CN114041763A - Wisdom sleep monitor system based on many information fusion - Google Patents
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Abstract
The invention relates to an intelligent sleep monitoring system based on multi-information fusion. The system consists of a sleep environment detection node, a sleep quality monitoring node and an OneNET cloud platform. The system carries out real-time multi-dimensional monitoring on human body sleep environment parameters and physiological characteristics through various sensors distributed in a sleep environment, the monitored environment and physiological characteristic data are uploaded to an OneNet cloud platform, a multi-information fusion technology is adopted to carry out fusion analysis on the physiological characteristics of a human body serving as subclasses of naive Bayesian classification optimized based on a field rough set at a decision-level fusion end, and an improved naive Bayesian algorithm is utilized to realize real-time sleep staging. Deep mining and matching analysis are carried out on the sleep environment detection data and the sleep quality monitoring data through the OneNet cloud platform, and real-time autonomous optimization and adjustment are carried out on the current sleep environment according to the feedback information, so that the purposes of helping a user to optimize the sleep environment and improve the sleep quality are achieved.
Description
Technical Field
The invention relates to the field of smart home, in particular to a smart sleep monitoring system based on multi-information fusion.
Background
The sleep is the more important physiological activity of people in a lifetime, one third of the life cycle is in the physiological process, and the high-quality sleep can ensure that the human body has good physical and mental health. However, in the current society, with the acceleration of life rhythm, the increase of working pressure of people and the improvement of the utilization rate of intelligent electronic equipment, the sleep quality of people is greatly influenced, and more than three people in China suffer from sleep disorder diseases at present. In the epidemic situation, a series of sleep problems are caused due to excessive psychological pressure of people, so that the real-time tracking and monitoring of the sleep environment and the sleep quality of a human body are very important for improving the sleep quality and improving the immunity of the human body.
At present, the demand of the sleep market starts rapidly, but the intelligent sleep product on the market is not fully satisfactory. The single sleep environment detection and sleep quality monitoring products are countless in the market, but an intelligent sleep device which is used for improving the sleep quality of people by combining the single sleep environment detection and the sleep quality monitoring products is almost absent; and a single sleep quality monitoring device such as a smart bracelet only monitors but does not control, and a user obtains only one cold data report, so that the aim of optimizing sleep quality cannot be achieved, and good user experience is difficult to achieve. Therefore, it is difficult to achieve the effect of improving the sleep of the user only by a single sleep environment and sleep quality monitoring device.
Therefore, the invention provides an intelligent sleep monitoring system based on multi-information fusion, which can improve the reliability, the real-time performance and the utilization rate of the system and achieve the aims of helping a user to optimize the sleep environment and improve the sleep quality.
Disclosure of Invention
The invention aims to provide a multi-information fusion-based intelligent sleep monitoring system, which carries out real-time multi-dimensional monitoring on human sleep environment parameters and physiological characteristics through various sensors distributed in a sleep environment, uploads the monitored environment and physiological characteristic data to an OneET cloud platform, carries out fusion analysis on the physiological characteristics of a human body serving as a subclass of naive Bayesian classification optimized based on a field rough set at a decision-level fusion end by adopting a multi-information fusion technology, realizes real-time sleep staging by utilizing an improved naive Bayesian algorithm, and finally carries out deep mining analysis on the relationship between the sleep environment and the sleep quality in the OneET cloud platform, autonomously adjusts and optimizes the sleep environment, so that a user can fall asleep in a comfortable sleep environment, and the sleep quality of the user is greatly improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
a smart sleep monitoring system based on multi-information fusion is composed of a sleep environment detection node, a sleep quality monitoring node and an OneNET cloud platform. The sleep environment detection node comprises a power supply module, a temperature and humidity sensor, a light intensity sensor, an air quality sensor, a noise sensor, a pressure sensor, a voice interaction module, an OLED display module and a Wi-Fi communication module. The sleep quality monitoring node comprises a power supply module, an infrared temperature measuring module, a heart rate blood oxygen sensor, a body motion sensor, an OLED display module and a Wi-Fi communication module. Data of the sleep environment detection node and the sleep quality monitoring node are uploaded to the OneNet cloud platform through the Wi-Fi communication module, and the monitored data can be checked at any time and any place.
In the sleep environment detection node, when a first main control chip detects that the value of a pressure sensor is larger than 3Kg, the system starts to work, the first main control chip controls a temperature and humidity sensor, a light intensity sensor, an air quality sensor and a noise sensor to work, a voice interaction module warmly prompts the current sleep environment, detected data are displayed through an OLED display module to realize human-computer visual interaction, and meanwhile, the data are uploaded to an OneNET cloud platform through a Wi-Fi communication module. The power supply module is connected with the temperature and humidity sensor, the light intensity sensor, the air quality sensor, the pressure sensor, the voice interaction module, the OLED display module and the Wi-Fi communication module through leads respectively, and the power supply module is connected with the first main control chip through leads and used for providing electric energy.
In the sleep quality monitoring node, the second main control chip controls the infrared temperature measuring module, the heart rate blood oxygen sensor and the physical movement sensor to monitor physiological characteristics of a user during sleep, monitored data can be displayed in real time through the OLED display module and uploaded to the OneNet cloud platform through the Wi-Fi communication module, and the data are further mined, processed and analyzed through a multi-information fusion technology and an optimized naive Bayesian algorithm in a cloud. The corresponding pins of the infrared temperature measurement module, the heart rate blood oxygen sensor, the body movement sensor, the OLED display module and the Wi-Fi communication module are connected with the I/O port corresponding to the second main control chip through wires, and the power supply module is connected with the infrared temperature measurement module, the heart rate blood oxygen sensor, the body movement sensor, the OLED display module, the Wi-Fi communication module and the second main control chip through wires respectively and used for supplying electric energy.
The OneNET cloud platform is a PaaS Internet of things open platform manufactured by China Mobile, can easily realize equipment access and equipment connection, uploads monitored data to the OneNET cloud platform through a Wi-Fi communication module, simplifies the pressure of front-end data processing, performs matching analysis on sleep environment detection data and sleep quality monitoring data through the OneET cloud platform, performs real-time autonomous optimization and adjustment on the current sleep environment according to cloud feedback information, and realizes 'cloud, edge and end' real-time interaction of the sleep environment detection node data and the sleep quality monitoring node data based on the OneNET cloud platform. After the user uses the sleep environment parameter and the sleep quality information for a long time, the OneNet cloud platform provides the optimal suggestion for improving the sleep of the user according to the matching analysis of the sleep environment parameter and the sleep quality information.
Preferably, the first and second main control chips of the intelligent sleep monitoring system based on multi-information fusion provided by the invention both adopt STM32F103 series, and the intelligent sleep monitoring system has the characteristics of high integration, low energy consumption and strong real-time performance;
preferably, the data transmission and communication of the intelligent sleep monitoring system based on multi-information fusion provided by the invention adopt Wi-Fi wireless communication modules, so that the high-efficiency data transmission and no packet loss are ensured, and the system has real-time performance and reliability.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention combines various sensors to realize multidimensional monitoring of human body sleep environment and sleep quality, thereby ensuring the reliability of monitoring data;
2. the invention mostly adopts a non-contact monitoring method, does not need to be in direct contact with the skin of a human body, realizes non-invasive monitoring and improves the comfort level of a tested person;
3. the method and the system perform deep mining analysis on the relationship between the sleep environment and the sleep quality based on the OneNet cloud platform, autonomously adjust and optimize the sleep environment, and enable the user to fall asleep in a comfortable sleep environment, so that the sleep quality of the user is greatly improved;
4. the invention adopts a multi-information fusion technology, and compared with single data information brought by a single sensor, the multi-sensor information fusion has greater advantages, and the result reliability after system fusion is also greatly improved.
Drawings
FIG. 1 is a diagram of a system for monitoring sleep based on multi-information fusion according to the present invention;
FIG. 2 is a feature level fusion model of an intelligent sleep monitoring system based on multi-information fusion according to the present invention;
FIG. 3 is a flowchart of a naive Bayesian sleep stage classification algorithm of the intelligent sleep monitoring system based on multi-information fusion after optimization of a domain-based rough set.
Detailed Description
The invention is further illustrated by the following specific examples.
As shown in fig. 1, a structure diagram of a smart sleep monitoring system based on multi-information fusion according to the present invention is shown, and the smart sleep monitoring system is composed of a sleep environment detection node, a sleep quality monitoring node, and an OneNET cloud platform.
The specific implementation process comprises the following steps:
the sleep environment detection node is placed in a bedroom, and a power supply module in the node provides electric energy for a temperature and humidity sensor, a light intensity sensor, an air quality sensor, a noise sensor, a pressure sensor, a voice interaction module, an OLED display module, a Wi-Fi communication module and a first main control chip; the pressure sensor is arranged under the mattress, the system is initialized only when a user goes to bed for rest, namely the value sensed by the pressure sensor is more than 3Kg, at the moment, the first main control chip controls each sensor to carry out multi-dimensional detection on the sleeping environment parameters of the human body, and the first main control chip controls the voice interaction module to warm and prompt the current sleeping environment condition; detected data are uploaded to the OneNet cloud platform through the Wi-Fi communication module, the cloud end issues a control instruction to automatically adjust the current sleep environment after the environmental parameters are matched and analyzed with the sleep quality data, and for example, the indoor temperature is controlled through an air conditioner, the air humidity is adjusted through a humidifier, and light and noise are controlled through an electric curtain. Through the independent adjustment and optimization of the sleeping environment, the user can fall asleep in a comfortable sleeping environment, and therefore the sleeping quality of the user is greatly improved.
The sleep quality monitoring node is worn on the hand of a tested person, and a power supply module in the node supplies electric energy to the infrared temperature measuring module, the heart rate blood oxygen sensor, the body motion sensor, the OLED display module, the Wi-Fi communication module and the second main control chip; after the system is initialized, the second main control chip controls the infrared temperature measurement module and the heart rate blood oxygen sensor to monitor physiological characteristics of the human body in real time during sleeping, and the body movement sensor is used for recording sleeping posture change conditions of the human body during sleeping; the physiological characteristics of the human body during sleeping can be watched on the OLED display module in real time or can be checked on the background by transmitting data to the OneNET cloud platform through the Wi-Fi communication module.
The OneNET cloud platform is arranged on a computer or a mobile phone mobile terminal, monitored data are uploaded to the OneNET cloud platform through the Wi-Fi communication module, pressure of front-end data processing is simplified, matching analysis can be conducted on sleep environment detection data and sleep quality monitoring data through the OneNET cloud platform, real-time autonomous optimization and adjustment can be conducted on the current sleep environment according to analysis result feedback control information, and cloud, edge and end real-time interaction of the sleep environment detection node data and the sleep quality monitoring node data is achieved.
Fig. 2 shows a feature level fusion model of an intelligent sleep monitoring system based on multi-information fusion, the feature level fusion belongs to the middle level of the fusion level, feature signals are extracted from each sensor to form a group of feature vectors, data information is fused for each group of feature vectors, and the feature level fusion reduces the influence caused by overlarge data by performing necessary compression on original signals, so that the real-time performance and the precision of the system for processing data are improved. Therefore, the real-time performance and the reliability of monitoring data transmission can be better ensured.
Fig. 3 is a flowchart of a naive bayesian sleep staging algorithm optimized by a domain rough set based on an intelligent sleep monitoring system based on multi-information fusion, which specifically includes the steps of acquiring initial data, acquiring physiological characteristics of a user in a waking period, a non-rapid eye movement period and a rapid eye movement period, setting 15% of a reduction range as a base line, and dividing the interval between a lowest value and an average value of the waking period into three intervals of a deep sleep period, a light sleep period and a rapid eye movement period; monitoring whether the human body has body movement through a body movement sensor, when the body movement occurs, the body movement time is within 3-6 seconds, then the physiological characteristic signals of 3 minutes are not analyzed, the time is directly judged to be a light sleep period, if the body movement time is more than 3 seconds, the time is judged to be a wake-up period, if the body movement phenomenon does not occur, people hardly reach the stage of sleep staging within one minute in life, the sleep time phase judgment is carried out on the physiological characteristic signals within two minutes according to the thought of an improved naive Bayes classification algorithm, and the sleep time phase with the highest percentage is used as the judgment result within two minutes. And finally, outputting a sleep time phase judgment result within two minutes to an OLED display module and an OneNET cloud platform in real time, wherein the OneNET cloud platform performs matching analysis on sleep time phases at different times and the sleep environment at the same time, autonomously issues a control instruction to perform real-time optimization adjustment on the current sleep environment, can obtain the optimal sleep environment parameters of the user after long-term use, and provides an optimal suggestion for improving the sleep of the user.
The intelligent sleep monitoring system based on multi-information fusion disclosed by the invention is based on the Internet of things technology, the multi-information fusion technology and the OneNet cloud platform, so that the deep mining of the relationship between the sleep environment detection data and the sleep quality monitoring data is realized, the OneET cloud platform carries out matching analysis on the sleep environment detection data and the sleep quality monitoring data, and feeds back control information to carry out real-time autonomous optimization and regulation on the current sleep environment according to the analysis result, so that the purposes of helping a user to optimize the sleep environment and improve the sleep quality are achieved.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood by those skilled in the art that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. The intelligent sleep monitoring system based on multi-information fusion is characterized by comprising a sleep environment detection node, a sleep quality monitoring node and an OneNET cloud platform.
2. The intelligent sleep monitor system based on multi-information fusion as claimed in claim 1, wherein: the sleep environment detection node comprises a power supply module, a temperature and humidity sensor, a light intensity sensor, an air quality sensor, a noise sensor, a pressure sensor, a voice interaction module, an OLED display module, a Wi-Fi communication module and a first main control chip; the corresponding pins of each sensor, the voice interaction module, the OLED display module and the Wi-Fi communication module are connected with the corresponding I/O port of the first main control chip through leads, and the power supply module is connected with each module through leads and used for supplying electric energy.
3. The intelligent sleep monitor system based on multi-information fusion as claimed in claim 1, wherein: the sleep quality monitoring node comprises a power supply module, an infrared temperature measuring module, a heart rate and blood oxygen sensor, a body motion sensor, an OLED display module, a Wi-Fi communication module and a second main control chip; the corresponding pins of the infrared temperature measurement module, the heart rate blood oxygen sensor, the body movement sensor, the OLED display module and the Wi-Fi communication module are connected with the corresponding I/O port of the second main control chip through wires, and the power supply module is connected with the infrared temperature measurement module, the heart rate blood oxygen sensor, the body movement sensor, the OLED display module, the Wi-Fi communication module and the second main control chip through wires and used for supplying electric energy.
4. The intelligent sleep monitor system based on multi-information fusion as claimed in claim 1, wherein: the OneNet cloud platform is communicated with the sleep environment detection node and the sleep quality monitoring node through the Wi-Fi wireless communication module, the OneET cloud platform performs matching analysis on data of the OneET cloud platform and the sleep quality monitoring node, and performs real-time autonomous optimization and adjustment on the current sleep environment according to analysis result feedback control information, so that the purposes of helping a user optimize the sleep environment and improving the sleep quality are achieved.
5. The intelligent sleep monitor system based on multi-information fusion as claimed in claim 1, wherein: STM32F103 series are selected for a first main control chip and an analysis node second main control chip in the collection node.
6. The intelligent sleep monitor system based on multi-information fusion as claimed in claim 1, wherein: the sleep quality monitoring node adopts the fusion of feature levels in a multi-information fusion technology, reduces the influence caused by overlarge data by compressing the original signal, and ensures the real-time performance and the reliability of monitoring data transmission.
7. The intelligent sleep monitor system based on multi-information fusion as claimed in claim 1, wherein: according to the naive Bayesian sleep stage division algorithm based on the optimization of the field rough set, the data redundancy items are removed from the rough set, so that the efficiency and the accuracy of the division of the human sleep interval are improved, and the sleep phase is divided into a wake-up period, a rapid eye movement period, a light sleep period and a deep sleep period according to the physiological characteristics of the human sleep such as heart rate, blood oxygen and body temperature.
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| CN116386120A (en) * | 2023-05-24 | 2023-07-04 | 杭州企智互联科技有限公司 | Noninductive monitoring management system |
| CN116386120B (en) * | 2023-05-24 | 2023-08-18 | 杭州企智互联科技有限公司 | A noninductive control management system for wisdom campus dormitory |
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