CN114707803A - Internet of things data fusion method and application thereof in intelligent power - Google Patents
Internet of things data fusion method and application thereof in intelligent power Download PDFInfo
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Abstract
The invention relates to the technical field of data fusion of the Internet of things, and discloses a data fusion method of the Internet of things, which comprises the following steps: firstly, constructing danger levels of monitoring parameters corresponding to each sensor, then calculating membership coefficients of each monitoring parameter corresponding to each danger level, normalization weights of each monitoring parameter, total membership coefficients of each monitoring parameter corresponding to each danger level, and association coefficients of each total membership coefficient corresponding to each danger level, and finally calculating the total danger levels of each monitoring parameter through the association coefficients; the data fusion method can be applied to a power transmission line monitoring system for safety inspection by adopting a wireless sensor network, fusion calculation is carried out on monitoring data collected by each sensor at a cluster head node, and the result of the fusion calculation is sent to a sink node, so that the technical effects of reducing data redundancy and reducing energy consumption when the node sends data can be achieved.
Description
Technical Field
The invention relates to the technical field of data fusion of the Internet of things, in particular to a data fusion method of the Internet of things and application thereof in intelligent power.
Background
The power transmission line is a key part for ensuring the normal operation of the whole power network, and the wireless sensor network with the advantages of all-weather online, self-organizing network, large-area coverage, adaptation to severe conditions, low cost and the like is adopted, so that the power transmission line with the distribution characteristics of long distance, wide distribution range, high maintenance difficulty, high maintenance cost and the like is patrolled and examined, and the method becomes an important development direction for monitoring the power transmission line.
In order to monitor the same target, a plurality of sensors may be used, so that the accuracy and reliability of the monitoring data can be ensured, and the energy consumed by the sensor nodes in the wireless sensor network during data transmission is far greater than the energy consumed by calculation in the nodes. Experiments show that the energy consumed by the node to transmit 1bit of data is approximately equivalent to the energy for executing 2950 calculation instructions. Therefore, data fusion inside the cluster head node is necessary.
Disclosure of Invention
Technical problem to be solved
The invention aims to provide an Internet of things data fusion method which can be applied to a power transmission line monitoring system for safety inspection by adopting a wireless sensor network.
(II) technical scheme
In order to achieve the purpose, the invention provides the following technical scheme:
an Internet of things data fusion method comprises the following steps:
firstly, respectively acquiring monitoring data M corresponding to each monitoring parameter X by each sensor of one node in a wireless sensor network, and feeding the monitoring data M back to the network node;
step two, the nodes in the wireless sensor network perform fusion calculation on the monitoring data M, and the process is as follows:
step S1, constructing danger levels N (X) of the monitoring parameters X by adopting an objective scientific method, dividing the danger levels into N levels according to danger conditions, and constructing a lower limit value xd and an upper limit value xu of each danger level N (X);
step S2, calculating a membership coefficient u (X) of each monitoring parameter X corresponding to each risk level, wherein the membership coefficient u (X) is calculated according to the following formula:
when M ≦ (xd + xu)/2, u (x) is 2 (M-xd)/(xu-xd);
when M > (xd + xu)/2, u (x)2 (xu-M)/(xu-xd);
and making the risk level N (X) corresponding to the membership coefficient U (X) more than or equal to 0 be the risk level to which each monitoring parameter X actually belongs;
in step S3, the weights q (X) and the normalized weights w (X) of the monitoring parameters X are calculated according to the following formula:
Q(X)＝[N-N(M)+1】＊{1+max[U(X)】}；
W(X)＝Q(X)/∑Q(X)；
step S4, calculating a total membership coefficient tu (X) of each monitoring parameter X corresponding to each risk level, wherein the total membership coefficient tu (X) is calculated according to the following formula:
TU(X)＝∑W(X)＊U(X)；
step S5, calculating the total membership coefficient tu (x) and the associated coefficient gu (x) corresponding to each risk level, wherein the calculation formula is as follows:
GU(X)＝{TU(X)-min[TU(X)】}/{max[TU(X)】-min[TU(X)】}；
step S6, calculating the total risk level z (X) of each monitoring parameter X, wherein the calculation formula is as follows:
Z(X)＝[N(X)＊GU(X)】/∑GU(X)。
preferably, the data fusion method of the internet of things can be applied to a power transmission line monitoring system which adopts a wireless sensor network for safety inspection.
Preferably, the monitoring parameter X in the internet of things data fusion method applied to the intelligent power includes: ambient temperature X1, ambient humidity X2, ambient wind speed X3, lead vibration acceleration X4, and lead temperature X5.
(III) advantageous technical effects
Compared with the prior art, the invention has the following beneficial technical effects:
firstly, constructing danger levels of monitoring parameters corresponding to each sensor, then calculating membership coefficients of each monitoring parameter corresponding to each danger level, normalization weights of each monitoring parameter, total membership coefficients of each monitoring parameter corresponding to each danger level, and association coefficients of each total membership coefficient corresponding to each danger level, and finally calculating the total danger levels of each monitoring parameter through the association coefficients, thereby realizing fusion calculation of the monitoring parameters corresponding to each sensor;
in the process of fusion calculation, the judgment of the risk level corresponding to the monitoring data of each monitoring parameter is determined according to the value range of the risk level which is constructed in advance, and the value range of the risk level is determined by an objective scientific method, so that the beneficial technical effect of objectively and scientifically judging the risk level of the monitoring data can be realized;
the fusion calculation method can be applied to a power transmission line monitoring system for safety inspection by adopting a wireless sensor network, fusion calculation is carried out on monitoring data acquired by each sensor at a cluster head node, and then the result of the fusion calculation is sent to a sink node, so that the technical effects of reducing data redundancy and reducing energy consumption when the node sends data are achieved.
Detailed Description
An Internet of things data fusion method comprises the following steps:
firstly, each sensor of a node in a wireless sensor network respectively acquires monitoring data M corresponding to each monitoring parameter X and feeds the monitoring data M back to the network node;
step two, the nodes in the wireless sensor network perform fusion calculation on the monitoring data M, and the specific fusion calculation process is as follows:
step S1, constructing danger levels N (X) of the monitoring parameters X by adopting an objective scientific method, dividing the danger levels into N levels according to danger conditions, and constructing a lower limit value xd and an upper limit value xu of each danger level N (X);
step S2, calculating a membership coefficient u (X) of each monitoring parameter X corresponding to each risk level, wherein the membership coefficient u (X) is calculated according to the following formula:
when M ≦ (xd + xu)/2, u (x) is 2 (M-xd)/(xu-xd);
when M > (xd + xu)/2, u (x)2 (xu-M)/(xu-xd);
and making the risk level N (X) corresponding to the membership coefficient U (X) more than or equal to 0 be the risk level to which each monitoring parameter X actually belongs;
step S3, calculating the weight q (X) and the normalized weight w (X) of each monitoring parameter X, the calculation formula is as follows:
Q(X)＝[N-N(M)+1】＊{1+max[U(X)】}；
W(X)＝Q(X)/∑Q(X)；
step S4, calculating a total membership coefficient tu (X) of each monitoring parameter X corresponding to each risk level, wherein the total membership coefficient tu (X) is calculated according to the following formula:
TU(X)＝∑W(X)＊U(X)；
step S5, calculating the total membership coefficient tu (x) and the associated coefficient gu (x) corresponding to each risk level, wherein the calculation formula is as follows:
GU(X)＝{TU(X)-min[TU(X)】}/{max[TU(X)]-min[TU(X)】}；
step S6, calculating the total risk level z (X) of each monitoring parameter X, wherein the calculation formula is as follows:
Z(X)＝[N(X)＊GU(X)】/∑GU(X)；
an Internet of things data fusion method applied to intelligent power comprises the following steps:
firstly, respectively acquiring monitoring data M corresponding to each monitoring parameter X in a power transmission line and a surrounding environment by each sensor of a cluster head node in a wireless sensor network, and feeding the monitoring data M back to the cluster head node;
wherein the monitoring parameter X comprises: the method comprises the following steps of (1) carrying out treatment on the lead at an ambient temperature X1, an ambient humidity X2, an ambient wind speed X3, a lead vibration acceleration X4 and a lead temperature X5;
step two, the cluster head node performs fusion calculation on the monitoring data M;
the fusion calculation process is specifically described by taking the following group of sample data as an example;
monitoring data M1 of the ambient temperature X1 is 26.6 ℃;
monitoring data M2 of the ambient humidity X2 is 30%;
monitoring data M3 of the ambient wind speed X3 is 0.5M/s;
monitoring data M4 of the wire vibration acceleration X4 is 0.83M/s2；
Monitoring data M5 of the lead temperature X5 is 80 ℃;
step S1, constructing the danger level N (X) of the monitoring parameter X by an objective scientific method, and dividing the danger level N (X) into N levels according to danger conditions;
order: n is 3;
n (x)1 represents a risk level of 1, i.e., n (x)1 ═ 1;
n (x)2 represents a risk level of 2, i.e., n (x)2 ═ 2;
n (x)3 represents a risk level of 3, i.e., n (x)3 ═ 3;
the danger levels are respectively low, normal and high, the corresponding numbers are 1, 2 and 3, and the larger the number is, the higher the danger level is;
then: the value ranges of the risk level 1 corresponding to the monitoring parameters X are respectively:
the value range of the ambient temperature X1 is [ X1d1, X1u1 ];
the value range of the environmental humidity X2 is [ X2d1, X2u1 ];
the value range of the ambient wind speed X3 is [ X3d1, X3u1 ];
the value range of the lead vibration acceleration X4 is [ X4d1, X4u1 ];
the value range of the wire temperature X5 is [ X5d1, X5u1 ];
the value ranges of the risk level 2 corresponding to the monitoring parameters X are respectively:
the value range of the ambient temperature X1 is (X1d2, X1u 2);
the value range of the ambient humidity X2 is (X2d2, X2u 2);
the value range of the ambient wind speed X3 is (X3d2, X3u 2);
the value range of the lead vibration acceleration X4 is (X4d2, X4u 2);
the value range of the lead temperature X5 is (X5d2, X5u 2);
the value ranges of the risk level 3 corresponding to the monitoring parameters X are respectively:
the value range of the environmental temperature X1 is [ X1d3, X1u3 ];
the value range of the environmental humidity X2 is [ X2d3, X2u3 ];
the value range of the ambient wind speed X3 is [ X3d3, X3u3 ];
the value range of the lead vibration acceleration X4 is [ X4d3, X4u3 ];
the value range of the lead temperature X5 is [ X5d3, X5u3 ];
assigning the end point values of the value range, wherein the assignment is as follows:
order: x1d1 ═ 0, x1u1 ═ 20 ═ x1d2, x1u2 ═ 40 ═ x1d3, and x1u3 ═ 60;
then: the value range of the environmental temperature X1 at the danger level 1 is [0, 20 ], the value range at the danger level 2 is (20, 40), and the value range at the danger level 3 is [40, 60 ];
order: x2d1 ═ 0, x2u1 ═ 50 ═ x2d2, x2u2 ═ 70 ═ x2d3, and x2u3 ═ 100;
then: the value range of the environmental humidity X2 at the danger level 1 is [0, 50 ], the value range at the danger level 2 is (50, 70), and the value range at the danger level 3 is [70, 100 ];
order: x3d1 ═ 0, x3u1 ═ 20 ═ x3d2, x3u2 ═ 40 ═ x3d3, and x3u3 ═ 60;
then: the value range of the ambient wind speed X3 at the risk level 1 is [0, 20 ], the value range at the risk level 2 is (20, 40), and the value range at the risk level 3 is [40, 60 ];
order: x4d1 ═ 0, x4u1 ═ 10 ═ x4d2, x4u2 ═ 20 ═ x4d3, and x4u3 ═ 30;
then: the value range of the lead vibration acceleration X4 at the danger level 1 is [0, 10 ], the value range at the danger level 2 is (10, 20), and the value range at the danger level 3 is [20, 30 ];
order: x5d1 ═ 40, x5u1 ═ 80 ═ x5d2, x5u2 ═ 100 ═ x5d3, and x5u3 ═ 120;
then: the value range of the wire temperature X5 at the danger level 1 is [40, 80 ], the value range at the danger level 2 is (80, 100), and the value range at the danger level 3 is [100, 120 ];
step S2, calculating a membership coefficient u (X) of each monitoring parameter X corresponding to each risk level, wherein the membership coefficient u (X) is calculated according to the following formula:
when M ≦ (xd + xu)/2, u (x)2 (M-xd)/(xu-xd);
when M > (xd + xu)/2, u (x)2 (xu-M)/(xu-xd);
and making the risk level N (X) corresponding to the membership coefficient U (X) more than or equal to 0 be the risk level to which each monitoring parameter X actually belongs;
the specific calculation results of the membership coefficient U (X1) of the monitored data M1 of the ambient temperature X1 corresponding to each risk level are as follows:
because: (x1d1+ x1u1)/2 ═ 10 (0+20)/2, M1 ═ 26.6;
therefore: m1 > (x1d1+ x1u 1)/2;
thus: the membership coefficient U (X1)1 of the monitoring data M1 corresponding to the risk level 1 is calculated as follows:
U(X1)1＝2(x1u1-M1)/(x1u1-x1d1)＝2(20-26.6)/(20-0)＝-0.660；
because: (x1d2+ x1u2)/2 ═ 20+40)/2 ═ 30, M1 ═ 26.6;
so that: m1 < (x1d2+ x1u 2)/2;
thus: the membership coefficient U (X1)2 of the monitoring data M1 corresponding to the risk level 2 is calculated as follows:
U(X1)2＝＝2(M1-x1d2)/(x1u2-x1d2)＝2(26.6-20)/(40-20)＝0.660；
due to the following: (x1d3+ x1u3)/2 ═ 50 (40+60)/2, M1 ═ 26.6;
therefore: m1 < (x1d3+ x1u 3)/2;
thus: the membership coefficient U (X1)3 of the monitoring data M1 corresponding to the risk level 3 is calculated as follows:
U(X1)3＝＝2(M1-x1d3)/(x1u3-x1d3)＝2(26.6-40)/(60-40)＝-1.340；
in summary, the monitoring data M1 belongs to risk level 2, i.e., N (M1) ═ 2;
the specific calculation results of the membership coefficient U (X2) of the monitored data M2 of the ambient humidity X2 corresponding to each risk level are as follows:
because: (x2d1+ x2u1)/2 ═ 25 (0+50)/2, M2 ═ 30;
so that: m2 > (x2d1+ x2u 1)/2;
thus: the membership coefficient U (X2)1 of the monitoring data M2 corresponding to the risk level 1 is calculated as follows:
U(X2)1＝2(x2u1-M2)/(x2u1-x2d1)＝2(50-30)/(50-0)＝0.800；
because: (x2d2+ x2u2)/2 ═ 60 (50+70)/2, M2 ═ 30;
therefore: m2 < (x2d2+ x2u 2)/2;
thus: the membership coefficient U (X2)2 of the monitoring data M2 corresponding to the risk level 2 is calculated as follows:
U(X2)2＝＝2(M2-x2d2)/(x2u2-x2d2)＝2(30-50)/(70-50)＝-2.000；
because: (x2d3+ x2u3)/2 ═ 70+100)/2 ═ 85, M2 ═ 30;
therefore: m2 < (x2d3+ x2u 3)/2;
thus: the membership coefficient U (X2)3 of the monitoring data M2 corresponding to the risk level 3 is calculated as follows:
U(X2)3＝＝2(M2-x2d3)/(x2u3-x2d3)＝2(30-70)/(100-70)＝-2.667；
in summary, the monitoring data M2 belongs to the danger level 1, i.e., N (M2) is 1;
the specific calculation results of the membership coefficient U (X3) of the monitored data M3 of the ambient wind speed X3 corresponding to each risk level are as follows:
because: (x3d1+ x3u1)/2 ═ 10 (0+20)/2, M3 ═ 0.5;
therefore: m3 < (x3d1+ x3u 1)/2;
thus: the membership coefficient U (X3)1 of the monitoring data M3 corresponding to the risk level 1 is calculated as follows:
U(X3)1＝2(M3-x3d1)/(x3u1-x3d1)＝2(0.5-0)/(20-0)＝0.050；
because: (x3d2+ x3u2)/2 ═ 20+40)/2 ═ 30, M3 ═ 0.5;
therefore: m3 < (x3d2+ x3u 2)/2;
thus: the membership coefficient U (X3)2 of the monitoring data M3 corresponding to the risk level 2 is calculated as follows:
U(X3)2＝2(M3-x3d2)/(x3u2-x3d2)＝2(0.5-20)/(40-20)＝-1.950；
because: (x3d3+ x3u3)/2 ═ 50 (40+60)/2, M3 ═ 0.5;
therefore: m3 < (x3d3+ x3u 3)/2;
thus: the membership coefficient U (X3)3 of the monitoring data M3 corresponding to the risk level 3 is calculated as follows:
U(X3)3＝2(M3-x3d3)/(x3u3-x3d3)＝2(0.5-40)/(60-40)＝-3.950；
in summary, the monitoring data M3 belongs to danger level 1, i.e., N (M3) is 1;
the specific calculation results of the membership coefficient U (X4) of the monitor data M4 of the wire vibration acceleration X4 corresponding to each risk level are as follows:
because: (x4d1+ x4u1)/2 ═ 5 (0+10)/2, M4 ═ 0.83;
therefore: m4 < (x4d1+ x4u 1)/2;
thus: the membership coefficient U (X4)1 of the monitor data M4 corresponding to the risk level 1 is calculated as follows:
U(X4)1＝2(M4-x4d1)/(x4u1-x4d1)＝2(0.83-0)/(10-0)＝0.166；
because: (x4d2+ x4u2)/2 ═ 10+20)/2 ═ 15, M4 ═ 0.83;
therefore: m4 < (x4d2+ x4u 2)/2;
thus: the membership coefficient U (X4)2 of the monitoring data M4 corresponding to the risk level 2 is calculated as follows:
U(X4)2＝2(M4-x4d2)/(x4u2-x4d2)＝2(0.83-10)/(20-10)＝-1.834；
because: (x4d3+ x4u3)/2 ═ 20+30)/2 ═ 25, M4 ═ 0.83;
therefore: m4 < (x4d3+ x4u 3)/2;
thus: the membership coefficient U (X4)3 of the monitoring data M4 corresponding to the risk level 3 is calculated as follows:
U(X4)3＝2(M4-x4d3)/(x4u3-x4d3)＝2(0.83-20)/(30-20)＝-3.834；
in summary, the monitoring data M4 belongs to danger level 1, i.e., N (M4) is 1;
the specific calculation results of the membership coefficient U (X5) of the monitor data M5 of the wire temperature X5 corresponding to each risk level are as follows:
because: (x5d1+ x5u1)/2 ═ 60 (40+80)/2, M5 ═ 80;
therefore: m5 > (x5d1+ x5u 1)/2;
thus: the membership coefficient U (X5)1 of the monitor data M5 corresponding to the risk level 1 is calculated as follows:
U(X5)1＝2(x5u1-M5)/(x5u1-x5d1)＝2(80-80)/(80-40)＝0；
because: (x5d2+ x5u2)/2 ═ 80+100)/2 ═ 90, M5 ═ 80;
therefore: m5 < (x5d2+ x5u 2)/2;
thus: the membership coefficient U (X5)2 of the monitoring data M5 corresponding to the risk level 2 is calculated as follows:
U(X5)2＝2(M5-x5d2)/(x5u2-x5d2)＝2(80-80)/(100-80)＝0；
because: (x5d3+ x5u3)/2 ═ 100+120)/2 ═ 110, M5 ═ 80;
therefore: m5 < (x5d3+ x5u 3)/2;
thus: the membership coefficient U (X5)3 of the monitoring data M5 corresponding to the risk level 3 is calculated as follows:
U(X5)3＝2(M5-x5d3)/(x5u3-x5d3)＝2(80-100)/(120-100)＝-2.000；
in summary, since the wire temperature X5 has a value range of [40, 80 ] at the risk level 1 and a value range of (80, 100) at the risk level 2, the monitoring data M5 belongs to the risk level 1, that is, N (M5) ═ 1;
in step S3, the weights q (X) and the normalized weights w (X) of the monitoring parameters X are calculated according to the following formula:
Q(X)＝[N-N(M)+1】＊{1+max[U(X)1，(U(X)2，(U(X)3】}；
W(X)＝Q(X)/∑Q(X)；
the weight Q (X1) of the ambient temperature X1 is calculated as follows:
Q(X1)＝[N-N(M1)+1】＊{1+max[U(X1)1，(U(X1)2，(U(X1)3】}＝[3-2+1】＊{1+0.660}＝3.32；
the weight Q (X2) of the ambient humidity X2 is calculated as follows:
Q(X2)＝[N-N(M2)+1】＊{1+max[U(X2)1，(U(X2)2，(U(X2)3】}＝[3-1+1】＊{1+0.800}＝5.4；
the weight Q (X3) of the ambient wind speed X3 is calculated as follows:
Q(X3)＝[N-N(M3)+1】＊{1+max[U(X3)1，(U(X3)2，(U(X3)3】}＝[3-1+1】＊{1+0.050}＝3.15；
the weight Q (X4) of the wire vibration acceleration X4 is calculated as follows:
Q(X4)＝[N-N(M4)+1】＊{1+max[U(X4)1，(U(X4)2，(U(X4)3】}＝[3-1+1】＊{1+0.166}＝3.498；
the weight Q (X5) of the wire temperature X5 is calculated as follows:
Q(X5)＝[N-N(M5)+1】＊{1+max[U(X5)1，(U(X5)2，(U(X5)3】}＝[3-1+1】＊{1+0}＝3；
normalized weight W (X1) of ambient temperature X1, which is calculated as follows: w (X1) ═ Q (X1)/[ Q (X1) + Q (X2) + Q (X3) + Q (X4) + Q (X5) ═ 3.32/[3.32+5.4+3.15+3.498+3 ] ═ 0.1807;
normalized weight W (X2) of ambient humidity X2, which is calculated as follows: w (X2) ═ Q (X2)/[ Q (X1) + Q (X2) + Q (X3) + Q (X4) + Q (X5) ═ 5.4/[3.32+5.4+3.15+3.498+3 ] ═ 0.2940;
normalized weight W (X3) of ambient wind speed X3, which is calculated as follows: w (X3) ═ Q (X3)/[ Q (X1) + Q (X2) + Q (X3) + Q (X4) + Q (X5) ═ 3.15/[3.32+5.4+3.15+3.498+3 ] ═ 0.1715;
the normalized weight W (X4) of the wire vibration acceleration X4 is calculated as follows:
W(X4)＝Q(X4)/[Q(X1)+Q(X2)+Q(X3)+Q(X4)+Q(X5)】＝3.498/[3.32+5.4+3.15+3.498+3】＝0.1904；
normalized weight W (X5) of wire temperature X5, which is calculated as follows:
W(X5)＝Q(X5)/[Q(X1)+Q(X2)+Q(X3)+Q(X4)+Q(X5)】＝3/[3.32+5.4+3.15+3.498+3】＝0.1633；
step S4, calculating a total membership coefficient tu (X) of each monitoring parameter X corresponding to each risk level, wherein the total membership coefficient tu (X) is calculated according to the following formula:
TU(X)＝W(X1)＊U(X1)+W(X2)＊U(X2)+W(X3)＊U(X3)+W(X4)＊U(X4)+W(X5)＊U(X5)；
calculating the total membership coefficient TU (X)1 of each monitoring parameter X corresponding to the danger level 1, wherein the calculation formula is as follows:
TU(X)1＝W(X1)＊U(X1)1+W(X2)＊U(X2)1+W(X3)＊U(X3)1+W(X4)＊U(X4)1+W(X5)＊U(X5)1
＝0.1807＊(-0.660)+0.2940＊0.800+0.1715＊0.050+0.1904＊0.166+0.1633＊0＝0.1561；
calculating the total membership coefficient TU (X)2 of each monitoring parameter X corresponding to the danger level 2, wherein the calculation formula is as follows:
TU(X)2＝W(X1)＊U(X1)2+W(X2)＊U(X2)2+W(X3)＊U(X3)2+W(X4)＊U(X4)2+W(X5)＊U(X5)2
＝0.1807＊0.660+0.2940＊(-2.000)+0.1715＊(-1.950)+0.1904＊(-1.834)+0.1633＊0＝-1.1524；
calculating the total membership coefficient TU (X)3 of each monitoring parameter X corresponding to the danger level 3, wherein the calculation formula is as follows:
TU(X)3＝W(X1)＊U(X1)3+W(X2)＊U(X2)3+W(X3)＊U(X3)3+W(X4)＊U(X4)3+W(X5)＊U(X5)3
＝0.1807＊(-1.340)+0.2940＊(-2.667)+0.1715＊(-3.950)+0.1904＊(-3.834)+0.1633＊(-2.000)＝-2.7605；
step S5, calculating the total membership coefficient tu (x) and the associated coefficient gu (x) corresponding to each risk level, wherein the calculation formula is as follows:
GU(X)＝{TU(X)-min[TU(X)1，TU(X)2，TU(X)3】}/{max[TU(X)1，TU(X)2，TU(X)3]-min[TU(X)1，TU(X)2，TU(X)3】}；
the overall membership coefficient tu (x)1 is calculated as the association coefficient gu (x)1 corresponding to the risk level 1, as follows:
GU(X)1＝{TU(X)1-min[TU(X)1，TU(X)2，TU(X)3】}/{max[TU(X)1，TU(X)2，TU(X)3]-min[TU(X)1，TU(X)2，TU(X)3】}
＝{0.1561-(-2.7606)}/{0.1561-(-2.7606)}
＝1；
the overall membership coefficient tu (x)2 is calculated as the correlation coefficient gu (x)2 corresponding to the risk level 2, and is calculated as follows:
GU(X)2＝{TU(X)2-min[TU(X)1，TU(X)2，TU(X)3】}/{max[TU(X)1，TU(X)2，TU(X)3]-min[TU(X)1,TU(X)2,TU(X)3】}
＝{(-1.1524)-(-2.7606)}/{0.1561-(-2.7606)}
＝0.551；
the overall membership coefficient tu (x)3 is calculated as the correlation coefficient gu (x)3 corresponding to the risk level 3, and is calculated as follows:
GU(X)3＝{TU(X)3-min[TU(X)1，TU(X)2，TU(X)3】}/{max[TU(X)1，TU(X)2，TU(X)3]-min[TU(X)1,TU(X)2,TU(X)3】}
＝{(-2.7606)-(-2.7606)}/{0.1561-(-2.7606)}
＝0；
step S6, calculating the total risk level z (X) of each monitoring parameter X, wherein the calculation formula is as follows:
Z(X)＝[N(X)＊GU(X)】/∑GU(X)
＝[N(X)1＊GU(X)1+N(X)2＊GU(X)2+N(X)3＊GU(X)3】/[GU(X)1+GU(X)2+GU(X)3】
＝[1＊1+2＊0.551+3＊0】/[1+0.551+0】
＝1.355；
in summary, z (X) is greater than n (X)1 and less than n (X)2, and the fusion calculation result indicates that the overall risk level z (X) of each monitoring parameter X is 1.
Claims (3)
1. A data fusion method of the Internet of things is characterized by comprising the following steps:
firstly, each sensor of a node in a wireless sensor network respectively acquires monitoring data M corresponding to each monitoring parameter X and feeds the monitoring data M back to the network node;
step two, the nodes in the wireless sensor network perform fusion calculation on the monitoring data M, and the process is as follows:
step S1, constructing danger levels N (X) of the monitoring parameters X by adopting an objective scientific method, dividing the danger levels into N levels according to danger conditions, and constructing a lower limit value xd and an upper limit value xu of each danger level N (X);
step S2, calculating a membership coefficient u (X) of each monitoring parameter X corresponding to each risk level, wherein the membership coefficient u (X) is calculated according to the following formula:
when M ≦ (xd + xu)/2, u (x) is 2 (M-xd)/(xu-xd);
when M > (xd + xu)/2, u (x)2 (xu-M)/(xu-xd);
and making the risk level N (X) corresponding to the membership coefficient U (X) more than or equal to 0 be the risk level to which each monitoring parameter X actually belongs;
step S3, calculating the weight q (X) and the normalized weight w (X) of each monitoring parameter X, the calculation formula is as follows:
Q(X)＝[N-N(M)+1]＊{1+max[U(X)]}；
W(X)＝Q(X)/∑Q(X)；
step S4, calculating a total membership coefficient tu (X) of each monitoring parameter X corresponding to each risk level, wherein the total membership coefficient tu (X) is calculated according to the following formula:
TU(X)＝∑W(X)＊U(X)；
step S5, calculating the total membership coefficient tu (x) and the associated coefficient gu (x) corresponding to each risk level, wherein the calculation formula is as follows:
GU(X)＝{TU(X)-min[TU(X)]}/{max[TU(X)]-min[TU(X)]}；
step S6, calculating the total risk level z (X) of each monitoring parameter X, wherein the calculation formula is as follows:
Z(X)＝[N(X)＊GU(X)]/∑GU(X)。
2. the data fusion method of the internet of things according to claim 1, wherein the data fusion method of the internet of things can be applied to a power transmission line monitoring system which adopts a wireless sensor network for safety inspection.
3. The data fusion method of the internet of things as claimed in claim 2, wherein the monitoring of the parameter X in the data fusion method of the internet of things applied to the smart power comprises: ambient temperature X1, ambient humidity X2, ambient wind speed X3, lead vibration acceleration X4, and lead temperature X5.
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