CN112887383A - Dynamic electrocardiogram data monitoring system based on Internet of things - Google Patents

Dynamic electrocardiogram data monitoring system based on Internet of things Download PDF

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CN112887383A
CN112887383A CN202110066492.4A CN202110066492A CN112887383A CN 112887383 A CN112887383 A CN 112887383A CN 202110066492 A CN202110066492 A CN 202110066492A CN 112887383 A CN112887383 A CN 112887383A
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things
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刘哲
杜春玲
粟锦平
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Hunan Ventmed Medical Technology Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention provides a dynamic electrocardiogram data monitoring system based on the Internet of things, which comprises the following modules: the system comprises a mobile electrocardio acquisition terminal, an electrocardio signal receiving module, an Internet of things wireless connection communication module, a main control module and a dynamic electrocardio data monitoring module which is in communication connection with the main control module; the mobile electrocardio acquisition terminal comprises an electrocardio electrode, a data processing module and an electrocardio acquisition terminal signal transmitting module; the main control module transmits the electrocardiosignal data to the dynamic electrocardio-data monitoring module for data statistical analysis and storage; the wireless connection communication module of the Internet of things executes an energy-saving transmission power control algorithm to adaptively and dynamically control the transmission power of the wireless connection gateway, and the estimated average value of the dynamically updated signal intensity information data
Figure DDA0002904425220000011
Satisfying the threshold range TRL, TRHvar]By adjusting the power supply level of the transmission power in a suitable manner, the transmission power is not changed, saving power consumed by the wireless connection communication.

Description

Dynamic electrocardiogram data monitoring system based on Internet of things
Technical Field
The invention belongs to the technical field of dynamic electrocardiogram monitoring, and particularly relates to a dynamic electrocardiogram data monitoring system based on the Internet of things.
Background
With the continuous rise of the incidence of cardiovascular diseases, the cardiovascular diseases seriously affect the health of people, and heart diseases have sudden and high risk, so that the real-time and effective monitoring of patients with heart diseases is very important in order to find the disease condition in time.
Along with scientific progress, modern medical technology is rapidly developed, and medical monitoring systems are continuously perfected, wherein an electrocardiogram monitor plays a vital role in medical institutions, is an electronic instrument which must be used by the medical institutions, and the quality of the electronic instrument directly influences the diagnosis effect on patients. The ECG monitor is widely used in clinical application and is also more and more paid attention by medical institutions, and the ECG monitor is characterized by simple operation, wide application field and easy recording and observation of physiological indexes of patients, and by recording the physiological parameters of the patients, the medical monitoring center can judge the actual illness state of the patients according to the set normal physiological parameters for comparative analysis, thereby carrying out diagnosis. Therefore, the development of the ECG monitor plays an important role in medical institutions and patients.
However, the technical problems that the energy consumption is too large and the gateway transmission power of the signal transmission node is unstable exist in the wireless transmission process of the electrocardio data monitoring system in the prior art, the service life of a battery of the mobile dynamic monitoring system is influenced, and the technical problem to be solved urgently is solved.
Disclosure of Invention
Aiming at the defects, the invention provides the dynamic electrocardiogram data monitoring system based on the Internet of things, which can adaptively and dynamically control the transmission power of the wireless connection gateway by adopting an energy-saving transmission power control algorithm during wireless signal transmission, thereby saving the power consumption of wireless connection communication.
The invention provides the following technical scheme: a dynamic electrocardiogram data monitoring system based on the Internet of things comprises the following modules:
the system comprises a mobile electrocardio acquisition terminal, an electrocardio signal receiving module, an Internet of things wireless connection communication module, a main control module and a dynamic electrocardio data monitoring module which is in communication connection with the main control module;
the mobile electrocardio acquisition terminal comprises an electrocardio electrode, a data processing module and an electrocardio acquisition terminal signal transmitting module;
the mobile electrocardio acquisition terminal is connected with the body of a monitored user to acquire electrocardiosignals of the user; the electrocardiosignal receiving module receives electrocardiosignal data acquired by the mobile electrocardiosignal acquisition terminal and transmits the electrocardiosignal data to the Internet of things wireless connection communication module (3); the internet of things wireless connection communication module transmits the received electrocardiosignal data to the main control module, and the main control module transmits the electrocardiosignal data to the dynamic electrocardiosignal data monitoring module for data statistical analysis and storage;
the wireless connection communication module of the internet of things executes an energy-saving transmission power control algorithm to adaptively and dynamically control the transmission power of the wireless connection gateway, so that the power consumption of wireless connection communication is saved.
Further, the energy-saving transmission power control algorithm comprises the following steps:
m1: the signal strength information data received by the received signal strength indicator in the wireless connection communication module of the Internet of things, and the transmission power value delta P at the moment iiLatest signal strength information data value RlatestCalculating an estimated average of the signal strength information data
Figure BDA0002904425200000021
M2: the wireless connection communication module acquires the latest signal strength information data value RlatestAnd the estimated mean value
Figure BDA0002904425200000022
Comparing, constructing an updating algorithm model to update the signal strength information dataIs estimated average value of
Figure BDA0002904425200000031
And obtaining an estimated average value based on said updating
Figure BDA0002904425200000032
Distinguishing a good channel and a bad channel;
m3: the wireless connection communication module of the Internet of things is updated
Figure BDA0002904425200000033
With known target signal strength RtargetComparing, constructing a transmission power level delta P calculation model, and calculating the transmission power level of the channel where the signal is located;
m4: calculating an estimated average of the updated signal strength information data
Figure BDA0002904425200000034
A constant low threshold TRL and a variable high threshold TRH respectively associated with the power required for said signal transmissionvarAfter comparison, a self-adaptive dynamic control wireless connection gateway transmission power comparison condition is established, and the wireless transmission module is self-adaptively adjusted to have stable transmission power and low power consumption when being connected with the gateway.
Further, the update algorithm model of the M2 step is as follows:
if it is
Figure BDA0002904425200000035
Then
Figure BDA0002904425200000036
If it is
Figure BDA0002904425200000037
Then
Figure BDA0002904425200000038
Wherein said alpha is1Is the good letterTrack average weight, said α2And averaging the weights of the bad channels.
Further, the transmission power level Δ P calculation model in the step M3 is as follows:
Figure BDA0002904425200000039
Figure BDA00029044252000000310
wherein N is the number of signal strength information data transmitted by the wireless connection communication module.
Further, the adaptive dynamic control wireless connection gateway transmission power comparison condition in the step M4 is as follows:
Figure BDA0002904425200000041
further, the known signal strength R in the M3 steptargetThe value of (d) was-85 dBm.
Further, the value of the constant low threshold TRL in the step M4 is-88 dBm.
Further, the TRH in the M4 stepvarThe calculation formula of (2) is as follows:
TRHvar＝TRL+σ；
Figure BDA0002904425200000042
wherein, R isiIs the signal strength information data value at time i, i ═ 1,2, …, N; wherein σ and N are a standard deviation and a number of signal strength information data transmitted by the wireless connection communication module, respectively.
Furthermore, the data processing module in the mobile electrocardio acquisition terminal comprises a front-end amplification unit, a signal filtering unit, a rear-end amplification unit, a wave trap and an A/D converter.
Furthermore, the dynamic electrocardio-data monitoring module comprises an electrocardio-signal database, a diagnosis and analysis module and a case query module which are connected with the electrocardio-signal database.
The invention has the beneficial effects that:
1. the wireless connection communication module in the dynamic electrocardiogram data monitoring system based on the Internet of things firstly collects the latest signal intensity information data value RlatestAnd the estimated average value
Figure BDA0002904425200000043
Comparing, and constructing the estimated average value of the updated signal intensity information data of the updated algorithm model
Figure BDA0002904425200000051
And based on the updated estimated average
Figure BDA0002904425200000052
Distinguishing the transmission channel with the latest signal intensity, belonging to good channel or bad channel, and updating the wireless connection communication module of the channel calculated according to the updating algorithm
Figure BDA0002904425200000053
With known target signal strength RtargetComparing, constructing a transmission power level delta P calculation model, and finally calculating the estimated average value of the updated signal intensity information data
Figure BDA0002904425200000054
A constant low threshold TRL and a variable high threshold TRH, respectively associated with the power required for signal transmissionvarAfter comparison, a self-adaptive dynamic control wireless connection gateway transmission power comparison condition is established, and the transmission power delta P of the wireless transmission module is adjusted in a self-adaptive mode when the wireless transmission module is connected with the gateway, so that the wireless connection gateway transmission power comparison condition is stable and low in power consumption.
2. The invention provides dynamic electrocardiogram data monitoring based on the Internet of thingsThe wireless connection communication module in the system sets an estimated average value of signal strength information data updated by comparison
Figure BDA0002904425200000055
Constant low threshold TRL and variable high threshold TRHvarThe transmission power is adaptively adjusted. If the estimated average value of the updated signal strength information data
Figure BDA0002904425200000056
Over TRHvarIt means that the channel state is very good and thus the transmission power is reduced to save energy. On the other hand, if the estimated average of the updated signal strength information data
Figure BDA0002904425200000057
When the TRL is reduced to be lower than the TRL, the channel state is in a bad channel state, so that the electrocardio transmission data information packet in the transmission channel is prevented from being lost by rapidly increasing the transmission power. If the estimated average value of the updated signal strength information data
Figure BDA0002904425200000058
Satisfying the threshold range TRL, TRHvar]By adjusting the supply level of the transmission power in an appropriate manner, the transmission power does not change.
3. The dynamic electrocardiogram data monitoring system based on the Internet of things can obtain dynamic electrocardiogram data with low noise by arranging the data processing module comprising the front-end amplifying unit, the signal filtering unit, the rear-end amplifying unit, the wave trap and the A/D converter.
4. The dynamic electrocardiogram data monitoring system based on the Internet of things can store and record dynamically monitored electrocardiogram data by arranging the electrocardiogram signal database, and utilizes the dynamic electrocardiogram data through the diagnosis analysis module and the case query module which are connected with the electrocardiogram signal database to perform medical analysis statistics and pathological research, thereby contributing to the heart medicine.
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The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings. Wherein:
fig. 1 is an overall schematic diagram of a dynamic electrocardiogram data monitoring system based on the internet of things, provided by the invention;
FIG. 2 is a schematic structural diagram of a data processing module in the mobile ECG acquisition terminal according to the present invention;
fig. 3 is a schematic structural diagram of a dynamic electrocardiographic data monitoring module provided by the present invention.
Detailed description of the preferred embodiments
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the dynamic electrocardiographic data monitoring system based on the internet of things provided by the present invention includes the following modules:
the system comprises a mobile electrocardio acquisition terminal 1, an electrocardio signal receiving module 2, an Internet of things wireless connection communication module 3, a main control module 4 and a dynamic electrocardio data monitoring module 5 which is in communication connection with the main control module 4;
the mobile electrocardio acquisition terminal 1 comprises an electrocardio electrode 11, a data processing module 12 and an electrocardio acquisition terminal signal transmitting module 13;
the mobile electrocardio acquisition terminal 1 is connected with the body of a monitored user to acquire electrocardiosignals of the user; the electrocardiosignal receiving module 2 receives electrocardiosignal data acquired by the mobile electrocardiosignal acquisition terminal 1 and transmits the electrocardiosignal data to the Internet of things wireless connection communication module 3; the internet of things wireless connection communication module 3 transmits the received electrocardiosignal data to the main control module 4, and the main control module 4 transmits the electrocardiosignal data to the dynamic electrocardiosignal data monitoring module 5 for data statistical analysis and storage.
As shown in fig. 2, the data processing module 12 in the mobile electrocardiograph acquisition terminal 1 includes a front-end amplification unit 121, a signal filtering unit 122, a back-end amplification unit 123, a wave trap 124 and an a/D converter 125.
As shown in fig. 3, the dynamic electrocardiographic data monitoring module 5 includes an electrocardiographic signal database 51, a diagnosis and analysis module 52 connected to the electrocardiographic signal database 51, and a case query module 53.
The wireless connection communication module 3 of the internet of things executes an energy-saving transmission power control algorithm to adaptively and dynamically control the transmission power of the wireless connection gateway, so that the power consumption of wireless connection communication is saved; the energy-saving transmission power control algorithm comprises the following steps:
m1: the signal strength information data received by the received signal strength indicator in the wireless connection communication module 3 of the internet of things, and the transmission power value delta P at the moment iiLatest signal strength information data value RlatestCalculating an estimated average of signal strength information data
Figure BDA0002904425200000071
M2: the wireless connection communication module 3 collects the latest signal intensity information data value RlatestAnd the estimated average value
Figure BDA0002904425200000072
Comparing, and constructing the estimated average value of the updated signal intensity information data of the updated algorithm model
Figure BDA0002904425200000073
And based on the updated estimated average
Figure BDA0002904425200000074
And distinguishing a good channel and a bad channel, and updating an algorithm model as follows:
if it is
Figure BDA0002904425200000081
Then
Figure BDA0002904425200000082
If it is
Figure BDA0002904425200000083
Then
Figure BDA0002904425200000084
Wherein alpha is1For good channel average weight, alpha2Averaging weights for bad channels;
m3: the wireless connection communication module 3 of the internet of things is updated
Figure BDA0002904425200000085
With known target signal strength RtargetAnd comparing, constructing a transmission power level delta P calculation model, and calculating the transmission power level of the channel where the signal is located, wherein the transmission power level delta P calculation model is as follows:
Figure BDA0002904425200000086
Figure BDA0002904425200000087
wherein N is the number of signal strength information data transmitted by the wireless connection communication module;
m4: estimating average value of updated signal strength information data
Figure BDA0002904425200000088
A constant low threshold TRL and a variable high threshold TRH, respectively associated with the power required for signal transmissionvarAfter comparison, establishing a comparison condition of the transmission power of the self-adaptive dynamic control wireless connection gateway, adaptively adjusting the transmission power of the wireless transmission module when the wireless transmission module is connected with the gateway to be stable and low in power consumption, wherein the comparison condition of the transmission power of the self-adaptive dynamic control wireless connection gateway is as follows:
Figure BDA0002904425200000089
known signal strength R in the M3 steptargetThe value of (d) was-85 dBm. The value of the constant low threshold TRL in the M4 step is-88 dBm, TRHvarThe calculation formula of (2) is as follows:
TRHvar＝TRL+σ；
Figure BDA0002904425200000091
wherein R isiThe signal strength information data value at time i, i ═ 1,2, …, N; where σ and N are the standard deviation (unit: dBm) and the amount of signal strength information data transmitted by the wireless connection communication module, respectively.
The dynamic electrocardiogram data monitoring system based on the Internet of things solves the problem of energy consumption of sensing nodes in the wireless information data transmission process, and provides a method for prolonging the service life of a battery of a medical wearable device. The dynamic electrocardiogram data monitoring system based on the Internet of things provided by the invention has the advantages that the battery life is prolonged by 18-20%, the battery life of clinical-grade electrocardiogram is prolonged by 8-10%, the technical problem of overlarge energy consumption of a gateway caused by active Internet connection in the wireless dynamic data transmission process of the Internet of things is solved, and the battery life of the gateway is prolonged.
While the invention has been described with reference to a preferred embodiment, various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the technical features mentioned in the embodiments can be combined in any way as long as there is no conflict between the technical solutions. It is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

Claims (10)

1. The utility model provides a developments electrocardio data monitored control system based on thing networking which characterized in that includes following module:
the system comprises a mobile electrocardio acquisition terminal (1), an electrocardio signal receiving module (2), an Internet of things wireless connection communication module (3), a main control module (4) and a dynamic electrocardio data monitoring module (5) which is in communication connection with the main control module (4);
the mobile electrocardio acquisition terminal (1) comprises an electrocardio electrode (11), a data processing module (12) and an electrocardio acquisition terminal signal transmitting module (13);
the mobile electrocardio acquisition terminal (1) is connected with the body of a monitored user to acquire electrocardiosignals of the user; the electrocardiosignal receiving module (2) receives electrocardiosignal data collected by the mobile electrocardiosignal collecting terminal (1) and transmits the electrocardiosignal data to the Internet of things wireless connection communication module (3); the internet of things wireless connection communication module (3) transmits the received electrocardiosignal data to the main control module (4), and the main control module (4) transmits the electrocardiosignal data to the dynamic electrocardiosignal data monitoring module (5) for data statistical analysis and storage;
the internet of things wireless connection communication module (3) executes an energy-saving transmission power control algorithm to adaptively and dynamically control the transmission power of the wireless connection gateway, so that the power consumption of wireless connection communication is saved.
2. The internet of things-based dynamic electrocardiogram data monitoring system according to claim 1, wherein the energy-saving transmission power control algorithm comprises the following steps:
m1: the signal strength information data received by the received signal strength indicator in the wireless connection communication module (3) of the Internet of things, and the transmission power value delta P at the moment iiLatest signal strength information data value RlatestCalculating an estimated average of the signal strength information data
Figure FDA0002904425190000011
M2: the wireless connection communication module (3) acquires the latest signal strength information data value RlatestAnd the estimated mean value
Figure FDA0002904425190000021
Comparing, constructing an updating algorithm model to update the estimated average value of the signal intensity information data
Figure FDA0002904425190000022
And obtaining an estimated average value based on said updating
Figure FDA0002904425190000023
Distinguishing a good channel and a bad channel;
m3: the wireless connection communication module (3) of the Internet of things is updated
Figure FDA0002904425190000024
With known target signal strength RtargetComparing, constructing a transmission power level delta P calculation model, and calculating the transmission power level of the channel where the signal is located;
m4: calculating an estimated average of the updated signal strength information data
Figure FDA0002904425190000025
A constant low threshold TRL and a variable high threshold TRH respectively associated with the power required for said signal transmissionvarAfter comparison, a self-adaptive dynamic control wireless connection gateway transmission power comparison condition is established, and the wireless transmission module is self-adaptively adjusted to have stable transmission power and low power consumption when being connected with the gateway.
3. The internet of things-based dynamic electrocardiographic data monitoring system according to claim 2, wherein the updating algorithm model of the M2 step is as follows:
if it is
Figure FDA0002904425190000026
Then
Figure FDA0002904425190000027
If it is
Figure FDA0002904425190000028
Then
Figure FDA0002904425190000029
Wherein said alpha is1For the good channel average weight, the alpha2And averaging the weights of the bad channels.
4. The internet of things-based dynamic electrocardiographic data monitoring system according to claim 2, wherein the transmission power level Δ P calculation model in the M3 step is as follows:
Figure FDA00029044251900000210
Figure FDA0002904425190000031
wherein N is the number of signal strength information data transmitted by the wireless connection communication module.
5. The internet of things-based dynamic electrocardiographic data monitoring system according to claim 2, wherein the adaptive dynamic control wireless connection gateway transmission power comparison condition in the step M4 is as follows:
Figure FDA0002904425190000032
6. the internet of things-based dynamic electrocardiogram data monitoring system according to claim 2, wherein the known signal strength R in M3 step istargetThe value of (d) was-85 dBm.
7. The internet of things-based dynamic electrocardiographic data monitoring system according to claim 2, wherein the constant low threshold TRL in the M4 step is-88 dBm.
8. The internet of things-based dynamic electrocardiogram data monitoring system according to claim 2, wherein the TRH in the M4 stepvarThe calculation formula of (2) is as follows:
TRHvar＝TRL+σ；
Figure FDA0002904425190000033
wherein, R isiA signal strength information data value at time i, where i is 1, 2. Wherein σ and N are a standard deviation and a number of signal strength information data transmitted by the wireless connection communication module, respectively.
9. The dynamic electrocardiogram data monitoring system based on the internet of things of claim 1, wherein the data processing module (12) in the mobile electrocardiogram collection terminal (1) comprises a front-end amplification unit (121), a signal filtering unit (122), a rear-end amplification unit (123), a wave trap (124) and an a/D converter (125).
10. The internet of things-based dynamic electrocardiogram data monitoring system according to claim 1, wherein the dynamic electrocardiogram data monitoring module (5) comprises an electrocardiogram signal database (51), a diagnosis and analysis module (52) connected with the electrocardiogram signal database (51), and a case query module (53).
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1282567A (en) * 2000-07-24 2001-02-07 广东省科学院自动化工程研制中心 Internet-based remote network system for cardioelectric monitor
CN104219742A (en) * 2013-06-03 2014-12-17 普天信息技术研究院有限公司 Power control method
CN104586381A (en) * 2015-01-19 2015-05-06 成都信息工程学院 Electrocardiograph monitoring system based on Internet of Things
CN105659670A (en) * 2015-12-29 2016-06-08 中国科学院深圳先进技术研究院 Wireless body area network power control method and system, and transmitting node

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1282567A (en) * 2000-07-24 2001-02-07 广东省科学院自动化工程研制中心 Internet-based remote network system for cardioelectric monitor
CN104219742A (en) * 2013-06-03 2014-12-17 普天信息技术研究院有限公司 Power control method
CN104586381A (en) * 2015-01-19 2015-05-06 成都信息工程学院 Electrocardiograph monitoring system based on Internet of Things
CN105659670A (en) * 2015-12-29 2016-06-08 中国科学院深圳先进技术研究院 Wireless body area network power control method and system, and transmitting node

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
ALI HASSAN SODHRO等: ""Energy-efficient adaptive transmission power control for wireless body area networks"", 《IET COMMUNICATIONS》 *
ALI HASSAN SODHRO等: ""Power Control Algorithms for Media Transmission in Remote Healthcare Systems"", 《IEEE ACCESS》 *
尚维来等: ""基于阈值预测的Linux系统休眠态低功耗策略"", 《万方数据库》 *

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