CN113825219A - Human body data collecting method and device - Google Patents

Human body data collecting method and device Download PDF

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Publication number
CN113825219A
CN113825219A CN202111325058.XA CN202111325058A CN113825219A CN 113825219 A CN113825219 A CN 113825219A CN 202111325058 A CN202111325058 A CN 202111325058A CN 113825219 A CN113825219 A CN 113825219A
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data
cluster head
communication
base station
communication base
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CN113825219B (en
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王炳坤
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De Rucci Healthy Sleep Co Ltd
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De Rucci Healthy Sleep Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H04W52/0225—Power saving arrangements in terminal devices using monitoring of external events, e.g. the presence of a signal
    • H04W52/0248—Power saving arrangements in terminal devices using monitoring of external events, e.g. the presence of a signal dependent on the time of the day, e.g. according to expected transmission activity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • 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

Abstract

The invention provides a human body data collecting method and a device, wherein the method comprises the following steps: s1, acquiring state data of the human body through the intelligent wearable device; s2, transmitting the state data to the cluster head node through the member node; s3, transmitting the state data to a communication base station through the cluster head node; s4, transmitting the state data to a data processing center through a communication base station; the communication base station is further used for clustering the wireless sensor nodes by adopting a self-adaptive time interval, and dividing the wireless sensor nodes into cluster head nodes and common nodes. The device is used for realizing the method. The invention realizes the self-adaptive change between the adjacent two clustering processes and the data forwarding amount, thereby balancing the energy consumption of the wireless sensor nodes and prolonging the average service life of all the wireless sensor nodes.

Description

Human body data collecting method and device
Technical Field
The invention relates to the field of data collection, in particular to a human body data collection method and device.
Background
Along with the development of the internet of things technology, intelligent household equipment is also more and more diversified. The smart home is connected with home equipment and a network through the Internet of things technology, so that people can conveniently control various smart homes with the Internet of things function on a unified control terminal, such as a mobile phone. However, the smart home is still set manually, and is not intelligent enough, so that the smart home capable of adaptively changing according to the mood is manufactured. The existing mood judgment of human body is generally to obtain parameters of human body through a data acquisition device worn on human body, such as a bracelet, and then to transmit the parameters of human body to a data processing center through a wireless sensor network to judge the mood state of human body. In the existing wireless sensor network, the selection of the cluster head node is generally carried out in a fixed time period, and the setting mode enables the time interval between two adjacent clusters not to be changed in a self-adaptive mode according to the actual situation, so that the average service life of the wireless sensor node is shortened.
Disclosure of Invention
In view of the above problems, the present invention provides a method and an apparatus for collecting human body data.
In one aspect, the present invention provides a method for collecting human body data, including:
s1, acquiring state data of a human body through intelligent wearable equipment, and transmitting the state data to member nodes;
s2, transmitting the state data to the cluster head node through the member node;
s3, transmitting the state data to a communication base station through the cluster head node;
s4, transmitting the state data to a data processing center through a communication base station;
the communication base station is also used for clustering the wireless sensor nodes by adopting a self-adaptive time interval, and dividing the wireless sensor nodes into cluster head nodes and common nodes;
the time interval is calculated by:
when the q-th clustering processing is carried out, predicting the data forwarding amount of the communication base station in the q-th collection period by the following method:
Figure 947021DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 115965DEST_PATH_IMAGE002
represents the predicted data forwarding amount of the communication base station in the q-th collection period,
Figure 29039DEST_PATH_IMAGE003
represents a preset first scale factor and a preset second scale factor,
Figure 156395DEST_PATH_IMAGE004
，
Figure 56218DEST_PATH_IMAGE005
represents the data forwarding amount of the communication base station in the q-1 collecting period,
Figure 560011DEST_PATH_IMAGE006
represents the predicted data forwarding amount of the communication base station in the q-1 th collection period,
Figure 799363DEST_PATH_IMAGE007
presentation pair
Figure 159937DEST_PATH_IMAGE006
The coefficient for the correction is made to be,
Figure 484739DEST_PATH_IMAGE008
indicating the data forwarding amount of the communication base station in the q-2 collection period,
Figure 792224DEST_PATH_IMAGE009
represents a preset second scaling factor that is,
Figure 682819DEST_PATH_IMAGE010
；
if it is
Figure 417557DEST_PATH_IMAGE011
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 964076DEST_PATH_IMAGE012
if it is
Figure 137568DEST_PATH_IMAGE013
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 77144DEST_PATH_IMAGE014
if it is
Figure 45100DEST_PATH_IMAGE015
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 78915DEST_PATH_IMAGE016
wherein the content of the first and second substances,
Figure 728202DEST_PATH_IMAGE017
and
Figure 593390DEST_PATH_IMAGE018
respectively represent a preset first judgment threshold and a second judgment threshold,
Figure 669930DEST_PATH_IMAGE019
indicates the time interval between the (q + 1) th clustering process and the (q) th clustering process,
Figure 191042DEST_PATH_IMAGE020
indicates the time interval between the q-th clustering process and the q-1 st clustering process,
Figure 644020DEST_PATH_IMAGE021
which represents a preset control coefficient of the control unit,
Figure 363714DEST_PATH_IMAGE022
indicating a preset length of unit time.
Preferably, the intelligent wearable device comprises an intelligent watch, an intelligent bracelet and intelligent glasses.
Preferably, the status data includes body temperature, heart rate, blood pressure, respiratory rate, blood oxygen content.
Preferably, the communication mode between the member node and the intelligent wearable device includes one or more of bluetooth communication, ZigBee communication, UWB communication, and RFID communication.
Preferably, the transmitting the status data to the cluster head node includes:
record member node as
Figure 611156DEST_PATH_IMAGE023
The member node
Figure 681880DEST_PATH_IMAGE023
Determining a final cluster head node by:
storing all cluster head nodes in the communication range of the member nodes into a set
Figure 672970DEST_PATH_IMAGE024
Performing the following steps;
respectively calculate
Figure 447503DEST_PATH_IMAGE025
Communication loss coefficient of each cluster head node:
Figure 865846DEST_PATH_IMAGE026
wherein the content of the first and second substances,
Figure 158287DEST_PATH_IMAGE027
to represent
Figure 953068DEST_PATH_IMAGE025
Cluster head node in
Figure 585037DEST_PATH_IMAGE028
And member node
Figure 236599DEST_PATH_IMAGE029
The coefficient of loss of communication between the two,
Figure 219598DEST_PATH_IMAGE030
which represents a preset weight coefficient for the weight of the image,
Figure 552490DEST_PATH_IMAGE031
，
Figure 101283DEST_PATH_IMAGE032
to represent
Figure 127008DEST_PATH_IMAGE028
And
Figure 331725DEST_PATH_IMAGE033
the communication distance between the two or more communication devices,
Figure 530625DEST_PATH_IMAGE034
to represent
Figure 874537DEST_PATH_IMAGE035
Cluster head node in and
Figure 133480DEST_PATH_IMAGE023
the average communication distance between them,
Figure 294334DEST_PATH_IMAGE036
to represent
Figure 500188DEST_PATH_IMAGE028
The total number of other cluster head nodes included in the communication range of (1),
Figure 23573DEST_PATH_IMAGE037
to represent
Figure 391100DEST_PATH_IMAGE038
The average value of the total number of other cluster head nodes included in the communication range of the cluster head node in (b),
Figure 304830DEST_PATH_IMAGE039
to represent
Figure 845532DEST_PATH_IMAGE028
And the average number of communication hops between communicating base stations,
Figure 426686DEST_PATH_IMAGE040
to represent
Figure 699536DEST_PATH_IMAGE025
Average value of average communication hop count between the cluster head node and the communication base station;
selecting
Figure 428458DEST_PATH_IMAGE035
And acquiring the cluster head node with the minimum communication loss coefficient as a final cluster head node, and transmitting the state data to the final cluster head node.
Preferably, the data processing center comprises a data storage module and a data analysis module;
the data storage module is used for storing the state data sent by the communication base station;
the data analysis is used for determining the mood state of the human body according to the state data, wherein the mood state comprises happiness, injury, anger, fear and calmness.
On the other hand, the invention provides a human body data collecting device, which comprises wearable equipment, member nodes, cluster head nodes, a communication base station and a data processing center, wherein the wearable equipment is used for collecting human body data;
the wearable device is used for acquiring state data of a human body and transmitting the state data to member nodes;
the member node is used for transmitting the state data to the cluster head node;
the cluster head node is used for transmitting the state data to a communication base station;
the wearable equipment is used for transmitting the state data to a data processing center;
the communication base station is also used for clustering the wireless sensor nodes by adopting a self-adaptive time interval, and dividing the wireless sensor nodes into cluster head nodes and common nodes;
the time interval is calculated by:
when the q-th clustering processing is carried out, predicting the data forwarding amount of the communication base station in the q-th collection period by the following method:
Figure 444955DEST_PATH_IMAGE041
wherein the content of the first and second substances,
Figure 942933DEST_PATH_IMAGE042
represents the predicted data forwarding amount of the communication base station in the q-th collection period,
Figure 383754DEST_PATH_IMAGE003
represents a preset first scale factor and a preset second scale factor,
Figure 537654DEST_PATH_IMAGE004
，
Figure 357843DEST_PATH_IMAGE005
represents the data forwarding amount of the communication base station in the q-1 collecting period,
Figure 648010DEST_PATH_IMAGE006
represents the predicted data forwarding amount of the communication base station in the q-1 th collection period,
Figure 590558DEST_PATH_IMAGE007
presentation pair
Figure 966176DEST_PATH_IMAGE006
The coefficient for the correction is made to be,
Figure 386793DEST_PATH_IMAGE008
data transfer representing communication base station in q-2 collection periodThe hair-sending quantity is measured,
Figure 797046DEST_PATH_IMAGE009
represents a preset second scaling factor that is,
Figure 582599DEST_PATH_IMAGE010
；
if it is
Figure 507830DEST_PATH_IMAGE011
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 669821DEST_PATH_IMAGE012
if it is
Figure 761728DEST_PATH_IMAGE043
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 983762DEST_PATH_IMAGE044
if it is
Figure 396289DEST_PATH_IMAGE045
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 96392DEST_PATH_IMAGE046
wherein the content of the first and second substances,
Figure 215658DEST_PATH_IMAGE017
and
Figure 608593DEST_PATH_IMAGE018
respectively representing preset first judgmentsA cutoff threshold value and a second cutoff threshold value,
Figure 508416DEST_PATH_IMAGE047
indicates the time interval between the (q + 1) th clustering process and the (q) th clustering process,
Figure 12209DEST_PATH_IMAGE048
indicates the time interval between the q-th clustering process and the q-1 st clustering process,
Figure 48299DEST_PATH_IMAGE021
which represents a preset control coefficient of the control unit,
Figure 612135DEST_PATH_IMAGE049
indicating a preset length of unit time.
According to the invention, through predicting the data forwarding amount of the communication base station in the next collection period, the self-adaptive adjustment of the time interval between two adjacent clustering processes is realized, and the average service life of the wireless sensor node is effectively prolonged. When the predicted data forwarding amount of the communication base station in the next collection period is significantly larger than the actual data forwarding amount of the communication base station in the previous collection period, the time interval between the next clustering processing and the current clustering processing can be shortened; otherwise, the time interval between the next clustering processing and the current clustering processing is prolonged, and the self-adaptive change between the two adjacent clustering processing and the data forwarding amount is realized, so that the energy consumption of the wireless sensor nodes can be balanced, and the average service life of all the wireless sensor nodes is prolonged.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a diagram of an exemplary embodiment of a method for collecting human body data according to the present invention.
FIG. 2 is a diagram of an exemplary embodiment of a body data gathering device according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in FIG. 1, the present invention provides a method and apparatus for collecting human body data.
In one aspect, the present invention provides a method for collecting human body data, including:
s1, acquiring state data of a human body through intelligent wearable equipment, and transmitting the state data to member nodes;
s2, transmitting the state data to the cluster head node through the member node;
s3, transmitting the state data to a communication base station through the cluster head node;
s4, transmitting the state data to a data processing center through a communication base station;
the communication base station is also used for clustering the wireless sensor nodes by adopting a self-adaptive time interval, and dividing the wireless sensor nodes into cluster head nodes and common nodes;
the time interval is calculated by:
when the q-th clustering processing is carried out, predicting the data forwarding amount of the communication base station in the q-th collection period by the following method:
Figure 936937DEST_PATH_IMAGE041
wherein the content of the first and second substances,
Figure 41159DEST_PATH_IMAGE002
represents the predicted data forwarding amount of the communication base station in the q-th collection period,
Figure 132088DEST_PATH_IMAGE003
represents a preset first scale factor and a preset second scale factor,
Figure 866825DEST_PATH_IMAGE004
，
Figure 475661DEST_PATH_IMAGE005
represents the data forwarding amount of the communication base station in the q-1 collecting period,
Figure 790099DEST_PATH_IMAGE006
represents the predicted data forwarding amount of the communication base station in the q-1 th collection period,
Figure 535201DEST_PATH_IMAGE007
presentation pair
Figure 503157DEST_PATH_IMAGE006
The coefficient for the correction is made to be,
Figure 536972DEST_PATH_IMAGE008
indicating the data forwarding amount of the communication base station in the q-2 collection period,
Figure 920680DEST_PATH_IMAGE009
represents a preset second scaling factor that is,
Figure 51447DEST_PATH_IMAGE010
；
if it is
Figure 127988DEST_PATH_IMAGE011
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 649099DEST_PATH_IMAGE050
if it is
Figure 898815DEST_PATH_IMAGE043
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 824701DEST_PATH_IMAGE044
if it is
Figure 72143DEST_PATH_IMAGE045
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 142867DEST_PATH_IMAGE046
wherein the content of the first and second substances,
Figure 133957DEST_PATH_IMAGE017
and
Figure 911420DEST_PATH_IMAGE018
respectively represent a preset first judgment threshold and a second judgment threshold,
Figure 392080DEST_PATH_IMAGE019
indicates the time interval between the (q + 1) th clustering process and the (q) th clustering process,
Figure 622204DEST_PATH_IMAGE048
indicates the time interval between the q-th clustering process and the q-1 st clustering process,
Figure 479301DEST_PATH_IMAGE021
which represents a preset control coefficient of the control unit,
Figure 111271DEST_PATH_IMAGE049
indicating a preset length of unit time.
According to the invention, through predicting the data forwarding amount of the communication base station in the next collection period, the self-adaptive adjustment of the time interval between two adjacent clustering processes is realized, and the average service life of the wireless sensor node is effectively prolonged. When the predicted data forwarding amount of the communication base station in the next collection period is significantly larger than the actual data forwarding amount of the communication base station in the previous collection period, the time interval between the next clustering processing and the current clustering processing can be shortened; otherwise, the time interval between the next clustering processing and the current clustering processing is prolonged, and the self-adaptive change between the two adjacent clustering processing and the data forwarding amount is realized, so that the energy consumption of the wireless sensor nodes can be balanced, and the average service life of all the wireless sensor nodes is prolonged.
In another optional embodiment, the data forwarding amount of the communication base station in the next collection period may also be predicted through a markov chain, so as to obtain the predicted data forwarding amount of the communication base station in the next collection period.
Preferably, the intelligent wearable device comprises an intelligent watch, an intelligent bracelet and intelligent glasses.
It should be noted that, here, the wearable device is not limited, but all devices with a human body data monitoring function can be used to implement the technical solution of the present invention.
Preferably, the status data includes body temperature, heart rate, blood pressure, respiratory rate, blood oxygen content.
Preferably, the communication mode between the member node and the intelligent wearable device includes one or more of bluetooth communication, ZigBee communication, UWB communication, and RFID communication.
In a relatively large space such as a villa, due to the movement of a human body, the communication distance of the wearable device is limited, so that the wearable device can be distributed in the activity space of the human body by arranging the wireless sensor nodes to form a wireless sensor network, and the forwarding of the state data of the human body, which is obtained by the wearable device, of the wearable device is completed.
Preferably, the transmitting the status data to the cluster head node includes:
record member node as
Figure 762832DEST_PATH_IMAGE023
The member node
Figure 745832DEST_PATH_IMAGE023
Determining a final cluster head node by:
storing all cluster head nodes in the communication range of the member nodes into a set
Figure 141041DEST_PATH_IMAGE051
Performing the following steps;
respectively calculate
Figure 627517DEST_PATH_IMAGE052
Communication loss coefficient of each cluster head node:
Figure 715559DEST_PATH_IMAGE053
wherein the content of the first and second substances,
Figure 917346DEST_PATH_IMAGE054
to represent
Figure 850667DEST_PATH_IMAGE052
Cluster head node in
Figure 457228DEST_PATH_IMAGE028
And member node
Figure 716171DEST_PATH_IMAGE029
The coefficient of loss of communication between the two,
Figure 142605DEST_PATH_IMAGE030
which represents a preset weight coefficient for the weight of the image,
Figure 145196DEST_PATH_IMAGE055
，
Figure 606264DEST_PATH_IMAGE032
to represent
Figure 770529DEST_PATH_IMAGE028
And
Figure 949838DEST_PATH_IMAGE033
the communication distance between the two or more communication devices,
Figure 490541DEST_PATH_IMAGE056
to represent
Figure 806115DEST_PATH_IMAGE057
Cluster head node in and
Figure 406861DEST_PATH_IMAGE023
the average communication distance between them,
Figure 807886DEST_PATH_IMAGE058
to represent
Figure 152280DEST_PATH_IMAGE028
The total number of other cluster head nodes included in the communication range of (1),
Figure 587941DEST_PATH_IMAGE059
to represent
Figure 94008DEST_PATH_IMAGE060
The average value of the total number of other cluster head nodes included in the communication range of the cluster head node in (b),
Figure 239120DEST_PATH_IMAGE061
to represent
Figure 121625DEST_PATH_IMAGE028
And the average number of communication hops between communicating base stations,
Figure 411792DEST_PATH_IMAGE040
to represent
Figure 88761DEST_PATH_IMAGE052
Average value of average communication hop count between the cluster head node and the communication base station;
selecting
Figure 464379DEST_PATH_IMAGE057
And acquiring the cluster head node with the minimum communication loss coefficient as a final cluster head node, and transmitting the state data to the final cluster head node.
In the prior art, the member node generally communicates with the cluster head node closest to the member node directly, but the state change of the cluster head node is obviously not considered in the communication mode, and the cluster head node closest to the member node is not necessarily the optimal communication target, so that the cluster head node may be farthest from the communication base station, which increases the power consumption in the data transmission process and is not beneficial to improving the average service life of the wireless sensor node. In the above embodiment of the present invention, by considering the communication distance, the total number of other cluster head nodes included in the communication range, and the average number of communication hops between the communication base stations, the communication loss coefficient is obtained through comprehensive calculation, and the larger the communication loss coefficient is, the larger the power loss representing the forwarding process of the state data is, so that the cluster head node that may cause the power loss in the forwarding process of the state data to be small can be selected as the final cluster head node, thereby effectively reducing the power loss in the forwarding process of the state data. The average service life of the wireless sensor node is prolonged.
Preferably, the communication between the member node and the final cluster head node is performed as follows:
calculating a back-off time reference value by:
Figure 150575DEST_PATH_IMAGE062
wherein the content of the first and second substances,
Figure 295249DEST_PATH_IMAGE063
a value representing a reference value of the back-off time,
Figure 408698DEST_PATH_IMAGE064
which indicates a preset length of time for which,
Figure 271612DEST_PATH_IMAGE065
represents a coefficient of a preset constant number of times,
Figure 495920DEST_PATH_IMAGE066
representing the total number of times the cluster head node communicates with the member nodes within a preset time period Tim,
Figure 760679DEST_PATH_IMAGE067
which represents a preset control coefficient of the control unit,
Figure 45030DEST_PATH_IMAGE068
indicating the number of all member nodes having a distance from the cluster head node less than R,
Figure 129661DEST_PATH_IMAGE069
the distance between the representation and the cluster head node is less than
Figure 157660DEST_PATH_IMAGE070
The number of all member nodes of (a),
Figure 276926DEST_PATH_IMAGE071
，
obtaining a communication yielding coefficient:
Figure 732178DEST_PATH_IMAGE072
wherein the content of the first and second substances,
Figure 566754DEST_PATH_IMAGE073
the communication-back-off coefficient is represented,
Figure 132864DEST_PATH_IMAGE074
g represents data between the member node and the cluster head node within a preset time period StThe packet loss rate during transmission, t, represents the number of times of packet loss of the member node within the preset time period St;
calculating the waiting coefficient of the member node:
Figure 106637DEST_PATH_IMAGE075
calculating the back-off latency by
Figure 732790DEST_PATH_IMAGE076
：
Figure 792013DEST_PATH_IMAGE077
If the member node detects that other communication connection exists in the final cluster head node when establishing communication connection to the final cluster head node, waiting for the member node
Figure 161814DEST_PATH_IMAGE076
Then establishing communication connection with the final cluster head node;
and the final cluster head node periodically calculates the back-off time reference value and broadcasts the back-off time reference value to the neighbor nodes.
In the above embodiment, the deferral waiting time is calculated by the size between the waiting coefficient and the communication deferral coefficient, and the deferral waiting time is not a fixed value but can be adaptively changed according to the current network state, so that the communication delay in the forwarding process of the state data can be effectively shortened, and the real-time performance of the state data reaching the data processing center can be improved. The human body state can be monitored in time. Meanwhile, the back-off time reference value is also dynamically calculated by the cluster head node, so that the adaptability of the embodiment of the invention to the network state change can be effectively improved.
Preferably, the data processing center comprises a data storage module and a data analysis module;
the data storage module is used for storing the state data sent by the communication base station;
the data analysis is used for determining the mood state of the human body according to the state data, wherein the mood state comprises happiness, injury, anger, fear and calmness.
Preferably, the data processing center comprises one or more of a computer, a tablet, and a smart phone.
On the other hand, the invention provides a human body data collecting device, which comprises wearable equipment, member nodes, cluster head nodes, a communication base station and a data processing center, wherein the wearable equipment is used for collecting human body data;
the wearable device is used for acquiring state data of a human body and transmitting the state data to member nodes;
the member node is used for transmitting the state data to the cluster head node;
the cluster head node is used for transmitting the state data to a communication base station;
the wearable equipment is used for transmitting the state data to a data processing center;
the communication base station is also used for clustering the wireless sensor nodes by adopting a self-adaptive time interval, and dividing the wireless sensor nodes into cluster head nodes and common nodes;
the time interval is calculated by:
when the q-th clustering processing is carried out, predicting the data forwarding amount of the communication base station in the q-th collection period by the following method:
Figure 255672DEST_PATH_IMAGE078
wherein the content of the first and second substances,
Figure 52727DEST_PATH_IMAGE002
represents the predicted data forwarding amount of the communication base station in the q-th collection period,
Figure 599246DEST_PATH_IMAGE003
represents a preset first scale factor and a preset second scale factor,
Figure 507159DEST_PATH_IMAGE004
，
Figure 455524DEST_PATH_IMAGE079
represents the data forwarding amount of the communication base station in the q-1 collecting period,
Figure 361163DEST_PATH_IMAGE006
represents the predicted data forwarding amount of the communication base station in the q-1 th collection period,
Figure 457295DEST_PATH_IMAGE080
presentation pair
Figure 106582DEST_PATH_IMAGE006
The coefficient for the correction is made to be,
Figure 177962DEST_PATH_IMAGE008
indicating the data forwarding amount of the communication base station in the q-2 collection period,
Figure 316819DEST_PATH_IMAGE009
represents a preset second scaling factor that is,
Figure 572351DEST_PATH_IMAGE010
；
if it is
Figure 87646DEST_PATH_IMAGE011
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 10603DEST_PATH_IMAGE081
if it is
Figure 320361DEST_PATH_IMAGE082
Then, the time between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as followsSeparating:
Figure 63189DEST_PATH_IMAGE083
if it is
Figure 54279DEST_PATH_IMAGE045
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 894059DEST_PATH_IMAGE046
wherein the content of the first and second substances,
Figure 374719DEST_PATH_IMAGE017
and
Figure 870422DEST_PATH_IMAGE018
respectively represent a preset first judgment threshold and a second judgment threshold,
Figure 461941DEST_PATH_IMAGE019
indicates the time interval between the (q + 1) th clustering process and the (q) th clustering process,
Figure 359490DEST_PATH_IMAGE020
indicates the time interval between the q-th clustering process and the q-1 st clustering process,
Figure 948734DEST_PATH_IMAGE021
which represents a preset control coefficient of the control unit,
Figure 728471DEST_PATH_IMAGE022
indicating a preset length of unit time.
It should be noted that the apparatus is used for implementing the functions of the method, and each module in the apparatus corresponds to the steps of the method,
and to enable implementation of different embodiments of the method described above, reference is made in detail to the description of the method described above, which is not described in detail here.
While embodiments of the invention have been shown and described, it will be understood by those skilled in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (7)

1. A method for collecting human body data, comprising:
s1, acquiring state data of a human body through intelligent wearable equipment, and transmitting the state data to member nodes;
s2, transmitting the state data to the cluster head node through the member node;
s3, transmitting the state data to a communication base station through the cluster head node;
s4, transmitting the state data to a data processing center through a communication base station;
the communication base station is also used for clustering the wireless sensor nodes by adopting a self-adaptive time interval, and dividing the wireless sensor nodes into cluster head nodes and common nodes;
the time interval is calculated by:
when the q-th clustering processing is carried out, predicting the data forwarding amount of the communication base station in the q-th collection period by the following method:
Figure 663052DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 349249DEST_PATH_IMAGE002
represents the predicted data forwarding amount of the communication base station in the q-th collection period,
Figure 493922DEST_PATH_IMAGE003
represents a preset first ratioFor example, the coefficients of the coefficients are,
Figure 607372DEST_PATH_IMAGE004
，
Figure 470285DEST_PATH_IMAGE005
represents the data forwarding amount of the communication base station in the q-1 collecting period,
Figure 694593DEST_PATH_IMAGE006
represents the predicted data forwarding amount of the communication base station in the q-1 th collection period,
Figure 959353DEST_PATH_IMAGE007
presentation pair
Figure 915807DEST_PATH_IMAGE006
The coefficient for the correction is made to be,
Figure 328334DEST_PATH_IMAGE008
indicating the data forwarding amount of the communication base station in the q-2 collection period,
Figure 294016DEST_PATH_IMAGE009
represents a preset second scaling factor that is,
Figure 475599DEST_PATH_IMAGE010
；
if it is
Figure 868534DEST_PATH_IMAGE011
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 502778DEST_PATH_IMAGE012
if it is
Figure 997782DEST_PATH_IMAGE013
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 299451DEST_PATH_IMAGE014
if it is
Figure 863287DEST_PATH_IMAGE015
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 984827DEST_PATH_IMAGE016
wherein the content of the first and second substances,
Figure 292311DEST_PATH_IMAGE017
and
Figure 386169DEST_PATH_IMAGE018
respectively represent a preset first judgment threshold and a second judgment threshold,
Figure 183224DEST_PATH_IMAGE019
indicates the time interval between the (q + 1) th clustering process and the (q) th clustering process,
Figure 526481DEST_PATH_IMAGE020
indicates the time interval between the q-th clustering process and the q-1 st clustering process,
Figure 434394DEST_PATH_IMAGE021
which represents a preset control coefficient of the control unit,
Figure 586021DEST_PATH_IMAGE022
indicating a preset length of unit time.
2. The method of claim 1, wherein the smart wearable device comprises a smart watch, a smart bracelet, and smart glasses.
3. The method of claim 1, wherein the status data includes body temperature, heart rate, blood pressure, respiratory rate, blood oxygen content.
4. The method for collecting body data according to claim 1, wherein the communication mode between the member node and the smart wearable device comprises one or more of bluetooth communication, ZigBee communication, UWB communication and RFID communication.
5. The method for collecting body data according to claim 1, wherein said transmitting said state data to cluster head nodes comprises:
record member node as
Figure 553977DEST_PATH_IMAGE023
The member node
Figure 587792DEST_PATH_IMAGE024
Determining a final cluster head node by:
storing all cluster head nodes in the communication range of the member nodes into a set
Figure 299396DEST_PATH_IMAGE025
Performing the following steps;
respectively calculate
Figure 102267DEST_PATH_IMAGE026
Communication loss coefficient of each cluster head node:
Figure 175877DEST_PATH_IMAGE027
wherein the content of the first and second substances,
Figure 759306DEST_PATH_IMAGE028
to represent
Figure 212284DEST_PATH_IMAGE029
Cluster head node in
Figure 931978DEST_PATH_IMAGE030
And member node
Figure 179420DEST_PATH_IMAGE031
The coefficient of loss of communication between the two,
Figure 250144DEST_PATH_IMAGE032
which represents a preset weight coefficient for the weight of the image,
Figure 241234DEST_PATH_IMAGE033
，
Figure 18697DEST_PATH_IMAGE034
to represent
Figure 499357DEST_PATH_IMAGE035
And
Figure 995060DEST_PATH_IMAGE036
the communication distance between the two or more communication devices,
Figure 586578DEST_PATH_IMAGE037
to represent
Figure 218548DEST_PATH_IMAGE038
Cluster head node in and
Figure 135688DEST_PATH_IMAGE024
the average communication distance between them,
Figure 853109DEST_PATH_IMAGE039
to represent
Figure 248318DEST_PATH_IMAGE040
The total number of other cluster head nodes included in the communication range of (1),
Figure 3303DEST_PATH_IMAGE041
to represent
Figure 825765DEST_PATH_IMAGE042
The average value of the total number of other cluster head nodes included in the communication range of the cluster head node in (b),
Figure 30482DEST_PATH_IMAGE043
to represent
Figure 229382DEST_PATH_IMAGE044
And the average number of communication hops between communicating base stations,
Figure 835944DEST_PATH_IMAGE045
to represent
Figure 829308DEST_PATH_IMAGE029
Average value of average communication hop count between the cluster head node and the communication base station;
selecting
Figure 521320DEST_PATH_IMAGE038
And acquiring the cluster head node with the minimum communication loss coefficient as a final cluster head node, and transmitting the state data to the final cluster head node.
6. The human data gathering method as recited in claim 1, wherein the data processing center comprises a data storage module and a data analysis module;
the data storage module is used for storing the state data sent by the communication base station;
the data analysis is used for determining the mood state of the human body according to the state data, wherein the mood state comprises happiness, injury, anger, fear and calmness.
7. A human body data collecting device is characterized by comprising wearable equipment, member nodes, cluster head nodes, a communication base station and a data processing center;
the wearable device is used for acquiring state data of a human body and transmitting the state data to member nodes;
the member node is used for transmitting the state data to the cluster head node;
the cluster head node is used for transmitting the state data to a communication base station;
the wearable equipment is used for transmitting the state data to a data processing center;
the communication base station is also used for clustering the wireless sensor nodes by adopting a self-adaptive time interval, and dividing the wireless sensor nodes into cluster head nodes and common nodes;
the time interval is calculated by:
when the q-th clustering processing is carried out, predicting the data forwarding amount of the communication base station in the q-th collection period by the following method:
Figure 258332DEST_PATH_IMAGE046
wherein the content of the first and second substances,
Figure 719400DEST_PATH_IMAGE002
represents the predicted data forwarding amount of the communication base station in the q-th collection period,
Figure 149245DEST_PATH_IMAGE003
represents a preset first scale factor and a preset second scale factor,
Figure 328553DEST_PATH_IMAGE004
，
Figure 541360DEST_PATH_IMAGE047
represents the data forwarding amount of the communication base station in the q-1 collecting period,
Figure 184831DEST_PATH_IMAGE006
represents the predicted data forwarding amount of the communication base station in the q-1 th collection period,
Figure 457680DEST_PATH_IMAGE048
presentation pair
Figure 186602DEST_PATH_IMAGE006
The coefficient for the correction is made to be,
Figure 200170DEST_PATH_IMAGE008
indicating the data forwarding amount of the communication base station in the q-2 collection period,
Figure 698147DEST_PATH_IMAGE009
represents a preset second scaling factor that is,
Figure 407477DEST_PATH_IMAGE010
；
if it is
Figure 358116DEST_PATH_IMAGE049
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 178304DEST_PATH_IMAGE050
if it is
Figure 530788DEST_PATH_IMAGE051
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 411019DEST_PATH_IMAGE052
if it is
Figure 848954DEST_PATH_IMAGE053
Then, the time interval between the clustering processing performed q +1 th time and the clustering processing performed q th time is calculated as follows:
Figure 207254DEST_PATH_IMAGE016
wherein the content of the first and second substances,
Figure 617507DEST_PATH_IMAGE017
and
Figure 730956DEST_PATH_IMAGE018
respectively represent a preset first judgment threshold and a second judgment threshold,
Figure 593870DEST_PATH_IMAGE054
indicates the time interval between the (q + 1) th clustering process and the (q) th clustering process,
Figure 614916DEST_PATH_IMAGE020
indicates the time interval between the q-th clustering process and the q-1 st clustering process,
Figure 817358DEST_PATH_IMAGE021
which represents a preset control coefficient of the control unit,
Figure 101709DEST_PATH_IMAGE022
indicating a preset length of unit time.
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