CN114707803B - Internet of things data fusion method and application thereof in intelligent power - Google Patents
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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 a risk level of each sensor corresponding to a monitoring parameter, then calculating a membership coefficient of each monitoring parameter corresponding to each risk level, a normalized weight of each monitoring parameter, a total membership coefficient of each monitoring parameter corresponding to each risk level and a correlation coefficient of the total membership coefficient corresponding to each risk level, and finally calculating the total risk level of each monitoring parameter through the correlation coefficient; the data fusion method can be applied to a power transmission line monitoring system adopting a wireless sensor network for safety inspection, and the data fusion method has the technical effects of reducing the data redundancy and reducing the energy consumption of the nodes for transmitting data by fusion calculation on the monitoring data acquired by each sensor at the cluster head node and transmitting the fusion calculation result to the sink node.
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 of the data fusion method of the Internet of things to intelligent power.
Background
The power transmission line is a key part for ensuring the normal operation of the whole power network, and a 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 subjected to inspection, and the power transmission line is an important development direction of power transmission line monitoring.
Multiple sensors may be used to monitor the same target, so that accuracy and reliability of the monitored data can be guaranteed, and the energy consumed by the sensor nodes in the wireless sensor network when data transmission is performed is far greater than the energy consumed by calculation in the nodes. Experiments show that the energy consumed by a node to transmit 1bit of data is approximately equivalent to the energy required to execute 2950 computing instructions. It is necessary to perform data fusion inside the cluster head node.
Disclosure of Invention
(One) solving the technical problems
The invention aims to provide an Internet of things data fusion method which can be applied to a power transmission line monitoring system for carrying out safety inspection by adopting a wireless sensor network.
(II) technical scheme
In order to achieve the above purpose, the present invention provides the following technical solutions:
The data fusion method of the Internet of things comprises the following steps of:
Step one, each sensor of a node in a wireless sensor network respectively collects 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 a risk level N (X) of the monitoring parameter X by adopting an objective scientific method, dividing the risk level N (X) into N levels according to the risk condition, and constructing a lower limit value xd and an upper limit value xu of each risk level N (X) at the same time;
step S2, calculating the membership coefficient U (X) of each monitoring parameter X corresponding to each risk level, wherein the calculation formula of the membership coefficient U (X) is as follows:
when M is less than or equal to (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) not less than 0 be the risk level actually belonging to each monitoring parameter X;
step S3, calculating the weight Q (X) and the normalized weight W (X) of each monitoring parameter X, wherein 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 the total membership coefficient TU (X) of each monitoring parameter X corresponding to each risk level, wherein the calculation formula of the total membership coefficient TU (X) is as follows:
TU(X)＝∑W(X)＊U(X)；
step S5, calculating the association coefficient GU (X) of the total membership coefficient TU (X) corresponding to each risk level, 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 for carrying out safety inspection by adopting a wireless sensor network.
Preferably, the monitoring the parameter X in the internet of things data fusion method applied to intelligent power includes: ambient temperature X1, ambient humidity X2, ambient wind speed X3, wire vibration acceleration X4, wire temperature X5.
(III) beneficial technical effects
Compared with the prior art, the invention has the following beneficial technical effects:
firstly, constructing a risk level of each sensor corresponding to a monitoring parameter, then calculating a membership coefficient of each monitoring parameter corresponding to each risk level, a normalized weight of each monitoring parameter, a total membership coefficient of each monitoring parameter corresponding to each risk level and a correlation coefficient of the total membership coefficient corresponding to each risk level, and finally calculating the total risk level of each monitoring parameter through the correlation coefficient, thereby realizing fusion calculation of the monitoring parameters corresponding to each sensor;
In the process of fusion calculation, the judgment of the corresponding risk level of the monitoring data of each monitoring parameter is established according to the magnitude range of the pre-constructed risk level, and the magnitude 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 adopting a wireless sensor network for safety inspection, and the technical effects of reducing the data redundancy and reducing the energy consumption of the nodes for transmitting data are achieved by carrying out fusion calculation on monitoring data acquired by each sensor at a cluster head node and then transmitting the fusion calculation result to a sink node.
Detailed Description
The data fusion method of the Internet of things comprises the following steps of:
Step one, each sensor of a node in a wireless sensor network respectively collects 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 a risk level N (X) of the monitoring parameter X by adopting an objective scientific method, dividing the risk level N (X) into N levels according to the risk condition, and constructing a lower limit value xd and an upper limit value xu of each risk level N (X) at the same time;
step S2, calculating the membership coefficient U (X) of each monitoring parameter X corresponding to each risk level, wherein the calculation formula of the membership coefficient U (X) is as follows:
when M is less than or equal to (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) not less than 0 be the risk level actually belonging to each monitoring parameter X;
step S3, calculating the weight Q (X) and the normalized weight W (X) of each monitoring parameter X, wherein 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 the total membership coefficient TU (X) of each monitoring parameter X corresponding to each risk level, wherein the calculation formula of the total membership coefficient TU (X) is as follows:
TU(X)＝∑W(X)＊U(X)；
step S5, calculating the association coefficient GU (X) of the total membership coefficient TU (X) corresponding to each risk level, 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:
Step one, each sensor of a cluster head node in a wireless sensor network respectively collects monitoring data M corresponding to each monitoring parameter X in a power transmission line and the surrounding environment, and feeds the monitoring data M back to the cluster head node;
Wherein the monitoring parameter X comprises: ambient temperature X1, ambient humidity X2, ambient wind speed X3, wire vibration acceleration X4, wire temperature X5;
Secondly, the cluster head node performs fusion calculation on the monitoring data M;
taking the following group of sample data as an example, the fusion calculation process is specifically described;
Monitored data m1=26.6 ℃ for ambient temperature X1;
monitored data m2=30% of ambient humidity X2;
monitoring data m3=0.5M/s for the ambient wind speed X3;
Monitor data m4=0.83M/s 2 of wire vibration acceleration X4;
Monitored data m5=80 ℃ for wire temperature X5;
Step S1, constructing a risk level N (X) of the monitoring parameter X by adopting an objective scientific method, wherein the risk level N (X) can be classified into N levels according to the risk condition;
and (3) making: n=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 1, 2 and 3 are used for representing, and the larger the number is, the higher the danger level is;
then: the magnitude ranges of the risk level 1 corresponding to the monitoring parameters X are respectively as follows:
The value range of the ambient temperature X1 is [ X1d1, X1u1 ];
The value range of the ambient humidity X2 is [ X2d1, X2u1 ];
the value range of the ambient wind speed X3 is [ X3d1, X3u1 ];
The value range of the wire vibration acceleration X4 is [ X4d1, X4u1 ];
The value range of the wire temperature X5 is [ X5d1, X5u1 ];
The magnitude ranges of the risk level 2 corresponding to the monitoring parameters X are respectively as follows:
The value range of the ambient temperature X1 is (X1 d2, X1u 2);
The value range of the ambient humidity X2 is (X2 d2, X2u 2);
The value range of the ambient wind speed X3 is (X3 d2, X3u 2);
The value range of the wire vibration acceleration X4 is (X4 d2, X4u 2);
the value range of the wire temperature X5 is (X5 d2, X5u 2);
The magnitude ranges of the risk level 3 corresponding to the monitoring parameters X are respectively as follows:
the value range of the ambient temperature X1 is [ X1d3, X1u3 ];
The value range of the ambient humidity X2 is [ X2d3, X2u3 ];
the value range of the ambient wind speed X3 is [ X3d3, X3u3 ];
The value range of the wire vibration acceleration X4 is [ X4d3, X4u3 ];
the value range of the wire temperature X5 is [ X5d3, X5u3 ];
and carrying out assignment processing on the endpoint values of the value range, wherein the specific assignment is as follows:
And (3) making: x1d1=0, x1u1=20=x1d2, x1u2=40=x1d3, x1u3=60;
Then: the environmental temperature X1 has a value range of [0, 20 ] at the risk level 1, a value range of (20, 40) at the risk level 2, and a value range of [40, 60] at the risk level 3;
And (3) making: x2d1=0, x2u1=50=x2d2, x2u2=70=x2d3, x2u3=100;
then: the environmental humidity X2 has a value range of [0, 50 ] at the risk level 1, a value range of (50, 70) at the risk level 2, and a value range of [70, 100] at the risk level 3;
and (3) making: x3d1=0, x3u1=20=x3d2, x3u2=40=x3d3, x3u3=60;
then: the value range of the ambient wind speed X3 at the dangerous level 1 is [0, 20 ], the value range at the dangerous level 2 is (20, 40), and the value range at the dangerous level 3 is [40, 60];
And (3) making: x4d1=0, x4u1=10=x4d2, x4u2=20=x4d3, x4u3=30;
Then: the value range of the vibration acceleration X4 of the lead is [0, 10 ] at the dangerous level 1, the value range of the vibration acceleration X4 of the lead is (10, 20) at the dangerous level 2, and the value range of the vibration acceleration X4 of the lead is [20, 30 ] at the dangerous level 3;
And (3) making: x5d1=40, x5u1=80=x5d2, x5u2=100=x5d3, x5u3=120;
Then: the value range of the wire temperature X5 at the dangerous level 1 is [40, 80 ], the value range at the dangerous level 2 is (80, 100), and the value range at the dangerous level 3 is [100, 120 ];
step S2, calculating the membership coefficient U (X) of each monitoring parameter X corresponding to each risk level, wherein the calculation formula of the membership coefficient U (X) is as follows:
when M is less than or equal to (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) not less than 0 be the risk level actually belonging to each monitoring parameter X;
The specific calculation results of the membership coefficient U (X1) of the monitoring data M1 of the ambient temperature X1 corresponding to each risk level are as follows:
as a result of: (x1d1+x1u1)/2= (0+20)/2=10, m1=26.6;
so that: m1 > (x1d1+x1u1)/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；
as a result of: (x1d2+x1u2)/2= (20+40)/2=30, m1=26.6;
So that: m1 < (x1d2+x1u2)/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；
as a result of: (x1d3+x1u3)/2= (40+60)/2=50, m1=26.6;
So that: m1 < (x1d3+x1u3)/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 the risk level 2, i.e., N (M1) =2;
The specific calculation results of the membership coefficient U (X2) of the monitoring data M2 of the ambient humidity X2 corresponding to each risk level are as follows:
as a result of: (x2d1+x2u1)/2= (0+50)/2=25, m2=30;
So that: m2 > (x2d1+x2u1)/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；
As a result of: (x2d2+x2u2)/2= (50+70)/2=60, m2=30;
So that: m2 < (x2d2+x2u2)/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；
as a result of: (x2d3+x2u3)/2= (70+100)/2=85, m2=30;
so that: m2 < (x2d3+x2u3)/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 risk level 1, i.e., N (M2) =1;
the specific calculation results of the membership coefficient U (X3) of the monitoring data M3 of the ambient wind speed X3 corresponding to each risk level are as follows:
As a result of: (x3d1+x3u1)/2= (0+20)/2=10, m3=0.5;
so that: m3 < (x3d1+x3u1)/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；
as a result of: (x3d2+x3u2)/2= (20+40)/2=30, m3=0.5;
So that: m3 < (x3d2+x3u2)/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；
as a result of: (x3d3+x3u3)/2= (40+60)/2=50, m3=0.5;
so that: m3 < (x3d3+x3u3)/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 the risk level 1, i.e., N (M3) =1;
The specific calculation result of the monitoring data M4 of the wire vibration acceleration X4 corresponding to the membership coefficient U (X4) of each risk level is as follows:
as a result of: (x4d1+x4u1)/2= (0+10)/2=5, m4=0.83;
so that: m4 < (x4d1+x4u1)/2;
Thus: the membership coefficient U (X4) 1 of the monitoring 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；
as a result of: (x4d2+x4u2)/2= (10+20)/2=15, m4=0.83;
So that: m4 < (x4d2+x4u2)/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；
as a result of: (x4d3+x4u3)/2= (20+30)/2=25, m4=0.83;
so that: m4 < (x4d3+x4u3)/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 the risk level 1, i.e., N (M4) =1;
The specific calculation results of the membership coefficient U (X5) of the monitoring data M5 of the wire temperature X5 corresponding to each risk level are as follows:
As a result of: (x5d1+x5u1)/2= (40+80)/2=60, m5=80;
So that: m5 > (x5d1+x5u1)/2;
thus: the membership coefficient U (X5) 1 of the monitoring 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；
As a result of: (x5d2+x5u2)/2= (80+100)/2=90, m5=80;
so that: m5 < (x5d2+x5u2)/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；
As a result of: (x5d3+x5u3)/2= (100+120)/2=110, m5=80;
So that: m5 < (x5d3+x5u3)/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, i.e., N (M5) =1;
step S3, calculating the weight Q (X) and the normalized weight W (X) of each monitoring parameter X, wherein the calculation formula is as follows:
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；
the normalized weight W (X1) of the ambient temperature X1 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;
The normalized weight W (X2) of the ambient humidity X2 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;
The normalized weight W (X3) of the ambient wind speed X3 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;
normalized weight W (X4) of 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；
the normalized weight W (X5) of the wire temperature X5 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 the total membership coefficient TU (X) of each monitoring parameter X corresponding to each risk level, wherein the calculation formula of the total membership coefficient TU (X) is as follows:
TU(X)＝W(X1)＊U(X1)+W(X2)＊U(X2)+W(X3)＊U(X3)+W(X4)＊U(X4)+W(X5)＊U(X5)；
the total membership coefficient TU (X) 1 of each monitoring parameter X corresponding to the risk level 1 is calculated 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；
the total membership coefficient TU (X) 2 of each monitoring parameter X corresponding to the risk level 2 is calculated 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；
the total membership coefficient TU (X) 3 of each monitoring parameter X corresponding to the risk level 3 is calculated 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 association coefficient GU (X) of the total membership coefficient TU (X) corresponding to each risk level, 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 association coefficient GU (X) 1 of the total membership coefficient TU (X) 1 corresponding to the risk level 1 is calculated 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 association coefficient GU (X) 2 of the total membership coefficient TU (X) 2 corresponding to the risk level 2 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 association coefficient GU (X) 3 of the total membership coefficient TU (X) 3 corresponding to the risk level 3 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 shows that the overall risk level Z (X) of each monitoring parameter X is 1.
Claims (3)
1. The data fusion method of the Internet of things is characterized by comprising the following steps of:
Step one, each sensor of a node in a wireless sensor network respectively collects 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 a risk level N (X) of the monitoring parameter X by adopting an objective scientific method, dividing the risk level N (X) into N levels according to the risk condition, and constructing a lower limit value xd and an upper limit value xu of each risk level N (X) at the same time;
step S2, calculating the membership coefficient U (X) of each monitoring parameter X corresponding to each risk level, wherein the calculation formula of the membership coefficient U (X) is as follows:
when M is less than or equal to (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) not less than 0 be the risk level actually belonging to each monitoring parameter X;
step S3, calculating the weight Q (X) and the normalized weight W (X) of each monitoring parameter X, wherein the calculation formula is as follows:
Q(X)=[N-N(M)+1]*{1+max[U(X)]}；
W(X)=Q(X)/ΣQ(X)；
Step S4, calculating the total membership coefficient TU (X) of each monitoring parameter X corresponding to each risk level, wherein the calculation formula of the total membership coefficient TU (X) is as follows:
TU(X)=ΣW(X)*U(X)；
step S5, calculating the association coefficient GU (X) of the total membership coefficient TU (X) corresponding to each risk level, 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)]/ΣGU(X)。
2. the internet of things data fusion method according to claim 1, wherein the internet of things data fusion method is applied to a power transmission line monitoring system for safety inspection by adopting a wireless sensor network.
3. The method for data fusion of the internet of things according to claim 2, wherein the monitoring of the parameter X in the method for data fusion of the internet of things applied to intelligent power comprises: ambient temperature X1, ambient humidity X2, ambient wind speed X3, wire vibration acceleration X4, wire temperature X5.
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