CN112235740A - Individual work and rest monitoring method and system based on Internet of things - Google Patents
Individual work and rest monitoring method and system based on Internet of things Download PDFInfo
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Abstract
The invention discloses an individual work and rest monitoring method and system based on the Internet of things, which comprises the steps of firstly, collecting at least one data for representing individual work and rest characteristics by a sensor; wherein, different sensors correspondingly acquire data used for representing work and rest characteristics of different individuals; then, sending the collected at least one data used for representing the individual work and rest characteristics to a cloud server through a wireless transmission module; then, the cloud server is used for receiving and storing the at least one data used for representing the work and rest characteristics of the individual; and finally, the cloud server performs machine learning according to the stored at least one data for representing the individual work and rest characteristics, and establishes a model matched with the individual work and rest characteristics.
Description
Technical Field
The invention relates to the field of Internet of things and artificial intelligence, in particular to an individual work and rest monitoring method and system based on the Internet of things.
Background
Currently, the application of the internet of things relates to various industrial chain fields promoted by sensor technology, including intelligent communities, intelligent homes, intelligent medical care and the like. In the field of smart homes such as institutions like nursing homes and residential institutions, an individual work and rest monitoring system realized through the internet of things technology is widely applied.
In the related technology, the individual work and rest monitoring system mainly comprises a mattress (with a built-in pressure sensor), an emergency call button, a Wi-Fi module and a background software system. The system can judge whether an individual is in a bed or not through the pressure sensor and transmit data collected by the pressure sensor to background software through the Wi-Fi module so that a background can monitor work and rest of the individual based on the data collected by the pressure sensor. In addition, when someone presses the emergency call button, the background can receive an alarm prompt in real time.
However, the above-mentioned individual work and rest monitoring system has the following disadvantages: 1) many elderly people do not have Wi-Fi networks in their homes; 2) the machine learning algorithm is not available, so that the machine learning algorithm cannot learn and automatically judge whether the daily life of the old people is abnormal or not, and second-level early warning is realized; 3) the background needs to be monitored by people in real time, so that the maintenance cost is high; 4) the bed can only monitor the state of the old in the sleeping time period, and the time except sleeping cannot be monitored.
Disclosure of Invention
In order to solve the problems of the existing individual work and rest monitoring system, the embodiment of the invention creatively provides an individual work and rest monitoring method and system based on the Internet of things.
According to a first aspect of the embodiments of the present invention, there is provided an individual work and rest monitoring method based on the internet of things, the method including: collecting at least one datum for characterizing the work and rest characteristics of an individual by using a sensor; wherein, different sensors correspondingly acquire data used for representing work and rest characteristics of different individuals; the collected data used for representing the individual work and rest characteristics are sent to a cloud server through a wireless transmission module; receiving and storing the at least one data for representing the work and rest characteristics of the individual by using a cloud server; and performing machine learning by the cloud server according to the stored at least one data for representing the individual work and rest characteristics, and establishing a model matched with the individual work and rest characteristics.
According to an embodiment of the invention, the method further comprises: the cloud server performs anomaly analysis on the at least one data used for representing the individual work and rest characteristics according to the established model matched with the individual work and rest characteristics to obtain an analysis result; and if the analysis result shows that the data for representing the individual work and rest characteristics are abnormal, an alarm is sent out through the cloud server.
According to an embodiment of the invention, the sensor comprises at least one of the following types: pressure sensor, infrared sensor.
According to an embodiment of the invention, the sensor is a pressure sensor; the method for acquiring at least one data for characterizing the work and rest characteristics of an individual by using a sensor comprises the following steps: acquiring data characterizing whether an individual is in bed and/or in bed time using a pressure sensor; performing machine learning by the cloud server according to the stored at least one data for characterizing the individual work and rest characteristics, and establishing a model matched with the individual work and rest characteristics, including: and performing machine learning by the cloud server according to the stored data for representing whether the individual is in the bed and/or in the bed time, and establishing a sleep model matched with whether the individual is in the bed and/or in the bed time.
According to an embodiment of the present invention, the sensor is an infrared sensor; the method for acquiring at least one data for characterizing the work and rest characteristics of an individual by using a sensor comprises the following steps: acquiring data for representing the number of times of the individual entering and exiting the doorway and/or the interval duration by adopting an infrared sensor; performing machine learning by the cloud server according to the stored at least one data for characterizing the individual work and rest characteristics, and establishing a model matched with the individual work and rest characteristics, including: and the cloud server performs machine learning according to the stored data for representing the number of times of the individual entering and exiting the doorway and/or the interval duration, and establishes a behavior model matched with the number of times of the individual entering and exiting the doorway and/or the interval duration.
According to a second aspect of the embodiments of the present invention, there is also provided an individual work and rest monitoring system based on the internet of things, the system including: a sensor for acquiring at least one datum characterizing a work and rest of an individual; wherein, different sensors collect data for characterizing the work and rest characteristics of different individuals; the wireless transmission module is used for transmitting the collected at least one data used for representing the work and rest characteristics of the individual to the cloud server; the cloud server is used for receiving and storing the at least one data used for representing the work and rest characteristics of the individual; and the system is also used for performing machine learning according to the stored at least one data for characterizing the individual work and rest characteristics and establishing a model matched with the individual work and rest characteristics.
According to an embodiment of the present invention, the cloud server is further configured to perform anomaly analysis on the at least one data for characterizing the individual features according to the established model matched with the individual work and rest features to obtain an analysis result; and if the analysis result indicates that the data for characterizing the work and rest characteristics of the individual is abnormal, an alarm is given.
According to an embodiment of the invention, the sensor comprises at least one of the following types: pressure sensor, infrared sensor.
According to an embodiment of the invention, the sensor is a pressure sensor; the pressure sensor is further configured to collect data indicative of whether the individual is in bed and/or at bed time; the cloud server is further used for conducting machine learning according to the stored data for representing whether the individual is in the bed and/or in the bed time, and establishing a sleep model matched with whether the individual is in the bed and/or in the bed time.
According to an embodiment of the present invention, the sensor is an infrared sensor; the infrared sensor is also used for acquiring data for representing the number of times of the individual entering and exiting the doorway and/or the interval duration; the cloud server is further used for conducting machine learning according to the stored data used for representing the number of times of the individual entering and exiting the doorway and/or the interval duration, and establishing a behavior model matched with the number of times of the individual entering and exiting the doorway and/or the interval duration.
According to the individual work and rest monitoring method and system based on the Internet of things, firstly, a sensor is adopted to collect at least one piece of data for representing individual work and rest characteristics; wherein, different sensors correspondingly acquire data used for representing work and rest characteristics of different individuals; then, sending the collected at least one data used for representing the individual work and rest characteristics to a cloud server through a wireless transmission module; then, the cloud server is used for receiving and storing the at least one data used for representing the work and rest characteristics of the individual; and finally, the cloud server performs machine learning according to the stored at least one data for representing the individual work and rest characteristics, and establishes a model matched with the individual work and rest characteristics. Therefore, the indoor basic settings are interconnected through the sensors, the wireless transmission module and the cloud server, and the data which are collected by the sensors and used for representing individual work and rest characteristics are subjected to machine learning through a machine learning algorithm, so that the individual modeling is carried out on the daily life rule or behavior pattern of each user, the background does not need manual monitoring, and the maintenance cost is reduced; meanwhile, the background can automatically identify the abnormality based on the established model, and second-level early warning is achieved.
It is to be understood that the teachings of the present invention need not achieve all of the above-described benefits, but rather that specific embodiments may achieve specific technical results, and that other embodiments of the present invention may achieve benefits not mentioned above.
Drawings
The above and other objects, features and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
in the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Fig. 1 is a schematic flow chart illustrating an implementation of an individual work and rest monitoring method based on the internet of things according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an entity scene of an Internet of things-based individual work and rest monitoring system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram illustrating a composition of an individual work and rest monitoring system based on the internet of things according to an embodiment of the present invention.
Detailed Description
The principles and spirit of the present invention will be described with reference to a number of exemplary embodiments. It is understood that these embodiments are given only to enable those skilled in the art to better understand and to implement the present invention, and do not limit the scope of the present invention in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The technical solution of the present invention is further elaborated below with reference to the drawings and the specific embodiments.
Fig. 1 is a schematic flow chart illustrating an implementation of an individual work and rest monitoring method based on the internet of things according to an embodiment of the present invention; fig. 2 is a schematic view of an entity scene of an internet-of-things-based individual work and rest monitoring system according to an embodiment of the present invention.
Referring to fig. 1, an individual work and rest monitoring method based on the internet of things according to an embodiment of the present invention includes: operation 101, collecting at least one data for characterizing the work and rest characteristics of an individual by using a sensor; wherein, different sensors correspondingly acquire data used for representing work and rest characteristics of different individuals; operation 102, sending, by the wireless transmission module, the acquired at least one data for characterizing the work and rest characteristics of the individual to the cloud server; operation 103, receiving and storing the at least one data for characterizing the work and rest characteristics of the individual by using the cloud server; and operation 104, performing machine learning by the cloud server according to the stored at least one data for characterizing the individual work and rest characteristics, and establishing a model matched with the individual work and rest characteristics.
Wherein the sensor comprises at least one of the following types: pressure sensor, infrared sensor. Referring to fig. 2, in actual use, the pressure sensor may be deployed on a user's bed. Thus, when the pressure sensor senses that an object exceeding a certain weight is in the bed, it indicates that the user is in the bed. When the pressure disappears, it indicates that the user is out of bed. The infrared sensor can be arranged at the doorway (such as a toilet) which is frequently passed by a user in daily life, and is limited in height. Thus, when a person passes by, the infrared sensor counts one time and starts counting time.
At operation 102, at least one data characterizing the work and rest of the individual collected by the sensor may be transmitted to the cloud server by the wireless transmission module in real time. The wireless transmission technology adopted by the wireless transmission module can be GSM/WCDMA/LTE/NB-IoT and the like. It should be noted that the above wireless communication technologies have advantages and disadvantages, and the system can select an applicable technical means according to the actual situation of an individual. Therefore, in the state of the Internet of things, wide area network transmission can be achieved between the sensor and the cloud server instead of local area networks such as Wi-Fi in the prior art, and therefore the indoor infrastructure of the user is not required.
In accordance with an embodiment of the present invention, when the sensor is a pressure sensor, operation 101 may employ the pressure sensor to collect data for characterizing whether the individual is in bed and/or in bed time; accordingly, at operation 104, a sleep model matching the presence and/or time of the individual may be established by the cloud server based on the stored data characterizing the presence and/or time of the individual for machine learning.
Specifically, referring to fig. 2, data for characterizing whether an individual is in bed and/or in bed time is collected by a pressure sensor and transmitted to a cloud server; further, the cloud server counts the sleeping habits of the individual according to the collected data for representing whether the individual is in bed and/or in bed time, and establishes the sleeping model of the individual through a machine learning algorithm. Therefore, if the user sleeps for a long time or does not sleep for a long time, the user indicates that the individual is abnormal, and a corresponding alarm needs to be sent out. If the individual's sleeping habits change, it can be presumed whether the individual is physiologically or psychologically abnormal.
According to an embodiment of the present invention, when the sensor is an infrared sensor, the operation 101 may employ the infrared sensor to acquire data representing the number of times of the individual entering or exiting the doorway and/or the interval duration; accordingly, in operation 104, machine learning may be performed by the cloud server according to the stored data representing the number of times of entrance and exit and/or the interval duration of the individual, and a behavior model matched with the number of times of entrance and exit and/or the interval duration of the individual may be established.
Specifically, referring to fig. 2, data representing the number of times of entrance and exit and/or the interval duration of an individual is collected by an infrared sensor and transmitted to a cloud server; furthermore, the cloud server counts the behavior patterns of the individuals according to the collected data used for representing the number of times of the individuals getting in and out of the doorways and/or the interval duration, and establishes the behavior models of the individuals through a machine learning algorithm. Therefore, if the behavior model changes, if no person passes through the behavior model for a long time, the abnormality of the individual is indicated, and a certain physiological abnormal change risk is indicated.
After operation 104, the method of the present invention further comprises, in accordance with an embodiment of the present invention: the cloud server performs anomaly analysis on the at least one data used for representing the individual work and rest characteristics according to the established model matched with the individual work and rest characteristics to obtain an analysis result; and if the analysis result shows that the data for representing the individual work and rest characteristics are abnormal, an alarm is sent out through the cloud server. Therefore, when abnormity occurs, abnormity can be automatically judged through the established model, or when the emergency call button is pressed down, alarm information is automatically pushed to a background Web page, an APP of an emergency contact person and informed ways such as WeChat and telephone of the emergency contact person, and early warning and intervention are achieved.
According to the individual work and rest monitoring method based on the Internet of things, firstly, a sensor is adopted to collect at least one piece of data for representing individual work and rest characteristics; wherein, different sensors correspondingly acquire data used for representing work and rest characteristics of different individuals; then, sending the collected at least one data used for representing the individual work and rest characteristics to a cloud server through a wireless transmission module; then, the cloud server is used for receiving and storing the at least one data used for representing the work and rest characteristics of the individual; and finally, the cloud server performs machine learning according to the stored at least one data for representing the individual work and rest characteristics, and establishes a model matched with the individual work and rest characteristics. Therefore, the indoor basic settings are interconnected through the sensors, the wireless transmission module and the cloud server, and the data which are collected by the sensors and used for representing individual work and rest characteristics are subjected to machine learning through a machine learning algorithm, so that the individual modeling is carried out on the daily life rule or behavior pattern of each user, the background does not need manual monitoring, and the maintenance cost is reduced; meanwhile, the background can automatically identify the abnormality based on the established model, and second-level early warning is achieved.
Based on the above mentioned individual work and rest monitoring method based on the internet of things, an embodiment of the present invention further provides an individual work and rest monitoring system based on the internet of things, as shown in fig. 3, the system 30 includes: a sensor 301 for collecting at least one datum characterizing an individual's work and rest; wherein, different sensors collect data for characterizing the work and rest characteristics of different individuals; the wireless transmission module 302 is configured to send the acquired at least one data used for characterizing the work and rest characteristics of the individual to a cloud server; the cloud server 303 is used for receiving and storing the at least one data for representing the work and rest characteristics of the individual; and the system is also used for performing machine learning according to the stored at least one data for characterizing the individual work and rest characteristics and establishing a model matched with the individual work and rest characteristics.
According to an embodiment of the present invention, the cloud server 303 is further configured to perform anomaly analysis on the at least one data for characterizing the individual features according to the established model matched with the individual work and rest features to obtain an analysis result; and if the analysis result indicates that the data for characterizing the work and rest characteristics of the individual is abnormal, an alarm is given.
According to an embodiment of the present invention, the sensor 301 comprises at least one of the following types: pressure sensor, infrared sensor.
According to an embodiment of the present invention, the sensor 301 is a pressure sensor; the pressure sensor is further configured to collect data indicative of whether the individual is in bed and/or at bed time; the cloud server 303 is further configured to perform machine learning according to the stored data for characterizing whether the individual is in bed and/or in bed time, and establish a sleep model matched with whether the individual is in bed and/or in bed time.
According to an embodiment of the present invention, the sensor 301 is an infrared sensor; the infrared sensor is also used for acquiring data for representing the number of times of the individual entering and exiting the doorway and/or the interval duration; the cloud server 303 is further configured to perform machine learning according to the stored data representing the number of times of entrance and exit of the individual and/or the interval duration, and establish a behavior model matched with the number of times of entrance and exit of the individual and/or the interval duration.
Here, it should be noted that: the above description of the embodiment of the internet of things based individual work and rest monitoring system is similar to the description of the embodiment of the method shown in fig. 1, and has similar beneficial effects to the embodiment of the method shown in fig. 1, and therefore, the description is omitted. For technical details that are not disclosed in the internet of things based individual work and rest monitoring system of the present invention, please refer to the foregoing description of the method embodiment shown in fig. 1 of the present invention for understanding, and therefore, for brevity, will not be described again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another device, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially or partially implemented in the form of a software product, which is stored in a storage medium and includes several instructions to enable an arithmetic unit device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An individual work and rest monitoring method based on the Internet of things is characterized by comprising the following steps:
collecting at least one datum for characterizing the work and rest characteristics of an individual by using a sensor; wherein, different sensors correspondingly acquire data used for representing work and rest characteristics of different individuals;
the collected data used for representing the individual work and rest characteristics are sent to a cloud server through a wireless transmission module;
receiving and storing the at least one data for representing the work and rest characteristics of the individual by using a cloud server;
and performing machine learning by the cloud server according to the stored at least one data for representing the individual work and rest characteristics, and establishing a model matched with the individual work and rest characteristics.
2. The method of claim 1, further comprising:
the cloud server performs anomaly analysis on the at least one data used for representing the individual work and rest characteristics according to the established model matched with the individual work and rest characteristics to obtain an analysis result;
and if the analysis result shows that the data for representing the individual work and rest characteristics are abnormal, an alarm is sent out through the cloud server.
3. The method of claim 1 or 2, wherein the sensor comprises at least one of the following types: pressure sensor, infrared sensor.
4. The method of claim 3, wherein the sensor is a pressure sensor;
the method for acquiring at least one data for characterizing the work and rest characteristics of an individual by using a sensor comprises the following steps:
acquiring data characterizing whether an individual is in bed and/or in bed time using a pressure sensor;
performing machine learning by the cloud server according to the stored at least one data for characterizing the individual work and rest characteristics, and establishing a model matched with the individual work and rest characteristics, including:
and performing machine learning by the cloud server according to the stored data for representing whether the individual is in the bed and/or in the bed time, and establishing a sleep model matched with whether the individual is in the bed and/or in the bed time.
5. The method of claim 3, wherein the sensor is an infrared sensor;
the method for acquiring at least one data for characterizing the work and rest characteristics of an individual by using a sensor comprises the following steps:
acquiring data for representing the number of times of the individual entering and exiting the doorway and/or the interval duration by adopting an infrared sensor;
performing machine learning by the cloud server according to the stored at least one data for characterizing the individual work and rest characteristics, and establishing a model matched with the individual work and rest characteristics, including:
and the cloud server performs machine learning according to the stored data for representing the number of times of the individual entering and exiting the doorway and/or the interval duration, and establishes a behavior model matched with the number of times of the individual entering and exiting the doorway and/or the interval duration.
6. An individual work and rest monitoring system based on the internet of things, the system comprising:
a sensor for acquiring at least one datum characterizing a work and rest of an individual; wherein, different sensors collect data for characterizing the work and rest characteristics of different individuals;
the wireless transmission module is used for transmitting the collected at least one data used for representing the work and rest characteristics of the individual to the cloud server;
the cloud server is used for receiving and storing the at least one data used for representing the work and rest characteristics of the individual; and the system is also used for performing machine learning according to the stored at least one data for characterizing the individual work and rest characteristics and establishing a model matched with the individual work and rest characteristics.
7. The system of claim 6,
the cloud server is further used for carrying out anomaly analysis on the at least one data used for representing the individual features according to the established model matched with the individual work and rest features to obtain an analysis result; and if the analysis result indicates that the data for characterizing the work and rest characteristics of the individual is abnormal, an alarm is given.
8. The system of claim 6 or 7, wherein the sensor comprises at least one of the following types: pressure sensor, infrared sensor.
9. The system of claim 8, wherein the sensor is a pressure sensor;
the pressure sensor is further configured to collect data indicative of whether the individual is in bed and/or at bed time;
the cloud server is further used for conducting machine learning according to the stored data for representing whether the individual is in the bed and/or in the bed time, and establishing a sleep model matched with whether the individual is in the bed and/or in the bed time.
10. The system of claim 8, wherein the sensor is an infrared sensor;
the infrared sensor is also used for acquiring data for representing the number of times of the individual entering and exiting the doorway and/or the interval duration;
the cloud server is further used for conducting machine learning according to the stored data used for representing the number of times of the individual entering and exiting the doorway and/or the interval duration, and establishing a behavior model matched with the number of times of the individual entering and exiting the doorway and/or the interval duration.
Priority Applications (1)
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| CN112998672A (en) * | 2021-03-25 | 2021-06-22 | 苏州沪应通智能科技有限公司 | Intelligent mattress with heart impact scanning sensor |
| CN113450536A (en) * | 2021-06-17 | 2021-09-28 | 桂林航天工业学院 | Safety monitoring system for solitary old people |
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