object to be managed according to the information of the plurality of data sources, wherein the health attributes include a plurality of health indexes.
Step 503, for each lifestyle sub-dimension in the lifestyle dimensions, determining a sub-evaluation result of the object to be managed on the lifestyle sub-dimension according to a plurality of target health indicators related to the lifestyle sub-dimension in the health attributes.
In the disclosed embodiment, as shown in fig. 6, the lifestyle dimensions may include a sleep sub-dimension, an activity sub-dimension, a fitness sub-dimension, a stress sub-dimension, a diet sub-dimension, a care sub-dimension, and the like. The target health index related to the sleep sub-dimension may be daily sleep quality or sleep rules, the target health index related to the activity sub-dimension may be daily activity amount rules, such as whether sitting for a long time, fighting for a long time, or continuous micro-activity, the target health index related to the stress sub-dimension may be exercise intensity, exercise time, exercise frequency, or exercise rules, etc., the target health index related to the diet sub-dimension may be relative amount and regularity of fatigue and recovery of the user, the target health index related to the diet sub-dimension may be amount and regularity of nutrition intake or alcohol intake of the user, and the target health index related to the care sub-dimension may be frequency, tooth brushing time, or defecation frequency, etc., of the user.
Wherein, the data required by the sleep sub-dimension, the activity sub-dimension, the fitness sub-dimension, and the stress sub-dimension can be acquired by a biological sensor (such as an acceleration sensor, a gyroscope) and a biological measurement algorithm (such as a motion recognition algorithm, a sleep staging algorithm, a stress measurement algorithm) integrated by the wearable device or by a questionnaire; the diet and care sub-dimensions may be provided with relevant information via a user questionnaire.
Optionally, for each lifestyle sub-dimension of the lifestyle dimension, determining a degree of risk of a plurality of target disease symptoms associated with the lifestyle sub-dimension and a degree of correlation between each target health indicator and each of the target disease symptoms; for each target health index, determining the weight of the target health index according to the harmfulness of the various target disease symptoms and the correlation between the target health index and each target disease symptom; and determining the sub-evaluation result of the object to be managed on the life style sub-dimension according to the numerical value and the weight of each target health index.
For example, taking the lifestyle sub-dimension as the sleep sub-dimension as an example, the target health indicators related to the sleep sub-dimension may be sleep-in time, sleep midpoint, deep sleep time, half-way wake time, REM (rapid eye movement) time, sleep efficiency, arousal ratio, deep-light ratio, regularity of sleep-in time, etc., the risk of various target disease symptoms related to the sleep sub-dimension may be determined by the above formula (1), the correlation between each target health indicator and each target disease symptom may be obtained by meta-analysis, and the weight of the target health indicator may be determined according to the risk of various target disease symptoms and the correlation between the target health indicator and each target disease symptom, specifically, the weight of the target health indicator may be determined by the above formula (2), and further, the value z of each target health indicator related to the sleep sub-dimension may be determined according to i And a weight w i Determining a sub-evaluation result L of the object to be managed on the sleep sub-dimension, and specifically calculating through the following formula:
L＝∑w i z i /∑w i (4)
step 504, determining the evaluation result of the object to be managed in the life style dimension according to the sub-evaluation results of the object to be managed in the multiple life style sub-dimensions.
In the embodiment of the present disclosure, weights corresponding to multiple lifestyle sub-dimensions may be preset, weighted summation is performed on sub-evaluation results of an object to be managed in the multiple lifestyle sub-dimensions to obtain a weighted summation result, and the weighted summation result is used as an evaluation result of the object to be managed in the lifestyle dimensions.
And 505, performing health management on the object to be managed according to the evaluation result of the object to be managed on the life style dimension.
It should be noted that, steps 501 to 502 and step 505 may be implemented by any one of the embodiments of the present disclosure, and the embodiments of the present disclosure are not limited thereto and are not described again.
In conclusion, various data source information of the object to be managed is obtained; determining health attributes of the object to be managed according to the information of the various data sources, wherein the health attributes comprise a plurality of health indexes; for each life style sub-dimension in the life style dimensions, determining a sub-evaluation result of the object to be managed on the life style sub-dimension according to a plurality of target health indexes related to the life style sub-dimension in the health attributes; determining the evaluation result of the object to be managed on the life style dimension according to the sub-evaluation results of the object to be managed on the sub-dimensions of various life styles; and performing health management on the object to be managed according to the evaluation result of the object to be managed on each dimension to be evaluated. Therefore, the evaluation result of the object to be managed in the life style dimension is determined according to the sub-evaluation results of the object to be managed in the sub-dimensions of the various life styles, the evaluation result of the object to be managed in the life style dimension can be accurately obtained, the object to be managed is subjected to health management according to the evaluation result of the object to be managed in the life style dimension, personalized and active health management of the object to be managed can be achieved, and the health level of a user is effectively improved.
In order to provide a clear target health status for an object to be managed and further improve the health level of a user, as shown in fig. 7, fig. 7 is a schematic diagram according to a fifth embodiment of the present disclosure, in the embodiment of the present disclosure, a target health status recommendation may be performed on the object to be managed, and the embodiment shown in fig. 7 may include the following steps:
step 701, obtaining various data source information of an object to be managed.
Step 702, determining health attributes of the object to be managed according to the information of the plurality of data sources, wherein the health attributes include a plurality of health indexes.
Step 703, determining an evaluation result of the object to be managed in the health status dimension according to the health index related to the health status dimension in the health attribute for the health status dimension of the object to be managed.
Step 704, performing health management on the object to be managed according to the evaluation result of the object to be managed in the health state dimension.
Step 705, determining a reference object data set, wherein the reference object data set comprises: the portrait information of the reference object, and the evaluation result of the reference object in the health status dimension in the adjacent third time period.
In the embodiment of the present disclosure, the profile information of the reference object may be established, for example, the gender, age, height, weight, etc. of the reference object may be included, and furthermore, the evaluation result in the dimension of the initial health status of the plurality of reference objects is assumed to be s 0 The evaluation result of the reference object in the health status dimension separated by a third time period (e.g., 42 days) is s 1 The evaluation result of the reference object in the health status dimension in the adjacent third time period is s 0 And s 1 。
And step 706, predicting a target evaluation result of the object to be managed on the health state dimension according to the evaluation result and the similarity between the object to be managed and the reference object.
Optionally, determining an evaluation result difference value of the reference object in the health state dimension in a third adjacent time period; predicting the evaluation result difference of the object to be managed according to the evaluation result difference of the reference objects and the similarity between the reference objects and the object to be managed; and predicting a target evaluation result of the object to be managed in the health state dimension according to a historical evaluation result of the object to be managed in the health state dimension and an evaluation result difference value of the object to be managed in the health state dimension.
That is to say, the evaluation results of the reference objects in the adjacent third time periods in the health status dimension may be subjected to phase difference, the evaluation result difference of the reference objects in the adjacent third time periods in the health status dimension is determined, the image information of the reference objects and the basic data of the object to be managed may be calculated (for example, euclidean distance or cosine distance, etc.), the similarity between the multiple reference objects and the object to be managed is obtained, and the evaluation result difference of the object to be managed is predicted, which may be specifically calculated by the following formula:
Δs＝∑r i Δs i /∑r i (5)
where Δ s denotes an evaluation result difference for predicting an object to be managed, r i Representing the degree of similarity, Δ s, between a plurality of reference objects and the object to be managed i And representing the difference of the evaluation results of the reference object in the health state dimension in the adjacent third time period. Further, the difference value of the evaluation results of the objects to be managed in the health state dimension is added to the historical evaluation results of the objects to be managed in the health state dimension, and the target evaluation results of the objects to be managed in the health state dimension can be predicted.
And step 707, performing target health state recommendation on the object to be managed according to the target evaluation result.
Further, according to the target evaluation result, the health state of the reference object similar to the target evaluation result in the reference object data set can be acquired, and the health state is recommended to the object to be managed as the target health state.
It should be noted that, steps 701 to 704 may be implemented by any one of the embodiments of the present disclosure, and the embodiments of the present disclosure are not limited to this and are not described again.
In summary, by acquiring various data source information of the object to be managed; determining health attributes of the object to be managed according to the information of the various data sources, wherein the health attributes comprise a plurality of health indexes; aiming at the health state dimension of the object to be managed, determining an evaluation result of the object to be managed on the health state dimension according to a health index related to the health state dimension in the health attributes; according to the evaluation result of the object to be managed on the health state dimension, carrying out health management on the object to be managed; determining a reference object data set, wherein the reference object data set comprises: the portrait information of the reference object and the evaluation result of the reference object in the health state dimension in the adjacent third time period; predicting a target evaluation result of the object to be managed on the health state dimension according to the evaluation result and the similarity between the object to be managed and the reference object; and according to the target evaluation result, recommending the target health state of the object to be managed. Therefore, the health state dimension of the object to be managed is evaluated and analyzed according to the health indexes related to the health state dimension in the health attributes, and then the object to be managed is subjected to health management according to the evaluation and analysis result of the health state dimension, so that the object to be managed can be subjected to personalized and active health management, the health level of a user is effectively improved, the object to be managed is subjected to target health state recommendation according to the target evaluation result, a clear target health state can be provided for the object to be managed, and the health level of the user is further improved.
In order to promote the object to be managed to gradually reach the target health state and further improve the health level of the user, as shown in fig. 8, fig. 8 is a schematic diagram according to a sixth embodiment of the present disclosure, in the embodiment of the present disclosure, health index guidance may be performed on the object to be managed, and the embodiment shown in fig. 8 may include the following steps:
step 801, obtaining various data source information of an object to be managed.
Step 802, determining health attributes of an object to be managed according to various data source information, wherein the health attributes include a plurality of health indicators.
And 803, determining an evaluation result of the object to be managed in the life style dimension according to the health indexes related to the health state dimension and the life style dimension in the health attributes aiming at the health state dimension and the life style dimension of the object to be managed.
And step 804, performing health management on the object to be managed according to the evaluation results of the object to be managed in the health state dimension and the life style dimension.
Step 805, determining a reference object data set, wherein the reference object data set comprises: profile information of a reference object, and an assessment of the reference object in a health state dimension over an adjacent third time period.
Step 806, predicting a target evaluation result of the object to be managed in the health status dimension according to the evaluation result and the similarity between the object to be managed and the reference object.
And step 807, recommending the target health state of the object to be managed according to the target evaluation result.
Step 808, inputting the historical evaluation result of the object to be managed in the health state dimension, the evaluation result difference of the object to be managed in the health state dimension, the historical evaluation result of the object to be managed in the life style dimension, and the portrait information of the object to be managed into a preset machine learning model, and obtaining the evaluation result difference of the object to be managed in the life style dimension output by the machine learning model.
For example, the historical evaluation result M of the object to be managed in the dimension of health status can be obtained 0 Evaluation result difference value deltas of the object to be managed in the dimension of health state and historical evaluation result L of the object to be managed in the dimension of life style 0 And portrait information (such as sex, age, height, weight and the like) of the object to be managed are input into a preset machine learning model, and the machine learning model can output an evaluation result difference value of the object to be managed in a life style dimension. The machine learning model may be a deep neural network, a regression tree, or a linear regression.
And step 809, determining the adjustment amount of the target health index related to the life style dimension in the health attribute according to the evaluation result difference value of the object to be managed in the life style dimension.
For example,the function used in equation (4) can be symbolized as g: { z i → L, since g corresponding to the formula (4) is a linear function, the adjustment amount of the target health index related to the lifestyle dimension can be directly derived from the difference of the evaluation results of the objects to be managed in the lifestyle dimension by the inverse function g' of g.
And 810, conducting health index guidance on the object to be managed according to the adjustment quantity of the target health index related to the life style dimension in the health attribute.
For example, a health path is established according to the adjustment amount of a target health index related to the life style dimension in the health attribute, and health index guidance is performed by using quantitative selection of a healthy life style and improvement of a healthy behavior habit as an operation means, so that the object to be managed is promoted to gradually reach the target health state.
It should be noted that, steps 801 to 807 may be implemented by any one of the embodiments of the present disclosure, and the embodiments of the present disclosure are not limited thereto and are not described again.
In summary, by acquiring various data source information of the object to be managed; determining health attributes of the object to be managed according to the information of the various data sources, wherein the health attributes comprise a plurality of health indexes; aiming at the health state dimension and the life style dimension of the object to be managed, determining the evaluation result of the object to be managed on the life style dimension according to the health indexes related to the health state dimension and the life style dimension in the health attributes; according to the evaluation results of the object to be managed in the health state dimension and the life style dimension, performing health management on the object to be managed; determining a reference object data set, wherein the reference object data set comprises: portrait information of a reference object, and an evaluation result of the reference object in a health state dimension in an adjacent third time period; predicting a target evaluation result of the object to be managed on the health state dimension according to the evaluation result and the similarity between the object to be managed and the reference object; according to the target evaluation result, recommending the target health state of the object to be managed; inputting a historical evaluation result of the object to be managed in the dimension of the health state, an evaluation result difference of the object to be managed in the dimension of the health state, a historical evaluation result of the object to be managed in the dimension of the life style and portrait information of the object to be managed into a preset machine learning model, and acquiring an evaluation result difference of the object to be managed in the dimension of the life style output by the machine learning model; determining the adjustment amount of a target health index related to the life style dimension in the health attribute according to the evaluation result difference value of the object to be managed in the life style dimension; and conducting health index guidance on the object to be managed according to the adjustment quantity of the target health index related to the life style dimension in the health attributes. Therefore, according to the health indexes related to the life style dimension in the health attributes, the life style dimension of the object to be managed is evaluated and analyzed, and then the object to be managed is subjected to health management according to the evaluation and analysis result of the life style dimension, so that the object to be managed can be subjected to personalized and active health management, the health level of a user is effectively improved, and in addition, the adjustment quantity of the target health indexes related to the life style dimension in the health attributes is determined according to the evaluation result difference value of the object to be managed in the life style dimension; according to the adjustment amount of the target health index related to the life style dimension in the health attribute, the health index guidance is carried out on the object to be managed, the object to be managed can be promoted to gradually reach the target health state, and the health level of the user is further improved.
In order to more clearly illustrate the above embodiments, the description will now be made by way of example.
As shown in fig. 9, environment data of an environment in which an object to be managed is located, sensing data (monitoring data of a wearable device worn by the object to be managed), questionnaire data, and registration data (basic data of the object to be managed) are acquired, the environment data, the sensing data, the questionnaire data, and the registration data are taken as various data source information, the sensing data of the object to be managed and the environment data of the environment in which the object to be managed is located may describe short-term characteristics of the object to be managed in a first period of time (e.g., one day) and long-term characteristics of the object to be managed in a second period of time (e.g., one month), the registration data of the object to be managed and the questionnaire data of the object to be managed may describe long-term characteristics of the object to be managed in the second period of time, a health attribute of the object to be managed is determined based on the short-term characteristics of the object to be managed in the first period of time and the long-term characteristics of the object in the second period of time, a health attribute related to a health element (health index) related to be evaluated in the health attribute, and evaluation of a current health status is an evaluation that is a non-controllable factor to be managed such as passively reacting to an organism (e.g., sleep or heart rate, etc.). Future health trends of the subject to be managed can be intervened to some extent by subjective efforts, such as managing his daily life in a healthy lifestyle. Therefore, from the perspective of active health management, health trend assessment, i.e., assessment of controllable factors such as lifestyle of the subject to be managed (e.g., work and rest, activity, stress, fitness, care or diet) can select a health goal (target health status) for a future period of time based on its current health status and trend, in combination with its life reality; the active health management system can also recommend a health target (target health state) according to the health portrait of the user through a recommendation algorithm (such as a collaborative filtering algorithm), and establish a mapping relation between the lifestyle dimension of the object to be managed and the target disease symptom through a big data mining technology. On the basis, a life style dimension quantitative adjustment scheme corresponding to the difference between the current health state and the health target (target health state) of the object to be managed is obtained through reverse analysis. Finally, health guidance is conducted by taking quantitative selection of healthy life style and improvement of healthy behavior habits as operation means, and the object to be managed is promoted to gradually reach a healthy target (target health state).
The health management method of the embodiment of the disclosure acquires various data source information of an object to be managed; determining health attributes of the object to be managed according to the information of the various data sources, wherein the health attributes comprise a plurality of health indexes; aiming at each dimension to be evaluated of the object to be managed, determining an evaluation result of the object to be managed on the dimension to be evaluated according to a health index related to the dimension to be evaluated in the health attributes; according to the method, the health management is carried out on the object to be managed according to the evaluation result of the object to be managed on each dimension to be evaluated, according to the health indexes related to the dimension to be evaluated in the health attributes, the multiple dimensions to be evaluated of the object to be managed are evaluated and analyzed, further, the object to be managed is subjected to the health management according to the evaluation and analysis results of the multiple dimensions to be evaluated, the personalized and active health management of the object to be managed can be achieved, and the health level of a user is effectively improved.
In order to implement the above embodiments, the present disclosure also proposes a health management device, and fig. 10 is a schematic diagram according to a seventh embodiment of the present disclosure.
As shown in fig. 10, the health management apparatus 1000 includes: an acquisition module 1010, a first determination module 1020, a second determination module 1030, and a management module 1040.
The acquiring module 1010 is configured to acquire various data source information of an object to be managed; a first determining module 1020, configured to determine a health attribute of an object to be managed according to multiple data source information, where the health attribute includes multiple health indicators; a second determining module 1030, configured to determine, for each dimension to be evaluated of the object to be managed, an evaluation result of the object to be managed in the dimension to be evaluated according to a health indicator related to the dimension to be evaluated in the health attribute; the management module 1040 is configured to perform health management on the object to be managed according to the evaluation result of the object to be managed in each dimension to be evaluated.
As a possible implementation manner of the embodiment of the present disclosure, the first determining module 1020 is specifically configured to: for each kind of data source information, determining a characteristic extraction mode of the data source information according to the acquisition cycle of the data source information; extracting features in the data source information according to a feature extraction mode; and determining the health attribute of the object to be managed according to the characteristics in the various data source information.
As a possible implementation manner of the embodiment of the present disclosure, the first determining module 1020 is further configured to: for each kind of data source information, determining whether the data source information is real-time information according to the acquisition cycle of the data source information; when the data source information is real-time information, determining a feature extraction mode of the data source information to comprise short-term feature extraction in a first time period and long-term feature extraction in a second time period; wherein the length of the first time period is less than the length of the second time period; and when the data source is non-real-time information, determining a feature extraction mode of the data source information to comprise long-term feature extraction in a second time period.
As a possible implementation manner of the embodiment of the present disclosure, the dimension to be evaluated includes a health state dimension, where the health state dimension includes a plurality of health state sub-dimensions; the second determining module 1030 is specifically configured to: for each health state sub-dimension in the health state dimensions, determining a sub-evaluation result of the object to be managed on the health state sub-dimension according to a plurality of target health indexes related to the health state sub-dimension in the health attributes; and determining the evaluation result of the object to be managed on the health state dimension according to the sub-evaluation results of the object to be managed on the various health state sub-dimensions.
As a possible implementation manner of the embodiment of the present disclosure, when the health state sub-dimension is a mental health state sub-dimension, the second determining module 1030 is further configured to: carrying out weighted summation on numerical values of a plurality of target health indexes related to the psychological health state sub-dimension in the health attributes to obtain weighted summation results; and determining the weighted summation result as a sub-evaluation result of the object to be managed on the psychological health state sub-dimension.
As a possible implementation manner of the embodiment of the present disclosure, when the health state sub-dimension is a physiological health state sub-dimension, the second determining module 1030 is further configured to: determining a criticality of a plurality of target disease symptoms associated with the physiological health status sub-dimension and a correlation between each target health indicator and each target disease symptom; for each target health index, determining the weight of the target health index according to the harmfulness of the various target disease symptoms and the correlation between the target health index and each target disease symptom; and determining a sub-evaluation result of the object to be managed on the physiological health state sub-dimension according to the numerical value and the weight of each target health index.
As a possible implementation of the embodiments of the present disclosure, the dimension to be evaluated includes a lifestyle dimension, wherein the lifestyle dimension includes a plurality of lifestyle sub-dimensions; a second determining module 1030 further configured to: determining, for each lifestyle sub-dimension of a lifestyle dimension, a degree of risk of a plurality of target disease symptoms associated with the lifestyle sub-dimension, and a degree of correlation between each of the target health indicators and each of the target disease symptoms; for each of the target health indicators, determining a weight for the target health indicator based on the criticality of the plurality of target disease symptoms and the degree of correlation between the target health indicator and each of the target disease symptoms; determining a sub-evaluation result of the object to be managed on the life style sub-dimension according to the numerical value and the weight of each target health index; and determining the evaluation result of the object to be managed in the life style dimension according to the sub-evaluation result of the object to be managed in the plurality of life style sub-dimensions.
As a possible implementation manner of the embodiment of the present disclosure, the dimension to be evaluated includes a health status dimension, and the health management apparatus 1000 further includes: the device comprises a third determination module, a prediction module and a recommendation module.
Wherein the third determining module is configured to determine a reference object data set, where the reference object data set includes: portrait information of a reference object, and an evaluation result of the reference object in a health state dimension in an adjacent third time period; the prediction module is used for predicting a target evaluation result of the object to be managed on the health state dimension according to the evaluation result and the similarity between the object to be managed and the reference object; and the recommending module is used for recommending the target health state of the object to be managed according to the target evaluation result.
As a possible implementation manner of the embodiment of the present disclosure, the prediction module is specifically configured to: determining an evaluation result difference value of the reference object in the health state dimension in a third adjacent time period; predicting the evaluation result difference of the object to be managed according to the evaluation result difference of the reference objects and the similarity between the reference objects and the object to be managed; and predicting a target evaluation result of the object to be managed in the health state dimension according to a historical evaluation result of the object to be managed in the health state dimension and an evaluation result difference value of the object to be managed in the health state dimension.
As a possible implementation manner of the embodiment of the present disclosure, the dimension to be evaluated further includes a life style dimension, and the health management apparatus 1000 further includes: the device comprises an input module, a fourth determination module and a guidance module.
The input module is used for inputting a historical evaluation result of the object to be managed in the health state dimension, an evaluation result difference value of the object to be managed in the health state dimension, a historical evaluation result of the object to be managed in the life style dimension and portrait information of the object to be managed into a preset machine learning model, and obtaining the evaluation result difference value of the object to be managed in the life style dimension, which is output by the machine learning model; a fourth determining module, configured to determine, according to an evaluation result difference of the object to be managed in the lifestyle dimension, an adjustment amount of a target health indicator related to the lifestyle dimension in the health attribute; and the guidance module is used for guiding the health indexes of the object to be managed according to the adjustment quantity of the target health indexes related to the life style dimension in the health attributes.
As a possible implementation manner of the embodiment of the present disclosure, the data source information includes at least two of the following information: the management system comprises basic data of an object to be managed, environment data of the environment where the object to be managed is located, questionnaire data of the object to be managed and monitoring data of wearable equipment worn by the object to be managed.
The health management device of the embodiment of the disclosure acquires various data source information of an object to be managed; determining health attributes of the object to be managed according to the information of the various data sources, wherein the health attributes comprise a plurality of health indexes; aiming at each dimension to be evaluated of the object to be managed, determining an evaluation result of the object to be managed on the dimension to be evaluated according to a health index related to the dimension to be evaluated in the health attributes; according to the evaluation results of the objects to be managed on the dimensions to be evaluated, the objects to be managed are subjected to health management, the device can evaluate and analyze the plurality of dimensions to be evaluated of the objects to be managed according to the health indexes related to the dimensions to be evaluated in the health attributes, further the objects to be managed are subjected to health management according to the evaluation and analysis results of the plurality of dimensions to be evaluated, personalized and active health management of the objects to be managed can be achieved, and the health level of a user is effectively improved.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 11 shows a schematic block diagram of an example electronic device 1100 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 11, the electronic apparatus includes: one or more processors 1101, a memory 1102, and interfaces for connecting the various components, including a high speed interface and a low speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 11, a processor 1101 is taken as an example.
The memory 1102 is a non-transitory computer readable storage medium as provided herein. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform the health management methods provided herein. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform the health management method provided by the present application.
The memory 1102, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the health management methods in the embodiments of the present application (e.g., the obtaining module 1010, the first determining module 1020, the second determining module 1030, and the managing module 1040 shown in fig. 10). The processor 1101 executes various functional applications of the server and data processing by running non-transitory software programs, instructions and modules stored in the memory 1102, that is, implements the health management method in the above method embodiment.
The memory 1102 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the trained electronic device of the model, and the like. Further, the memory 1102 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 1102 optionally includes memory located remotely from processor 1101, and these remote memories may be connected to the trained electronics of the model over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the training method of the model may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103 and the output device 1104 may be connected by a bus or other means, and are exemplified by being connected by a bus in fig. 11.
The input device 1103 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the trained electronic device of the model, such as a touch screen, keypad, mouse, track pad, touch pad, pointer stick, one or more mouse buttons, track ball, joystick, or other input device. The output devices 1104 may include a display device, auxiliary lighting devices (e.g., LEDs), tactile feedback devices (e.g., vibrating motors), and the like. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be noted that artificial intelligence is a subject for studying a computer to simulate some human thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware and software technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, machine learning/deep learning, a big data processing technology, a knowledge map technology and the like.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims (15)

1. A health management method, comprising:
acquiring various data source information of an object to be managed;
determining health attributes of the object to be managed according to the various data source information, wherein the health attributes comprise a plurality of health indexes;
for each dimension to be evaluated of the object to be managed, determining an evaluation result of the object to be managed on the dimension to be evaluated according to a health index related to the dimension to be evaluated in the health attribute;
and performing health management on the object to be managed according to the evaluation result of the object to be managed on each dimension to be evaluated.
2. The method of claim 1, wherein the dimension to be evaluated comprises a health status dimension, wherein the health status dimension comprises a plurality of health status sub-dimensions;
the determining, for each dimension to be evaluated of the object to be managed, an evaluation result of the object to be managed on the dimension to be evaluated according to a health index related to the dimension to be evaluated in the health attribute includes:
for each health state sub-dimension in the health state dimensions, determining a sub-evaluation result of the object to be managed on the health state sub-dimension according to a plurality of target health indexes related to the health state sub-dimension in the health attributes;
and determining the evaluation result of the object to be managed on the health state dimension according to the sub-evaluation results of the object to be managed on the various health state sub-dimensions.
3. The method according to claim 2, wherein when the health status sub-dimension is a physiological health status sub-dimension, the determining a sub-evaluation result of the object to be managed on the health status sub-dimension according to a plurality of target health indicators related to the health status sub-dimension in the health attributes comprises:
determining a criticality of a plurality of target disease symptoms associated with the physiological health state sub-dimension and a correlation between each of the target health indicators and each of the target disease symptoms;
for each of the target health indicators, determining a weight for the target health indicator based on the criticality of the plurality of target disease symptoms and the degree of correlation between the target health indicator and each of the target disease symptoms;
and determining the sub-evaluation result of the object to be managed on the physiological health state sub-dimension according to the numerical value and the weight of each target health index.
4. The method of claim 1, wherein the dimension to be assessed comprises a lifestyle dimension, wherein the lifestyle dimension comprises a plurality of lifestyle sub-dimensions;
the determining, for each dimension to be evaluated of the object to be managed, an evaluation result of the object to be managed on the dimension to be evaluated according to a health index related to the dimension to be evaluated in the health attribute includes:
determining, for each lifestyle sub-dimension of the lifestyle dimension, a degree of risk of a plurality of target disease symptoms associated with the lifestyle sub-dimension and a degree of correlation between each of the target health indicators and each of the target disease symptoms;
for each of the target health indicators, determining a weight for the target health indicator based on the criticality of the plurality of target disease symptoms and the degree of correlation between the target health indicator and each of the target disease symptoms;
determining a sub-evaluation result of the object to be managed on the life style sub-dimension according to the numerical value and the weight of each target health index;
and determining the evaluation result of the object to be managed in the life style dimension according to the sub-evaluation result of the object to be managed in the plurality of life style sub-dimensions.
5. The method of claim 1, wherein the dimension to be evaluated comprises a health status dimension; the method further comprises the following steps:
determining a reference object data set, wherein the reference object data set comprises: portrait information of a reference object, and an evaluation result of the reference object in a health state dimension in an adjacent third time period;
predicting a target evaluation result of the object to be managed on the health state dimension according to the evaluation result and the similarity between the object to be managed and the reference object;
and recommending the target health state of the object to be managed according to the target evaluation result.
6. The method according to claim 5, wherein predicting the target evaluation result of the object to be managed in the health status dimension according to the evaluation result and the similarity between the object to be managed and the reference object comprises:
determining an evaluation result difference value of the reference object in the health state dimension in a third adjacent time period;
predicting the evaluation result difference of the object to be managed according to the evaluation result difference of the reference objects and the similarity between the reference objects and the object to be managed;
and predicting a target evaluation result of the object to be managed on the health state dimension according to a historical evaluation result of the object to be managed on the health state dimension and an evaluation result difference value of the object to be managed on the health state dimension.
7. The method of claim 5, wherein the dimension to be assessed further comprises a lifestyle dimension; the method further comprises the following steps:
inputting a historical evaluation result of the object to be managed on the health state dimension, an evaluation result difference of the object to be managed on the health state dimension, a historical evaluation result of the object to be managed on the life style dimension and portrait information of the object to be managed into a preset machine learning model, and acquiring an evaluation result difference of the object to be managed on the life style dimension, which is output by the machine learning model;
determining the adjustment amount of a target health index related to the life style dimension in the health attribute according to the evaluation result difference value of the object to be managed in the life style dimension;
and guiding the health indexes of the objects to be managed according to the adjustment quantity of the target health indexes related to the life style dimension in the health attributes.
8. A health management device, comprising:
the acquisition module is used for acquiring various data source information of the object to be managed;
the first determination module is used for determining the health attribute of the object to be managed according to the various data source information, wherein the health attribute comprises a plurality of health indexes;
a second determining module, configured to determine, for each dimension to be evaluated of the object to be managed, an evaluation result of the object to be managed in the dimension to be evaluated according to a health indicator, related to the dimension to be evaluated, in the health attribute;
and the management module is used for performing health management on the object to be managed according to the evaluation result of the object to be managed on each dimension to be evaluated.
9. The apparatus of claim 8, wherein the dimension to be evaluated comprises a health dimension, wherein the health dimension comprises a plurality of health sub-dimensions;
the second determining module is specifically configured to:
for each health state sub-dimension in the health state dimensions, determining a sub-evaluation result of the object to be managed on the health state sub-dimension according to a plurality of target health indexes related to the health state sub-dimension in the health attributes;
and determining the evaluation result of the object to be managed on the health state dimension according to the sub-evaluation results of the object to be managed on the various health state sub-dimensions.
10. The apparatus of claim 8, wherein the dimension to be evaluated comprises a health status dimension, the apparatus further comprising:
a third determining module, configured to determine a reference object data set, where the reference object data set includes: portrait information of a reference object, and an evaluation result of the reference object in a health state dimension in an adjacent third time period;
the prediction module is used for predicting a target evaluation result of the object to be managed on the health state dimension according to the evaluation result and the similarity between the object to be managed and the reference object;
and the recommending module is used for recommending the target health state of the object to be managed according to the target evaluation result.
11. The apparatus of claim 10, wherein the prediction module is specifically configured to:
determining an evaluation result difference value of the reference object in the health state dimension in a third adjacent time period;
predicting the evaluation result difference of the object to be managed according to the evaluation result difference of the reference objects and the similarity between the reference objects and the object to be managed;
and predicting a target evaluation result of the object to be managed in the health state dimension according to a historical evaluation result of the object to be managed in the health state dimension and an evaluation result difference value of the object to be managed in the health state dimension.
12. The apparatus of claim 10, wherein the dimension to be assessed further comprises a lifestyle dimension, the apparatus further comprising:
the input module is used for inputting a historical evaluation result of the object to be managed on the health state dimension, an evaluation result difference value of the object to be managed on the health state dimension, a historical evaluation result of the object to be managed on the life style dimension and portrait information of the object to be managed into a preset machine learning model, and obtaining the evaluation result difference value of the object to be managed on the life style dimension, which is output by the machine learning model;
a fourth determining module, configured to determine, according to an evaluation result difference of the object to be managed in the lifestyle dimension, an adjustment amount of a target health indicator related to the lifestyle dimension in the health attribute;
and the guidance module is used for guiding the health indexes of the object to be managed according to the adjustment quantity of the target health indexes related to the life style dimension in the health attributes.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.
15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-7.
CN202110723718.3A 2021-06-29 2021-06-29 Health management method and device, electronic equipment and storage medium Pending CN115547491A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117038074A (en) * 2023-08-01 2023-11-10 中国工业互联网研究院 User management method, device, equipment and storage medium based on big data

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117038074A (en) * 2023-08-01 2023-11-10 中国工业互联网研究院 User management method, device, equipment and storage medium based on big data

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