CN117876919A - Empty nest old man monitoring method and system based on big data artificial intelligence - Google Patents
Empty nest old man monitoring method and system based on big data artificial intelligence Download PDFInfo
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Abstract
The invention belongs to the technical field of empty-nest old man monitoring, and discloses an empty-nest old man monitoring method and system based on big data artificial intelligence, wherein the empty-nest old man monitoring system based on big data artificial intelligence comprises the following steps: the device comprises a video monitoring module, a body temperature monitoring module, a sound acquisition module, a main control module, a fall identification module, a data analysis module, an abnormality early warning module, a cloud storage module, an information notification module and a display module. The invention can effectively identify the normal actions and the habit actions of the empty-nest old people under the normal condition through the fall identification module, can timely detect the stress response of the empty-nest old people when the empty-nest old people collide, and can accurately judge whether the empty-nest old people collide or not by combining the set threshold value; meanwhile, the abnormality early warning module accurately determines whether the activities of the empty nest old people are abnormal or not according to the association network of the related activity information of the empty nest old people and the personnel track of the current empty nest old people, and early warning is carried out.
Description
Technical Field
The invention belongs to the technical field of empty-nest old people monitoring, and particularly relates to an empty-nest old people monitoring method and system based on big data artificial intelligence.
Background
With the increasing emphasis of social aging, the population proportion of the old people in China is higher and higher, and the old people in China (more than or equal to 65 years) in 2010 account for 8.9% of the total population proportion; the proportion of the old population in 2011 reaches 9.1%; the proportion of the old population in 2012 reaches 9.4%. By 2014, the elderly over 80 years old in China reaches 2400 tens of thousands, the disabled and semi-disabled elderly are nearly 4000 tens of thousands, and nursing and monitoring of the elderly become more and more important. With the increasing degree of aging of the population, the number of empty-nest elderly is increasing and they are often faced with health problems and accidents. Conventional monitoring methods are often inadequate to meet this need.
Through the above analysis, the problems and defects existing in the prior art are as follows:
(1) Care and monitoring of elderly people is becoming increasingly important. With the increasing degree of aging of the population, the number of empty-nest elderly is increasing and they are often faced with health problems and accidents.
(2) The existing empty nest old man monitoring system based on big data artificial intelligence detects the change of the postures of the empty nest old man before and after falling, and carries out falling judgment, so that the algorithm is simple, and the situation of missing judgment and misjudgment is easy to occur; meanwhile, whether activities of empty nest old people are abnormal or not cannot be accurately determined.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a method and a system for monitoring empty-nest old people based on big data artificial intelligence.
The invention is realized in this way, a empty nest old man monitoring system based on big data artificial intelligence, which is characterized in that the system comprises:
the system comprises a video monitoring module, a body temperature monitoring module, a sound acquisition module, a biological identification module, an environment monitoring module, a main control module, a fall identification module, a data analysis module, an abnormality early warning module, a cloud storage module, an information notification module and a display module;
the video monitoring module is connected with the main control module and is used for carrying out video monitoring on the empty nest old people through the camera, and the high-definition intelligent camera is used and has night vision and motion tracking functions;
the body temperature monitoring module is connected with the main control module and is used for monitoring the body temperature of the empty nest old people through the temperature sensor;
the sound collection module is connected with the main control module, and captures and analyzes the sound and the environmental sound of the old people by using the high-sensitivity microphone;
the biological recognition module integrates a face recognition system to verify the identity of the monitored person;
the environment monitoring module is provided with an air quality sensor, a temperature and humidity sensor and a smoke and harmful gas detection sensor and is used for comprehensively monitoring the living environment;
the main control module is connected with the video monitoring module, the body temperature monitoring module, the sound acquisition module, the falling identification module, the data analysis module, the abnormality early warning module, the cloud storage module, the information notification module and the display module and used for controlling the normal work of each module;
the falling identification module is connected with the main control module and used for identifying falling actions of the empty nest old people;
the data analysis module is connected with the main control module and is used for carrying out real-time and historical analysis on the collected data by utilizing an artificial intelligent algorithm; analysis includes detecting abnormal activity, identifying unexpected events (such as falls), and assessing the health of the elderly;
the abnormality early warning module is connected with the main control module and is used for early warning the abnormal activities of the empty nest old;
the cloud storage module is connected with the main control module and used for carrying out cloud storage on the monitoring data through the cloud server;
the information notification module is connected with the main control module and is used for generating an alarm and notifying related personnel in various modes, including relatives, nursing staff and medical institutions, when the potential risk or abnormal situation is detected;
the display module is connected with the main control module and used for displaying monitoring video, body temperature, identification results, analysis results, early warning information and notification information through the display.
Further, the biological recognition module captures a facial image of the monitored old person by using a high-resolution camera, analyzes the image through a deep learning algorithm, and extracts facial features including key points of the face, facial contours and skin colors;
comparing the extracted features with facial features prestored in a database, calculating a similarity score by using cosine similarity, and if the similarity score exceeds a preset threshold, confirming the identity of the monitored person.
Further, the environment monitoring module monitors the concentration of pollutants in the air by using a PM2.5 sensor and a VOC sensor, and measures the concentration of particles in the air by using a light scattering principle;
the temperature sensor adopts a thermocouple or a thermal resistor, and measures the temperature according to the characteristic that the resistance value changes along with the temperature; the humidity sensor adopts a capacitive sensor; the smoke sensor is based on a photoelectric principle, when smoke particles enter a photoelectric chamber, light is scattered, so that the current of the sensor is changed, and an alarm is triggered; the harmful gas sensor is based on electrochemical principles, and when harmful gas reacts with chemical substances on the sensor surface, current change is generated and converted into a reading of gas concentration.
Further, the fall recognition module recognition method is as follows:
(1) Configuring sensor parameters, and acquiring body posture data and physical sign data of the empty nest old people in real time by using the sensors; inputting the acquired body posture data and physical sign data into a constructed deep learning model;
(2) The deep learning model calculates according to the received body posture data and physical sign data; and outputting the tumbling grade to the communication equipment according to the corresponding output tumbling grade of the calculation result, and giving an alarm.
Further, when the deep learning model is constructed, the method comprises the following steps:
acquiring sample data from historical data, and calculating the variation of the received body posture data at two adjacent detection time points; after the calculated variation is normalized, inputting the variation into a neural network for learning, and predicting the body posture after the neural network learns;
setting a change amount threshold of the body posture data, and judging whether the change amount of the body posture data exceeds the threshold; when the threshold value is exceeded, according to the prediction result, the acquired physical sign data are combined, under the constraint condition, a fall detection matrix is calculated, and the fall grade is judged according to the calculation result.
Further, the abnormality early warning module early warning method comprises the following steps:
constructing an old man database, and storing the acquired old man data into the old man database; collecting basic information of the empty nest old personnel, wherein the basic information comprises personal information, health conditions, economic conditions, living conditions and family conditions;
collecting a plurality of position information of the empty nest seniors at a plurality of time points;
determining a personnel track of the empty nest old personnel according to the plurality of position information, wherein the personnel track comprises time information and stay information;
based on the personnel track of the empty nest old personnel, determining main residence points of the empty nest old personnel by taking a plurality of preset time periods as periods;
determining a plurality of important personal interested positions of the empty nest old person according to the main resident points of the empty nest old person, the map interest point information and the basic information of the empty nest old person;
comparing the track similarity of empty nest old people at the appointed time and the non-appointed time by taking the important interested positions of the individuals as a starting point and an ending point;
determining behavior events of the empty nest old people according to the track similarity of the empty nest old people;
correlating time information, position information and stay information in the personnel track of the empty nest old personnel with behavior events of the empty nest old personnel to obtain a correlation network aiming at relevant activity information of the empty nest old personnel of behaviors;
correlating time information, stay information and behavior events of the empty-nest old people in the personnel track of the empty-nest old people with position information in the personnel track of the empty-nest old people to obtain a correlation network aiming at relevant activity information of the empty-nest old people in the position;
and determining whether the activities of the empty-nest old people are abnormal according to the correlation network aiming at the behaviors and the related activity information of the empty-nest old people aiming at the positions and the personnel tracks of the current empty-nest old people.
Another object of the present invention is to provide an empty-nest old man monitoring system based on big data artificial intelligence, comprising:
the system comprises a video monitoring module, a body temperature monitoring module, a sound acquisition module, a main control module, a fall identification module, a data analysis module, an abnormality early warning module, a cloud storage module, an information notification module and a display module;
the video monitoring module is connected with the main control module and used for carrying out video monitoring on the empty nest old people through the camera;
the body temperature monitoring module is connected with the main control module and is used for monitoring the body temperature of the empty nest old people through the temperature sensor;
the sound collection module is connected with the main control module and used for collecting sound of the empty nest old people through the sound sensor;
the main control module is connected with the video monitoring module, the body temperature monitoring module, the sound acquisition module, the falling identification module, the data analysis module, the abnormality early warning module, the cloud storage module, the information notification module and the display module and used for controlling the normal work of each module;
the falling identification module is connected with the main control module and used for identifying falling actions of the empty nest old people;
the data analysis module is connected with the main control module and is used for carrying out real-time and historical analysis on the collected data by utilizing an artificial intelligent algorithm; analysis includes detecting abnormal activity, identifying unexpected events (such as falls), and assessing the health of the elderly;
the abnormality early warning module is connected with the main control module and is used for early warning the abnormal activities of the empty nest old;
the cloud storage module is connected with the main control module and used for carrying out cloud storage on the monitoring data through the cloud server;
the information notification module is connected with the main control module and is used for generating an alarm and notifying related personnel in various modes, including relatives, nursing staff and medical institutions, when the potential risk or abnormal situation is detected;
the display module is connected with the main control module and used for displaying monitoring video, body temperature, identification results, analysis results, early warning information and notification information through the display.
Further, the fall recognition module recognition method is as follows:
(1) Configuring sensor parameters, and acquiring body posture data and physical sign data of the empty nest old people in real time by using the sensors; inputting the acquired body posture data and physical sign data into a constructed deep learning model;
(2) The deep learning model calculates according to the received body posture data and physical sign data; and outputting the tumbling grade to the communication equipment according to the corresponding output tumbling grade of the calculation result, and giving an alarm.
Further, the body posture data includes a combination of any one or more of the following: altitude, tilt angle, acceleration, angular acceleration; the sign data includes a combination of any one or more of the following: heart rate, blood oxygen concentration, blood pressure, pulse.
Further, when the deep learning model is constructed, the method comprises the following steps:
acquiring sample data from historical data, and calculating the variation of the received body posture data at two adjacent detection time points; after the calculated variation is normalized, inputting the variation into a neural network for learning, and predicting the body posture after the neural network learns;
setting a change amount threshold of the body posture data, and judging whether the change amount of the body posture data exceeds the threshold; when the threshold value is exceeded, according to the prediction result, the acquired physical sign data are combined, under the constraint condition, a fall detection matrix is calculated, and the fall grade is judged according to the calculation result.
Further, the abnormality early warning module early warning method comprises the following steps:
constructing an old man database, and storing the acquired old man data into the old man database; collecting basic information of the empty nest old personnel, wherein the basic information comprises personal information, health conditions, economic conditions, living conditions and family conditions;
collecting a plurality of position information of the empty nest seniors at a plurality of time points;
determining a personnel track of the empty nest old personnel according to the plurality of position information, wherein the personnel track comprises time information and stay information;
based on the personnel track of the empty nest old personnel, determining main residence points of the empty nest old personnel by taking a plurality of preset time periods as periods;
determining a plurality of important personal interested positions of the empty nest old person according to the main resident points of the empty nest old person, the map interest point information and the basic information of the empty nest old person;
comparing the track similarity of empty nest old people at the appointed time and the non-appointed time by taking the important interested positions of the individuals as a starting point and an ending point;
determining behavior events of the empty nest old people according to the track similarity of the empty nest old people;
correlating time information, position information and stay information in the personnel track of the empty nest old personnel with behavior events of the empty nest old personnel to obtain a correlation network aiming at relevant activity information of the empty nest old personnel of behaviors;
correlating time information, stay information and behavior events of the empty-nest old people in the personnel track of the empty-nest old people with position information in the personnel track of the empty-nest old people to obtain a correlation network aiming at relevant activity information of the empty-nest old people in the position;
and determining whether the activities of the empty-nest old people are abnormal according to the correlation network aiming at the behaviors and the related activity information of the empty-nest old people aiming at the positions and the personnel tracks of the current empty-nest old people.
Further, the determining the personnel track of the empty nest old personnel according to the plurality of position information comprises:
ordering the plurality of location information in time;
sampling the plurality of position information at preset time intervals;
determining the personnel track of the empty nest old personnel according to the sampled position information and the time information and the stay information corresponding to each position information in the sampled position information;
according to the association network of the related activity information of the empty nest old people and the current personnel track of the empty nest old people, determining whether the activity of the empty nest old people is abnormal comprises the following steps:
acquiring the time information, the stay information and the position information of the current empty nest old person from the personnel track of the current empty nest old person;
and determining whether the activities of the empty nest old people are abnormal according to the networking of the related activity information of the empty nest old people, the time information, the stay information and the position information of the current empty nest old people.
The empty nest old man monitoring method of big data artificial intelligence includes:
firstly, performing video monitoring on the empty nest old people by using a camera through a video monitoring module; the body temperature of the empty nest old people is monitored by a body temperature monitoring module through a temperature sensor; the sound collection module is used for collecting the sound of the empty nest old people by utilizing the sound sensor;
step two, the main control module identifies the falling action of the empty nest old people through the falling identification module;
thirdly, carrying out real-time and historical analysis on the collected data by utilizing an artificial intelligence algorithm through a data analysis module; analysis includes detecting abnormal activity, identifying unexpected events (such as falls), and assessing the health of the elderly;
step four, early warning is carried out on abnormal activities of the empty nest old people through an abnormal early warning module; the cloud storage module is used for carrying out cloud storage on the monitoring data by utilizing a cloud server; the potential risk or abnormal situation is monitored by the information notification module pair, which will generate an alarm and notify relevant personnel in a variety of ways, including relatives, caregivers and medical institutions;
and fifthly, displaying the monitoring video, the body temperature, the identification result, the analysis result, the early warning information and the notification information by using a display through a display module.
A computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the big data artificial intelligence empty nest elderly monitoring method.
A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the big data artificial intelligence empty nest elderly monitoring method.
The information data processing terminal is characterized in that the information data processing terminal is used for realizing the empty nest old man monitoring system of big data artificial intelligence.
In combination with the technical scheme and the technical problems to be solved, the technical scheme to be protected has the following advantages and positive effects:
first, overall monitoring function: the intelligent monitoring system integrates various modules such as video monitoring, body temperature monitoring, sound collection, biological identification, environment monitoring and the like, provides omnibearing life monitoring for empty-nest old people, and ensures safety and health.
Intelligent data analysis: abnormal activities are effectively identified through real-time and historical data analysis of an artificial intelligence algorithm, the health condition of the old is timely estimated, and the monitoring accuracy and efficiency are improved.
Early warning mechanism: the integrated abnormality early warning module can timely give an alarm based on data analysis and rapidly respond to possible emergency situations.
Cloud storage function: through the cloud storage module, the monitoring data can be effectively stored and backed up, and remote access and historical data backtracking are facilitated.
Diversified notification modes: the information notification module can notify relatives, nursing staff and medical institutions in various modes, and timeliness and accuracy of information transmission are ensured.
Secondly, the invention relates to a method and a system for monitoring empty-nest old people based on big data and artificial intelligence, which can monitor the life condition of the old people in real time, provide alarm and notification and improve the life quality and safety of the old people. The system is realized by a sensor network, a data analysis and alarm notification module; the invention can effectively identify the normal actions and the habit actions of the empty-nest old people under normal conditions through the fall identification module, can timely detect the stress response of the empty-nest old people when the empty-nest old people collide, and can accurately judge whether the empty-nest old people collide or not by combining the set threshold value. Avoiding the occurrence of missing report and false report. And after collision occurs, the tumbling grade is evaluated by utilizing the immediately detected sign data, and an alarm is output. The rescue personnel can know the body information of the empty nest old people in time, and correct rescue measures are adopted; meanwhile, the abnormality early warning module accurately determines whether the activities of the empty nest old people are abnormal or not according to the association network of the related activity information of the empty nest old people and the personnel track of the current empty nest old people, and early warning is carried out.
Drawings
Fig. 1 is a block diagram of an empty-nest old man monitoring system based on big data artificial intelligence provided by an embodiment of the invention.
Fig. 2 is a flowchart of an empty nest old man monitoring method based on big data artificial intelligence provided by the embodiment of the invention.
Fig. 3 is a flowchart of a fall recognition module recognition method according to an embodiment of the present invention.
In fig. 1: 1. a video monitoring module; 2. a body temperature monitoring module; 3. a sound collection module; 4. a main control module; 5. a fall recognition module; 6. a data analysis module; 7. an abnormality early warning module; 8. a cloud storage module; 9. an information notification module; 10. and a display module.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Two specific embodiments of the embodiment of the invention are as follows:
embodiment one: intelligent life auxiliary system
Lifestyle analysis and environmental regulation: the data analysis module is used to collect and analyze activity data (such as activity time, frequency and duration) of the elderly. According to the analysis result, the indoor environment is automatically adjusted, including an air conditioner and a humidifier are adjusted through a temperature and humidity sensor, and comfortable living conditions are ensured. The air quality sensor monitors the indoor air quality and activates the air purifier if necessary.
Health monitoring and management: the body temperature monitoring module and the biological recognition module track physiological indexes of the old, such as body temperature, heart rate, blood pressure and the like, in real time. The data analysis module performs long-term trend analysis on the collected physiological data to predict a possible health risk. Upon detection of a health anomaly, the information notification module automatically sends an alert to the medical institution and family members.
Safety protection measures: the video monitoring module and the fall-down identifying module are combined to monitor daily activities of the old and timely identify accidents such as falling down. Upon a fall, the system immediately contacts the emergency service and the family members through the information notification module. The video surveillance module may also be used to detect intruders or other home security issues.
Embodiment two: remote medical consultation and emergency rescue system
Remote medical consultation: the video monitoring module is matched with the high-definition intelligent camera, so that the old people can communicate with doctors through video communication. The display module displays diagnostic comments and guidelines of the doctor while allowing the elderly to share their health data. The identity of the old is confirmed through the biological identification module, so that the information safety is ensured.
Emergency rescue mechanism: and the system monitors and analyzes physiological data and activity modes of the old people by combining the data analysis module and the abnormality early warning module. Upon detection of a significant health abnormality (e.g., heart rate abnormality, fall, etc.), the system automatically triggers an emergency response, contacts the emergency rescue service, and notifies the family members. The information notification module provides key information such as the position, the health condition and the like of the old people for rescue workers.
Health data recording and management: the cloud storage module is used for recording health data of the old, including body temperature, heart rate, activity modes and the like. The method provides historical and real-time data for doctors, and helps to more accurately evaluate and diagnose health. Allowing doctors to access these data remotely for timely health management and advice
As shown in fig. 1, the empty-nest old man monitoring system based on big data artificial intelligence provided by the embodiment of the invention comprises: the system comprises a video monitoring module 1, a body temperature monitoring module 2, a sound collecting module 3, a main control module 4, a fall-down identification module 5, a data analysis module 6, an abnormal early warning module 7, a cloud storage module 8, an information notification module 9 and a display module 10.
The video monitoring module 1 is connected with the main control module 4 and is used for carrying out video monitoring on the empty nest old people through the camera;
the body temperature monitoring module 2 is connected with the main control module 4 and is used for monitoring the body temperature of the empty nest old people through the temperature sensor;
the sound collection module 3 is connected with the main control module 4 and is used for collecting sound of the empty nest old people through the sound sensor;
the main control module 4 is connected with the video monitoring module 1, the body temperature monitoring module 2, the sound collecting module 3, the falling recognition module 5, the data analysis module 6, the abnormality early warning module 7, the cloud storage module 8, the information notification module 9 and the display module 10 and is used for controlling the normal work of each module;
the falling identification module 5 is connected with the main control module 4 and is used for identifying falling actions of the empty nest old people;
the data analysis module 6 is connected with the main control module 4 and is used for carrying out real-time and historical analysis on the collected data by utilizing an artificial intelligence algorithm; analysis includes detecting abnormal activity, identifying unexpected events (such as falls), and assessing the health of the elderly;
the abnormality early warning module 7 is connected with the main control module 4 and is used for early warning the abnormal activities of the empty nest old;
the cloud storage module 8 is connected with the main control module 4 and is used for carrying out cloud storage on the monitoring data through a cloud server;
an information notification module 9, connected to the main control module 4, for generating an alarm and notifying the relevant personnel in various ways, including relatives, caregivers and medical institutions, for the detection of a potential risk or abnormal situation;
the display module 10 is connected with the main control module 4 and is used for displaying monitoring video, body temperature, identification result, analysis result, early warning information and notification information through a display.
As shown in fig. 2, the empty nest old man monitoring method based on big data artificial intelligence provided by the invention comprises the following steps:
s101, performing video monitoring on the empty nest old people by using a camera through a video monitoring module; the body temperature of the empty nest old people is monitored by a body temperature monitoring module through a temperature sensor; the sound collection module is used for collecting the sound of the empty nest old people by utilizing the sound sensor;
s102, a main control module identifies the falling actions of the empty nest old people through a falling identification module;
s103, carrying out real-time and historical analysis on the collected data by using an artificial intelligence algorithm through a data analysis module; analysis includes detecting abnormal activity, identifying unexpected events (such as falls), and assessing the health of the elderly;
s104, carrying out early warning on abnormal activities of the empty nest old people through an abnormal early warning module; the cloud storage module is used for carrying out cloud storage on the monitoring data by utilizing a cloud server; the potential risk or abnormal situation is monitored by the information notification module pair, which will generate an alarm and notify relevant personnel in a variety of ways, including relatives, caregivers and medical institutions;
s105, displaying the monitoring video, the body temperature, the identification result, the analysis result, the early warning information and the notification information by using a display module through a display.
As shown in fig. 3, the method for identifying the falling recognition module provided by the invention is as follows:
s201, configuring sensor parameters, and acquiring body posture data and physical sign data of the empty nest old people in real time by using the sensors; inputting the acquired body posture data and physical sign data into a constructed deep learning model;
s202, the deep learning model calculates according to the received body posture data and physical sign data; and outputting the tumbling grade to the communication equipment according to the corresponding output tumbling grade of the calculation result, and giving an alarm.
The body posture data provided by the invention comprises any one or a combination of the following data: altitude, tilt angle, acceleration, angular acceleration; the sign data includes a combination of any one or more of the following: heart rate, blood oxygen concentration, blood pressure, pulse.
The invention provides a deep learning model, which comprises the following steps:
acquiring sample data from historical data, and calculating the variation of the received body posture data at two adjacent detection time points; after the calculated variation is normalized, inputting the variation into a neural network for learning, and predicting the body posture after the neural network learns;
setting a change amount threshold of the body posture data, and judging whether the change amount of the body posture data exceeds the threshold; when the threshold value is exceeded, according to the prediction result, the acquired physical sign data are combined, under the constraint condition, a fall detection matrix is calculated, and the fall grade is judged according to the calculation result.
The abnormality early warning module provided by the invention has the following early warning method:
constructing an old man database, and storing the acquired old man data into the old man database; collecting basic information of the empty nest old personnel, wherein the basic information comprises personal information, health conditions, economic conditions, living conditions and family conditions;
collecting a plurality of position information of the empty nest seniors at a plurality of time points;
determining a personnel track of the empty nest old personnel according to the plurality of position information, wherein the personnel track comprises time information and stay information;
based on the personnel track of the empty nest old personnel, determining main residence points of the empty nest old personnel by taking a plurality of preset time periods as periods;
determining a plurality of important personal interested positions of the empty nest old person according to the main resident points of the empty nest old person, the map interest point information and the basic information of the empty nest old person;
comparing the track similarity of empty nest old people at the appointed time and the non-appointed time by taking the important interested positions of the individuals as a starting point and an ending point;
determining behavior events of the empty nest old people according to the track similarity of the empty nest old people;
correlating time information, position information and stay information in the personnel track of the empty nest old personnel with behavior events of the empty nest old personnel to obtain a correlation network aiming at relevant activity information of the empty nest old personnel of behaviors;
correlating time information, stay information and behavior events of the empty-nest old people in the personnel track of the empty-nest old people with position information in the personnel track of the empty-nest old people to obtain a correlation network aiming at relevant activity information of the empty-nest old people in the position;
and determining whether the activities of the empty-nest old people are abnormal according to the correlation network aiming at the behaviors and the related activity information of the empty-nest old people aiming at the positions and the personnel tracks of the current empty-nest old people.
The method for determining the personnel track of the empty nest old personnel according to the plurality of position information provided by the invention comprises the following steps:
ordering the plurality of location information in time;
sampling the plurality of position information at preset time intervals;
determining the personnel track of the empty nest old personnel according to the sampled position information and the time information and the stay information corresponding to each position information in the sampled position information;
according to the association network of the related activity information of the empty nest old people and the current personnel track of the empty nest old people, determining whether the activity of the empty nest old people is abnormal comprises the following steps:
acquiring the time information, the stay information and the position information of the current empty nest old person from the personnel track of the current empty nest old person;
and determining whether the activities of the empty nest old people are abnormal according to the networking of the related activity information of the empty nest old people, the time information, the stay information and the position information of the current empty nest old people.
A computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the big data artificial intelligence empty nest elderly monitoring method.
A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the big data artificial intelligence empty nest elderly monitoring method.
The information data processing terminal is characterized in that the information data processing terminal is used for realizing the empty nest old man monitoring system of big data artificial intelligence.
The invention relates to a method and a system for monitoring empty-nest old people based on big data and artificial intelligence, which can monitor the life condition of the old people in real time, provide alarm and notification and improve the life quality and safety of the old people. The system is realized by a sensor network, a data analysis and alarm notification module; the invention can effectively identify the normal actions and the habit actions of the empty-nest old people under normal conditions through the fall identification module, can timely detect the stress response of the empty-nest old people when the empty-nest old people collide, and can accurately judge whether the empty-nest old people collide or not by combining the set threshold value. Avoiding the occurrence of missing report and false report. And after collision occurs, the tumbling grade is evaluated by utilizing the immediately detected sign data, and an alarm is output. The rescue personnel can know the body information of the empty nest old people in time, and correct rescue measures are adopted; meanwhile, the abnormality early warning module accurately determines whether the activities of the empty nest old people are abnormal or not according to the association network of the related activity information of the empty nest old people and the personnel track of the current empty nest old people, and early warning is carried out.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (10)
1. Empty nest old man monitoring system based on big data artificial intelligence, characterized in that includes:
the system comprises a video monitoring module, a body temperature monitoring module, a sound acquisition module, a biological identification module, an environment monitoring module, a main control module, a fall identification module, a data analysis module, an abnormality early warning module, a cloud storage module, an information notification module and a display module;
the video monitoring module is connected with the main control module and is used for carrying out video monitoring on the empty nest old people through the camera, and the high-definition intelligent camera is used and has night vision and motion tracking functions;
the body temperature monitoring module is connected with the main control module and is used for monitoring the body temperature of the empty nest old people through the temperature sensor;
the sound collection module is connected with the main control module, and captures and analyzes the sound and the environmental sound of the old people by using the high-sensitivity microphone;
the biological recognition module integrates a face recognition system to verify the identity of the monitored person;
the environment monitoring module is provided with an air quality sensor, a temperature and humidity sensor and a smoke and harmful gas detection sensor and is used for comprehensively monitoring the living environment;
the main control module is connected with the video monitoring module, the body temperature monitoring module, the sound acquisition module, the falling identification module, the data analysis module, the abnormality early warning module, the cloud storage module, the information notification module and the display module and used for controlling the normal work of each module;
the falling identification module is connected with the main control module and used for identifying falling actions of the empty nest old people;
the data analysis module is connected with the main control module and is used for carrying out real-time and historical analysis on the collected data by utilizing an artificial intelligent algorithm; analysis includes detecting abnormal activity, identifying unexpected events (such as falls), and assessing the health of the elderly;
the abnormality early warning module is connected with the main control module and is used for early warning the abnormal activities of the empty nest old;
the cloud storage module is connected with the main control module and used for carrying out cloud storage on the monitoring data through the cloud server;
the information notification module is connected with the main control module and is used for generating an alarm and notifying related personnel in various modes, including relatives, nursing staff and medical institutions, when the potential risk or abnormal situation is detected;
the display module is connected with the main control module and used for displaying monitoring video, body temperature, identification results, analysis results, early warning information and notification information through the display.
2. The empty-nest geriatric monitoring system based on big data artificial intelligence of claim 1, wherein the biometric module captures facial images of the geriatric being monitored using a high resolution camera, analyzes the images by a deep learning algorithm, extracts facial features including key points of the face, facial contours, skin colors;
comparing the extracted features with facial features prestored in a database, calculating a similarity score by using cosine similarity, and if the similarity score exceeds a preset threshold, confirming the identity of the monitored person.
3. The empty-nest old man monitoring system based on big data artificial intelligence according to claim 1, wherein the environment monitoring module monitors the pollutant concentration in the air by using a PM2.5 sensor and a VOC sensor, and measures the concentration of particles in the air by using a light scattering principle;
the temperature sensor adopts a thermocouple or a thermal resistor, and measures the temperature according to the characteristic that the resistance value changes along with the temperature; the humidity sensor adopts a capacitive sensor; the smoke sensor is based on a photoelectric principle, when smoke particles enter a photoelectric chamber, light is scattered, so that the current of the sensor is changed, and an alarm is triggered; the harmful gas sensor is based on electrochemical principles, and when harmful gas reacts with chemical substances on the sensor surface, current change is generated and converted into a reading of gas concentration.
4. The empty nest old man monitoring system based on big data artificial intelligence according to claim 1, wherein the fall recognition module recognition method is as follows:
(1) Configuring sensor parameters, and acquiring body posture data and physical sign data of the empty nest old people in real time by using the sensors; inputting the acquired body posture data and physical sign data into a constructed deep learning model;
(2) The deep learning model calculates according to the received body posture data and physical sign data; and outputting the tumbling grade to the communication equipment according to the corresponding output tumbling grade of the calculation result, and giving an alarm.
5. The empty-nest geriatric monitoring system based on big data artificial intelligence of claim 4, wherein the deep learning model is constructed by:
acquiring sample data from historical data, and calculating the variation of the received body posture data at two adjacent detection time points; after the calculated variation is normalized, inputting the variation into a neural network for learning, and predicting the body posture after the neural network learns;
setting a change amount threshold of the body posture data, and judging whether the change amount of the body posture data exceeds the threshold; when the threshold value is exceeded, according to the prediction result, the acquired physical sign data are combined, under the constraint condition, a fall detection matrix is calculated, and the fall grade is judged according to the calculation result.
6. The empty nest old man monitoring system based on big data artificial intelligence according to claim 1, wherein the anomaly early warning module early warning method is as follows:
constructing an old man database, and storing the acquired old man data into the old man database; collecting basic information of the empty nest old personnel, wherein the basic information comprises personal information, health conditions, economic conditions, living conditions and family conditions;
collecting a plurality of position information of the empty nest seniors at a plurality of time points;
determining a personnel track of the empty nest old personnel according to the plurality of position information, wherein the personnel track comprises time information and stay information;
based on the personnel track of the empty nest old personnel, determining main residence points of the empty nest old personnel by taking a plurality of preset time periods as periods;
determining a plurality of important personal interested positions of the empty nest old person according to the main resident points of the empty nest old person, the map interest point information and the basic information of the empty nest old person;
comparing the track similarity of empty nest old people at the appointed time and the non-appointed time by taking the important interested positions of the individuals as a starting point and an ending point;
determining behavior events of the empty nest old people according to the track similarity of the empty nest old people;
correlating time information, position information and stay information in the personnel track of the empty nest old personnel with behavior events of the empty nest old personnel to obtain a correlation network aiming at relevant activity information of the empty nest old personnel of behaviors;
correlating time information, stay information and behavior events of the empty-nest old people in the personnel track of the empty-nest old people with position information in the personnel track of the empty-nest old people to obtain a correlation network aiming at relevant activity information of the empty-nest old people in the position;
and determining whether the activities of the empty-nest old people are abnormal according to the correlation network aiming at the behaviors and the related activity information of the empty-nest old people aiming at the positions and the personnel tracks of the current empty-nest old people.
7. An empty-nest geriatric monitoring method of implementing big data artificial intelligence of an empty-nest geriatric monitoring system of any one of claims 1-6, the method of empty-nest geriatric monitoring of big data artificial intelligence comprising:
firstly, performing video monitoring on the empty nest old people by using a camera through a video monitoring module; the body temperature of the empty nest old people is monitored by a body temperature monitoring module through a temperature sensor; the sound collection module is used for collecting the sound of the empty nest old people by utilizing the sound sensor;
step two, the main control module identifies the falling action of the empty nest old people through the falling identification module;
thirdly, carrying out real-time and historical analysis on the collected data by utilizing an artificial intelligence algorithm through a data analysis module; analysis includes detecting abnormal activity, identifying unexpected events (such as falls), and assessing the health of the elderly;
step four, early warning is carried out on abnormal activities of the empty nest old people through an abnormal early warning module; the cloud storage module is used for carrying out cloud storage on the monitoring data by utilizing a cloud server; the potential risk or abnormal situation is monitored by the information notification module pair, which will generate an alarm and notify relevant personnel in a variety of ways, including relatives, caregivers and medical institutions;
and fifthly, displaying the monitoring video, the body temperature, the identification result, the analysis result, the early warning information and the notification information by using a display through a display module.
8. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the big data artificial intelligence empty nest elderly monitoring method of any of claims 7.
9. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the big data artificial intelligence empty-nest elderly monitoring method of any of claims 7.
10. An information data processing terminal, wherein the information data processing terminal is used for realizing the empty nest old man monitoring system of big data artificial intelligence according to claim 1.
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