CN114334158A - Monitoring management method and system based on Internet of things - Google Patents
Monitoring management method and system based on Internet of things Download PDFInfo
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Abstract
The application discloses a monitoring management method and a monitoring management system based on the Internet of things, wherein the monitoring type of a user is determined based on user positioning information; according to the user monitoring category, extracting user analysis data from the user health database, and inputting the user analysis data into a matched double-hidden-layer model to obtain a user health analysis result; performing user health management analysis according to the user health analysis result and the health management guidance information to obtain user monitoring management information; monitoring the daily monitoring information of the user based on the user monitoring management information, and acquiring abnormal nursing information when the daily monitoring information of the user does not meet the user monitoring management information. The method solves the technical problem that single health intervention pertinence is not strong due to the fact that single parameters are analyzed when the health state of the old is monitored in the prior art. The corresponding monitoring management content is given according to the position and body indexes of the user, and the technical effects of strong pertinence and high reliability are achieved.
Description
Technical Field
The application relates to the technical field of data analysis, in particular to a monitoring management method and system based on the Internet of things.
Background
The social aging is serious, and along with the rapid increase of the number of the old people, the health service of the old people matched with the social aging also receives more and more attention. The prevention is greater than the treatment, supervision to the old person health status through daily, in order to avoid the gliding of health level, be the key of effectively carrying out old person health maintenance, just set forth the supervision to the health problem appearing, sometimes have nothing to do with help, therefore more and more old people become strong to daily health care and health supervision's consciousness, present common thing networking supervision platform, combine with the platform through thing networking hardware, though realized old man's sign supervision, only can single numerical value look over contrast and reference, unable fine formation health intervention, and the high guidance that needs of data specialty has, the practicality is not strong.
The above-mentioned techniques have been found to have at least the following technical problems:
when the health state of the old is monitored through Internet of things equipment in the prior art, a single parameter is analyzed, and the technical problems of single monitoring parameter and weak pertinence of health intervention guidance exist.
Disclosure of Invention
The application aims to provide a monitoring management method and a monitoring management system based on the Internet of things, and the monitoring management method and the monitoring management system are used for solving the technical problems that monitoring parameters are single and health intervention guidance is not strong in pertinence when health states of old people are monitored through Internet of things equipment in the prior art.
In view of the above problems, the present application provides a monitoring management method and system based on the internet of things.
In a first aspect, the present application provides a monitoring management method based on the internet of things, where the method includes: acquiring multi-parameter index information of a user through intelligent wearable equipment to obtain user health monitoring data, and storing the user health monitoring data in a health data pool to construct a user health database; obtaining user positioning information; determining a user monitoring category based on the user positioning information; according to the user monitoring category, extracting user analysis data from the user health database, and inputting the user analysis data into a matched double-hidden-layer model; obtaining an output result of the double hidden layer model, wherein the output result comprises a user health analysis result; performing user health management analysis according to the user health analysis result and the health management guidance information to obtain user monitoring management information; monitoring the daily monitoring information of the user based on the user monitoring management information, and acquiring abnormal nursing information when the daily monitoring information of the user does not meet the user monitoring management information.
In another aspect, the present application further provides an internet of things-based monitoring management system for implementing the internet of things-based monitoring management method according to the first aspect, the system includes:
the system comprises a first obtaining unit, a second obtaining unit and a health monitoring unit, wherein the first obtaining unit is used for collecting multi-parameter index information of a user through intelligent wearable equipment, obtaining user health monitoring data and storing the user health monitoring data in a health data pool to construct a user health database;
a second obtaining unit, configured to obtain user positioning information;
a first determination unit, configured to determine a user monitoring category based on the user positioning information;
the first execution unit is used for extracting user analysis data from the user health database according to the user monitoring category and inputting the user analysis data into a matched double-hidden-layer model;
a third obtaining unit, configured to obtain an output result of the double-hidden-layer model, where the output result includes a user health analysis result;
a fourth obtaining unit, configured to perform user health management analysis according to the user health analysis result and the health management guidance information, and obtain user monitoring management information;
a fifth obtaining unit, configured to monitor the user daily monitoring information based on the user monitoring management information, and obtain abnormal care information when the user daily monitoring information does not satisfy the user monitoring management information.
In a third aspect, the present application further provides an monitoring management system based on the internet of things, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to the first aspect when executing the program.
In a fourth aspect, the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of the first aspects.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the application discloses a monitoring management method and system based on the Internet of things, wherein multi-parameter index information of a user is acquired through intelligent wearable equipment, user health monitoring data are obtained, and the user health monitoring data are stored in a health data pool to construct a user health database; obtaining user positioning information; determining a user monitoring category based on the user positioning information; according to the user monitoring category, extracting user analysis data from the user health database, and inputting the user analysis data into a matched double-hidden-layer model; obtaining an output result of the double hidden layer model, wherein the output result comprises a user health analysis result; performing user health management analysis according to the user health analysis result and the health management guidance information to obtain user monitoring management information; monitoring the daily monitoring information of the user based on the user monitoring management information, and acquiring abnormal nursing information when the daily monitoring information of the user does not meet the user monitoring management information. The method has the advantages that the corresponding monitoring management content is given according to the position and the body index of the user, the state of the user is monitored in real time, the targeted health intervention can be rapidly and effectively carried out according to the state of the user, meanwhile, the artificial intelligence technology is added, the operation efficiency and the reliability of an analysis result are effectively improved, the technical effect of monitoring guidance with strong pertinence and high reliability is realized for the user, and therefore the technical problems that in the prior art, when the health state of the old is monitored through the Internet of things equipment, the single parameter is analyzed, the monitoring parameter is single, and the targeted guidance is not strong are solved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flowchart of a monitoring management method based on the internet of things according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a monitoring management system based on the internet of things according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a first determining unit 13, a first executing unit 14, a third obtaining unit 15, a fourth obtaining unit 16, a fifth obtaining unit 17, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 305.
Detailed Description
The embodiment of the application provides a monitoring management method and system based on the Internet of things, and solves the technical problems that monitoring parameters are single and health intervention guidance is not strong in pertinence when health states of old people are monitored through Internet of things equipment in the prior art and single parameters are analyzed.
In the following, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application. It should be further noted that, for the convenience of description, only some but not all of the elements relevant to the present application are shown in the drawings.
The technical scheme provided by the application has the following general idea:
acquiring multi-parameter index information of a user by using intelligent wearable equipment to obtain user health monitoring data, and storing the user health monitoring data in a health data pool to construct a user health database; analyzing the environment where the user is located based on the user positioning information, and determining the user monitoring category; according to the user monitoring category, extracting user analysis data from the user health database, and inputting the user analysis data into a matched double-hidden-layer model; calculating the monitoring data through a double hidden layer model to obtain a user health analysis result; carrying out user health management analysis by using the user health analysis result and the health management guidance information to obtain user monitoring management information; monitoring the daily monitoring information of the user based on the user monitoring management information, and acquiring abnormal nursing information when the daily monitoring information of the user does not meet the user monitoring management information. The method has the advantages that the corresponding monitoring management content is given according to the position and body index of the user, the state of the user is monitored in real time, the targeted health intervention can be rapidly and effectively carried out according to the state of the user, meanwhile, the artificial intelligence technology is added, the operation efficiency is effectively improved, and the technical effect of the monitoring guidance with strong pertinence and high reliability is provided for the user.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
Referring to fig. 1, an embodiment of the present application provides a monitoring management method based on the internet of things, where the method includes:
step S100: the method comprises the steps of collecting multi-parameter index information of a user through intelligent wearable equipment, obtaining user health monitoring data, and storing the user health monitoring data in a health data pool to build a user health database.
Specifically, the monitoring management method based on the Internet of things is applied to an Internet of things platform, the Internet of things platform is connected with Internet of things wearable equipment, communities, third-party nursing institutions and the like, the intelligent wearable equipment comprises monitoring equipment with various body health parameters, the intelligent wearable equipment is used for monitoring body indexes of users, the collected health monitoring data of the users are stored in a local health data pool of the Internet of things platform, each user corresponds to an ID or an account number of the user and uniquely corresponds to a health database according to the account of the user, the database establishes connection with a user health database for a key based on the identification ID, the account number and the like of the user, the collected data are synchronously stored and updated in the user health database, and in the subsequent monitoring, the respective user health database is also updated based on the information of the user, and continuous storage and perfection of data are completed.
Step S200: user positioning information is obtained.
Specifically, carry out position location to the user through monitoring facilities, monitoring facilities can be intelligent terminal or have locate function's intelligent wearing equipment. And carrying out accurate positioning and analysis on the specific position and environment when the user monitors the operation process.
Step S300: and determining a user monitoring category based on the user positioning information.
Specifically, which monitoring management service the user currently needs to perform is determined according to the specific environment in which the user location information is located. For example, when the current user is at rest in a room, the user monitoring category is the monitoring of the general physical indicators, and the monitoring of the corresponding indicators and data can be performed according to the physical state of the static user. When the user moves in the community at present, monitoring is carried out according to the body index state of the user when the user moves, and different positioning determines the body environment and state of the user, so that the monitoring category of the user is determined, wherein the monitoring category comprises basic index monitoring, motion amount statistics, motion index detection, diet guidance, motion guidance, sleep guidance, motion guidance and the like.
Step S400: and extracting user analysis data from the user health database according to the user monitoring category, and inputting the user analysis data into the matched double-hidden-layer model.
Specifically, according to the user monitoring category which is currently required to be executed by the user, data extraction is carried out from a health database corresponding to the user based on data characteristics and data requirements required by the user monitoring category, user analysis data is a data set meeting the requirement of the current monitoring category, different monitoring category requirements correspond to a trained double-hidden-layer model, the corresponding operation processing double-hidden-layer model is matched according to the current user monitoring category, the user analysis data is input into a calculation model matched with the corresponding data type, operation processing is given based on the user monitoring data, and an operation result is output through the double-hidden-layer model. The double-hidden-layer model is a neural network model with two hidden layers, comprises an input layer, a first hidden layer, a second hidden layer and an output layer, has two hidden layers compared with other neural network double-hidden-layer models, is operated through the second hidden layer after being operated by the first hidden layer, and has the effects of small error and accurate data analysis and prediction. The method comprises the steps of determining historical training data by using corresponding data analysis requirements, carrying out training learning convergence on a model through the training data, carrying out supervised learning by using labels in the historical training data, determining an operational relation between data parameters, and continuously perfecting and optimizing the model by using gradient descent and a loss function, so that a calculation double-hidden-layer model corresponding to a monitoring category is obtained, input can be carried out according to the parameter requirements of the category, an identification result corresponding to the parameter requirements is output, the effect of intelligent analysis processing is realized, and the operational efficiency is improved. For example, when a current user is at rest, according to the requirement of monitoring the heartbeat, blood pressure, body temperature, skin humidity and the like of the user during rest, the index parameter monitored at the current time or the parameter related to the index is extracted from the health database corresponding to the user and matched with the double hidden layer model of the corresponding analysis category, and the extracted user data meeting the requirement, time and data characteristics is input into the corresponding matched double hidden layer model for operation, so that the body monitoring analysis result of the current user in the sleep state is obtained.
Step S500: and obtaining an output result of the double hidden layer model, wherein the output result comprises a user health analysis result.
Step S600: and performing user health management analysis according to the user health analysis result and the health management guidance information to obtain user monitoring management information.
Specifically, the health management of the user is performed on the user health analysis result and the health management guidance information output by the double hidden layer model, the health management guidance information can be a data set which is acquired in advance and constructed by expert data, or data can be provided by a third-party nursing institution, the corresponding monitoring management guidance such as specific exercise guidance, diet guidance, work and rest guidance, mood state dispersion and the like is performed according to the user health analysis result and the health management guidance information aiming at the current state of the user, and the supervision report is fed back to the user according to the supervision report corresponding to the user supervision management information, the user can check the supervision report, know the self state in real time, and the supervision report utilizes character analysis records and can be played through language, so that the old people can conveniently know the supervision report and adapt to different client groups, meanwhile, the form of the text report or the language report is more popular and easy to understand, and the method is different from the method that professional monitoring data or trend chart needs guidance and explanation of professional staff. The user monitoring management information provides corresponding specific monitoring requirements, including monitoring content, monitoring time, data range setting conditions and the like, for the current monitoring requirement category of the user.
Step S700: monitoring the daily monitoring information of the user based on the user monitoring management information, and acquiring abnormal nursing information when the daily monitoring information of the user does not meet the user monitoring management information.
Specifically, the user is monitored and acquired daily through intelligent wearable equipment, the acquired data are stored in a user health database, meanwhile, information acquired through daily monitoring is compared with a data requirement range in user monitoring management information, if the data indexes are abnormal, a prompt is timely sent out, active communication and health intervention information is sent to the user terminal through a third-party nursing mechanism or an internet of things platform, abnormal nursing information is nursing guidance suggestions conforming to the current user state according to the environment corresponding to the abnormal indexes, and abnormal nursing information can be fed back to a terminal held by the user through the internet of things platform. Different contacts can be contacted in combination with different data levels of indexes during feedback, for example, for a user with a light input level in a data range, the user can be contacted, or the user with the most frequent use of the platform, the emergency contact of the user can be contacted in the case of a large data range excess amount and heavy level, corresponding intervention information is given according to a comparison result required by daily monitoring information of the user and monitoring management information of the user, intervention measures are adjusted in the case of intervention deviation, and corresponding health intervention is given according to different analysis conditions. The monitoring management content corresponding to the position and the body index of the user is provided, the state of the user is monitored in real time, targeted health intervention can be rapidly and effectively carried out on the state of the user, different monitoring indexes are determined according to different environments, the flexibility is strong, the application range is wide, the artificial intelligence technology is added, the operation efficiency is effectively improved, the technical effect of targeted and high-reliability monitoring guidance is provided for the user, the problem that in the prior art, the health state of the old is monitored through the Internet of things equipment, single monitoring parameters exist, and the targeted technical problem of health intervention guidance is not strong is solved.
When the user positioning information is the first positioning type, obtaining historical work and rest monitoring information of a user according to the user positioning information; acquiring user task execution information according to the user positioning information and the user historical work and rest monitoring information; determining the user monitoring category based on the user execution task information.
Specifically, two types of division are performed on the environment where the user is located according to the user positioning information, wherein the first positioning type is located indoors such as at home, and the second positioning type is located outdoors such as in communities and gardens. The method comprises the steps of aiming at the situation that a first positioning type is at home, aiming at the activity characteristic distribution situation of user history at home and the structure determination of the home, analyzing work and rest characteristics of a user, determining historical work and rest monitoring information of the user, and determining the current specific position of the user based on the historical work and rest monitoring information of the user, such as the current position of the user, a bedroom, a dining room, a kitchen and the like. The users correspond to different work and rest contents for different environments and time nodes, and the users execute task information, namely events of user operation corresponding to the current environment and time nodes, such as user preparation for sleeping, user preparation for lunch and the like. When a user sleeps or moves, physical indexes and sign parameters with high risk probability which need to be noticed in the current state of the personal state of the user are supervised according to the state of the current user, meal analysis can be carried out according to the health state of the user when the user prepares to eat, and corresponding eating instructions such as eating requirements and consumption are given, so that analysis is carried out according to the positioning of the user and the characteristics of the user, the supervision type which needs to be carried out currently is determined, different monitoring parameters are determined according to different user environments, all-round targeted guidance is provided for the user, the user can determine the current specific conditions and carry out real-time specific guidance, boring physical examination reports do not need analysis of professional persons, and the user can start without help, and the user lacks guidance on health intervention means in actual life.
Further, the method further comprises: when the user positioning information is the second positioning type, acquiring a positioning location monitoring system; establishing system contact according to the positioning location monitoring system; based on the system contact, obtaining a user health data pool of the location monitoring system; performing federal learning according to the double hidden layer model and a user health data pool of the positioning location monitoring system to obtain a federal health management model; acquiring and obtaining user location monitoring data through monitoring equipment of the location monitoring system; and inputting the monitoring data of the user location into the federal health management model to obtain a user monitoring analysis result.
Specifically, when user positioning information is displayed outdoors, such as a residential garden, a fitness equipment and the like, a monitoring system used by the location of a user is determined by utilizing the current positioning information, and the synchronization of user data is realized by establishing connection between an Internet of things platform and the monitoring system used by the location of the user, the application is suitable for the outdoors covered by the monitoring system in a smart park and the like, the monitoring system is a monitoring system covered in the current positioning of the user, the monitoring system is provided with a self model and a user health data pool, outdoor health monitoring is carried out on the user, federal learning is carried out by utilizing the user health data pool of the location monitoring system and a double hidden layer model and a health data pool used by a local Internet of things platform, a federal health management model is constructed to realize the health monitoring of the outdoor user, and meanwhile, the federal health management model is built on the basis of the double hidden layer model, the operation processing effects of different indoor and outdoor parameters are realized. The method comprises the steps of utilizing indoor and outdoor monitoring data to carry out comprehensive analysis on the body health state of a user, carrying out targeted analysis on the user monitoring analysis result aiming at the outdoor by utilizing the user monitoring analysis result, utilizing the outdoor monitoring data and based on the indoor health monitoring result, and giving reminding and health intervention to abnormal conditions of the user.
Further, the obtaining of the federal health management model by federal learning according to the double hidden layer model and the user health data pool of the monitoring system where the location is located includes: obtaining a cooperation intermediate platform; performing model training through a user health data pool of the location monitoring system to obtain a joint training model; respectively carrying out encryption gradient uploading on the double hidden layer model and the joint training model to the cooperation intermediate platform; updating model parameters through the cooperation intermediate platform polymerization gradient to obtain a federal model; and updating the double hidden layer model according to the federal model to obtain the federal health management model.
Particularly, federal learning is a machine learning framework, which can effectively help a plurality of organizations to perform data use and machine learning modeling under the condition of meeting the requirements of user privacy protection, data safety and government regulations. The cooperation intermediate platform is an intermediate platform with trust degree in the middle, the data of the platform and the data of other systems are uploaded to the cooperation intermediate platform through encryption, the uploaded data are encrypted and trained, so that the data with the same attribute are jointly used as model training samples, training data are enriched, and the reliability of model training results is improved, each platform or system participating in the training and learning process utilizes local data to train respective models, the embodiment of the application utilizes a local health data pool to train a double-hidden layer model, the trained double-hidden layer model encryption gradient is uploaded to the cooperation intermediate platform, the monitoring systems use the user health data pool of the monitoring systems in the locations to train the models to generate a joint training model of the systems in the locations, and the cooperation intermediate platform performs aggregate training on the models by all the received data, the method comprises the steps of aggregating gradient update model parameters of users of all parties, returning an updated model, namely a federal model, to a local Internet of things platform, updating a local double hidden layer model by using the updated model parameters to obtain a federal health management model, wherein the federal health management model is subjected to aggregation training by using all related data in a cooperation platform, so that the method has better accuracy, simultaneously considers the model characteristics of a monitoring system at the place, can more accurately combine and manage the health data of the users, ensures the monitoring requirements of the local Internet of things platform and the monitoring requirements of the monitoring system at the place where the users are located outdoors, and accurately analyzes and manages the states of the users.
Further, the method further comprises: judging whether the monitoring data of the location of the user meets the user monitoring management information or not, and obtaining a first result; judging whether the user monitoring analysis result meets the user monitoring management information or not, and obtaining a second result; when the first result and/or the second result meet/meets a preset requirement, first reminding information is sent; and based on the first reminding information, determining health intervention information according to the user monitoring management information, and feeding back the health intervention information to the user.
Specifically, when monitoring the health state of a user is carried out outdoors, when monitored real-time data does not meet the index requirements in user monitoring management information, a prompt is given in time, or when the user monitoring analysis result given by a federal health management model does not meet the requirements, the prompt is given in the same way, different stages of analysis and prompt are carried out according to different indexes, meanwhile, health intervention is given according to the current analysis result, if the heart rate of the user is monitored too fast, a prompt exceeding the monitoring range of the current user is given, adverse effects are generated on the body state, the amount of exercise needs to be reduced, or the user needs to slowly sit down for rest, and the like, and guidance suggestions are given. If the comprehensive analysis is carried out on the indoor and outdoor index change conditions and the comprehensive result exceeds the requirement in the user monitoring management information, the health intervention corresponding to the comprehensive index is given, such as the proportion requirement of setting the outdoor time length and the indoor time length, the outdoor and indoor clothing increment and decrement and the like. If the first result and/or the second result meet the preset requirement, the abnormality is caused if the abnormality exceeds the requirement in the user monitoring management information, and health intervention is required.
Further, the method further comprises: associating the abnormal nursing information with corresponding daily monitoring information of the user, and storing the abnormal nursing information in an abnormal nursing library; acquiring preset periodic analysis information; according to a set requirement in the preset periodic analysis information, regularly performing health analysis on data in the abnormal nursing database to obtain user health fluctuation information; and when the user health fluctuation information exceeds a preset requirement, acquiring monitoring management adjustment information according to the user health fluctuation information and the user monitoring management information.
Specifically, when the daily monitoring information of the user is abnormal, the abnormal detection information and the abnormal nursing information are stored in the abnormal nursing base of the user, the abnormal data are stored as long as the abnormal data are abnormal, the abnormal nursing base of the user is constructed, the data in the abnormal nursing base of the user can be periodically analyzed, the preset periodic analysis information is determined according to the data frequency and the data fluctuation size in the abnormal nursing base of the user, and can be set by the user, for example, when the abnormal nursing information of the user occurs frequently, such as 2 times in a week, the preset periodic information can be set to be one time in a week or once in two days, the periodic information can be set to be 2 days, and the like, the periodic time is set according to the abnormal frequency of different users, and the periodic rule is set for abnormal analysis and determination, or starting the abnormal analysis when the amplitude of the abnormal index reaches a large or small value, judging whether the nursing guidance deviation exists or not by aiming at the data analysis in the abnormal nursing library of the user, or the body state is abnormal, the health of the user is guided according to the health fluctuation information of the user, the currently set user nursing management information is adjusted according to the fluctuation range of the user health, for example, the adjustment of the user monitoring management information is performed according to the current health index when the user health fluctuation information reaches the oblique angle fluctuation rate, if the detection range of the parameter is adjusted to be small, the number of detection reminding times is increased, close attention to the user is realized, the user can be adjusted to be larger when abnormal fluctuation of the user is slowed down, the method is suitable for the current state of the user, specific guidance is conducted according to the specific situation of the user, and reliable health intervention management is provided for the user.
Further, the monitoring device of the location monitoring system comprises an image monitoring device and an internet of things sign monitoring device, and the method comprises the following steps: acquiring user image monitoring information according to the user positioning information through the image monitoring equipment; analyzing the body state of the user image monitoring information to obtain user activity index information; acquiring user sign monitoring information through Internet of things sign monitoring equipment, wherein the user sign monitoring information comprises monitoring time; based on the monitoring time, performing time mapping according to the user sign monitoring information and the user image monitoring information to obtain user activity sign information; and acquiring the user location monitoring data based on the user activity index information and the user activity sign information, and synchronously storing the user location monitoring data in the user health database and a user health data pool of the positioning location monitoring system.
Specifically, the monitoring device of the outdoor location detection system can comprise an image monitoring device and an internet of things sign monitoring device, the current action, track, movement time and the like of a user are monitored and analyzed through the image monitoring device, the sign data of the user are collected and monitored through the internet of things sign monitoring device, the user image monitoring information and the user sign monitoring information jointly construct outdoor health data of the user, when the user exercises, the user can utilize the image monitoring information of the user to carry out characteristic analysis and time interception, the amount of exercise, the movement time, the movement amplitude and the movement type of the current user are analyzed, the data are user activity index information, the user sign monitoring data are evaluated by combining the user activity index information, and when the amount of exercise is large, the user sign data are different from those in a small exercise state or a stable state, determining the current state of the user by combining the image of the user, analyzing and processing by utilizing the monitored physical sign information of the user, determining whether the monitoring index of the current user is normal, giving a prompt when the monitoring index exceeds the index range in the state, and correspondingly generating health intervention information to provide health intervention guidance for the user. Meanwhile, the collected user location monitoring data, namely user image monitoring information and user sign monitoring information, are synchronously stored in a user health database of the Internet of things platform and a user health data pool of the location monitoring system, so that subsequent analysis and use are facilitated, a perfect user monitoring database is established, and reliable monitoring management is guaranteed for users.
Example two
Based on the monitoring management method based on the internet of things in the foregoing embodiments, the same inventive concept is also provided in the present invention, referring to fig. 2, the monitoring management system based on the internet of things includes:
the first obtaining unit 11 is used for collecting multi-parameter index information of a user through intelligent wearable equipment, obtaining user health monitoring data, and storing the user health monitoring data in a health data pool to construct a user health database;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain user location information;
a first determining unit 13, wherein the first determining unit 13 is configured to determine a user monitoring category based on the user positioning information;
a first execution unit 14, where the first execution unit 14 is configured to extract user analysis data from the user health database according to the user monitoring category, and input the user analysis data into a matched double-hidden-layer model;
a third obtaining unit 15, where the third obtaining unit 15 is configured to obtain an output result of the double hidden layer model, where the output result includes a user health analysis result;
a fourth obtaining unit 16, where the fourth obtaining unit 16 is configured to perform user health management analysis according to the user health analysis result and the health management guidance information, so as to obtain user monitoring management information;
a fifth obtaining unit 17, where the fifth obtaining unit 17 is configured to monitor the user daily monitoring information based on the user monitoring management information, and obtain abnormal nursing information when the user daily monitoring information does not satisfy the user monitoring management information.
Further, the system further comprises:
a sixth obtaining unit, configured to, when the user positioning information is the first positioning type, obtain historical work and rest monitoring information of a user according to the user positioning information;
a seventh obtaining unit, configured to obtain task execution information of the user according to the user positioning information and the historical work and rest monitoring information of the user;
a second determination unit to determine the user monitoring category based on the user execution task information.
Further, the system further comprises:
an eighth obtaining unit, configured to obtain a location monitoring system when the user positioning information is the second positioning type;
the second execution unit is used for establishing system contact according to the positioning location monitoring system;
a ninth obtaining unit, configured to obtain a user health data pool of the location monitoring system based on the system contact;
a tenth obtaining unit, configured to perform federal learning according to the double hidden layer model and the user health data pool of the location monitoring system, and obtain a federal health management model;
an eleventh obtaining unit, configured to acquire and obtain user location monitoring data through the monitoring device of the location monitoring system;
and the twelfth obtaining unit is used for inputting the monitoring data of the user location into the federal health management model to obtain a user monitoring analysis result.
Further, the system further comprises:
a thirteenth obtaining unit for obtaining a collaboration intermediary platform;
a fourteenth obtaining unit, configured to perform model training through a user health data pool of the location monitoring system to obtain a joint training model;
a first uploading unit, configured to upload the double hidden layer model and the joint training model to the cooperation intermediate platform in an encryption gradient respectively;
a fifteenth obtaining unit, configured to update model parameters through the cooperation intermediate platform aggregation gradient to obtain a federated model;
and the sixteenth obtaining unit is used for updating the double hidden layer model according to the federal model to obtain the federal health management model.
Further, the system further comprises:
a seventeenth obtaining unit, configured to determine whether the monitoring data of the location of the user meets the user monitoring management information, and obtain a first result;
an eighteenth obtaining unit, configured to determine whether the user monitoring analysis result meets the user monitoring management information, and obtain a second result;
the first sending unit is used for sending first reminding information when the first result and/or the second result meet preset requirements;
and the third execution unit is used for determining health intervention information according to the user monitoring management information based on the first reminding information and feeding back the health intervention information to the user.
Further, the system further comprises:
the first storage unit is used for associating the abnormal nursing information with corresponding daily monitoring information of the user and storing the abnormal nursing information in an abnormal nursing library;
a nineteenth obtaining unit configured to obtain preset periodic analysis information;
a twentieth obtaining unit, configured to periodically perform health analysis on the data in the abnormal care repository according to a setting requirement in the preset periodic analysis information, so as to obtain health fluctuation information of the user;
a twenty-first obtaining unit, configured to obtain monitoring management adjustment information according to the user health fluctuation information and the user monitoring management information when the user health fluctuation information exceeds a preset requirement.
Further, the system further comprises:
a twenty-second obtaining unit, configured to obtain, by the image monitoring device, user image monitoring information according to the user positioning information;
a twenty-third obtaining unit, configured to perform a posture analysis on the user image monitoring information to obtain user activity index information;
a twenty-fourth obtaining unit, configured to obtain user sign monitoring information through internet of things sign monitoring equipment, where the user sign monitoring information includes monitoring time;
a twenty-fifth obtaining unit, configured to perform time mapping according to the user sign monitoring information and the user image monitoring information based on the monitoring time, and obtain user activity sign information;
and the fourth execution unit is used for acquiring the monitoring data of the location of the user based on the index information of the user activity and the information of the user activity sign, and synchronously storing the monitoring data of the location of the user in the user health database and a user health data pool of the monitoring system of the location of the user.
In the present description, each embodiment is described in a progressive manner, and the emphasis of each embodiment is to expect the difference of the other embodiments, and the foregoing monitoring management method based on the internet of things in the first embodiment of fig. 1 and the specific example are also applicable to the monitoring management system based on the internet of things in the present embodiment, and through the foregoing detailed description of the monitoring management method based on the internet of things, those skilled in the art can clearly know the monitoring management system based on the internet of things in the present embodiment, so for the brevity of the description, detailed description is not repeated here. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the monitoring management method based on the internet of things in the foregoing embodiments, the present invention further provides a monitoring management system based on the internet of things, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any one of the foregoing monitoring management methods based on the internet of things are implemented.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, the present application may take the form of an entirely software embodiment, an entirely hardware embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application is in the form of a computer program product that may be embodied on one or more computer-usable storage media having computer-usable program code embodied therewith. And such computer-usable storage media include, but are not limited to: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk Memory, a Compact Disc Read-Only Memory (CD-ROM), and an optical Memory.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the same technology as the present invention, it is intended that the present invention encompass such modifications and variations as well.
Claims (10)
1. A monitoring management method based on the Internet of things is characterized by comprising the following steps:
acquiring multi-parameter index information of a user through intelligent wearable equipment to obtain user health monitoring data, and storing the user health monitoring data in a health data pool to construct a user health database;
obtaining user positioning information;
determining a user monitoring category based on the user positioning information;
according to the user monitoring category, extracting user analysis data from the user health database, and inputting the user analysis data into a matched double-hidden-layer model;
obtaining an output result of the double hidden layer model, wherein the output result comprises a user health analysis result;
performing user health management analysis according to the user health analysis result and the health management guidance information to obtain user monitoring management information;
monitoring the daily monitoring information of the user based on the user monitoring management information, and acquiring abnormal nursing information when the daily monitoring information of the user does not meet the user monitoring management information.
2. The method of claim 1, wherein the user location information comprises a first location type, a second location type, the method comprising:
when the user positioning information is the first positioning type, obtaining user historical work and rest monitoring information according to the user positioning information;
acquiring user task execution information according to the user positioning information and the user historical work and rest monitoring information;
determining the user monitoring category based on the user execution task information.
3. The method of claim 2, wherein the method further comprises:
when the user positioning information is the second positioning type, acquiring a positioning location monitoring system;
establishing system contact according to the positioning location monitoring system;
based on the system contact, obtaining a user health data pool of the location monitoring system;
performing federal learning according to the double hidden layer model and a user health data pool of the positioning location monitoring system to obtain a federal health management model;
acquiring and obtaining user location monitoring data through monitoring equipment of the location monitoring system;
and inputting the monitoring data of the user location into the federal health management model to obtain a user monitoring analysis result.
4. The method of claim 3, wherein the obtaining a federated health management model by federated learning from the dual-hidden-layer model and a user health data pool of the location based monitoring system comprises:
obtaining a cooperation intermediate platform;
performing model training through a user health data pool of the location monitoring system to obtain a joint training model;
respectively carrying out encryption gradient uploading on the double hidden layer model and the joint training model to the cooperation intermediate platform;
updating model parameters through the cooperation intermediate platform polymerization gradient to obtain a federal model;
and updating the double hidden layer model according to the federal model to obtain the federal health management model.
5. The method of claim 3, wherein the method further comprises:
judging whether the monitoring data of the location of the user meets the user monitoring management information or not, and obtaining a first result;
judging whether the user monitoring analysis result meets the user monitoring management information or not, and obtaining a second result;
when the first result and/or the second result meet/meets a preset requirement, first reminding information is sent;
and based on the first reminding information, determining health intervention information according to the user monitoring management information, and feeding back the health intervention information to the user.
6. The method of claim 1, wherein the method further comprises:
associating the abnormal nursing information with corresponding daily monitoring information of the user, and storing the abnormal nursing information in an abnormal nursing library;
acquiring preset periodic analysis information;
according to a set requirement in the preset periodic analysis information, regularly performing health analysis on data in the abnormal nursing database to obtain user health fluctuation information;
and when the user health fluctuation information exceeds a preset requirement, acquiring monitoring management adjustment information according to the user health fluctuation information and the user monitoring management information.
7. The method of claim 3, wherein the monitoring devices of the location monitoring system comprise an image monitoring device and an Internet of things sign monitoring device, and the method comprises:
acquiring user image monitoring information according to the user positioning information through the image monitoring equipment;
analyzing the body state of the user image monitoring information to obtain user activity index information;
acquiring user sign monitoring information through Internet of things sign monitoring equipment, wherein the user sign monitoring information comprises monitoring time;
based on the monitoring time, performing time mapping according to the user sign monitoring information and the user image monitoring information to obtain user activity sign information;
and acquiring the user location monitoring data based on the user activity index information and the user activity sign information, and synchronously storing the user location monitoring data in the user health database and a user health data pool of the positioning location monitoring system.
8. An internet of things-based monitoring management system, the system comprising:
the system comprises a first obtaining unit, a second obtaining unit and a health monitoring unit, wherein the first obtaining unit is used for collecting multi-parameter index information of a user through intelligent wearable equipment, obtaining user health monitoring data and storing the user health monitoring data in a health data pool to construct a user health database;
a second obtaining unit, configured to obtain user positioning information;
a first determination unit, configured to determine a user monitoring category based on the user positioning information;
the first execution unit is used for extracting user analysis data from the user health database according to the user monitoring category and inputting the user analysis data into a matched double-hidden-layer model;
a third obtaining unit, configured to obtain an output result of the double-hidden-layer model, where the output result includes a user health analysis result;
a fourth obtaining unit, configured to perform user health management analysis according to the user health analysis result and the health management guidance information, and obtain user monitoring management information;
a fifth obtaining unit, configured to monitor the user daily monitoring information based on the user monitoring management information, and obtain abnormal care information when the user daily monitoring information does not satisfy the user monitoring management information.
9. An internet of things based monitoring management system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the method of any one of claims 1-7.
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