基于区间证据理论的多传感器数据融合水质判断方法

doi:10.11959/j.issn.1000-436x.2016174

Abstract

Abstract:

For the inevitable uncertainty and random uncertainty in the process of measuring water quality data with the sensor network,a multi-sensor data fusion method for water quality evaluation based on interval evidence theory was proposed.Considering the precision error of sensor and the abnormalities of measured data,every water quality data measured by sensor was represented by interval number.By calculating the distance between the water quality data and the features of each water quality class,the interval evidence of water quality class was acquired.According to the interval evidence combining rule,a comprehensive interval evidence was obtained by combining the interval evidence of each sensor.Finally,the water quality class was determined based on the comprehensive interval evidence by the decision rule.Experiments show that the proposed method can evaluate water quality class more accurately from the uncertain water quality data.

Key words: water quality evaluation, interval evidence theory, sensor network, multi-sensor data fusion, uncertainty

Jian ZHOU,Chen-hao MA,Lin-feng LIU,Li-juan SUN,Fu XIAO. Multi-sensor data fusion method for water quality evaluation based on interval evidence theory[J]. Journal on Communications, 2016, 37(9): 20-29.

Figures/Tables 12

信度	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ	Θ
${[m]}^{'}_{1}$	[0.072 3,0.104 8]	[0.112 1,0.196 8]	[0.279 4,0.406 4]	[0.080 3,0.178 0]	[0.041 3,0.085 4]	[0.095 3,0.158 2]
${[m]}^{'}_{2}$	[0.054 0,0.152 1]	[0.112 8,0.342 2]	[0.113 8,0.343 1]	[0.036 7,0.123 8]	[0.021 6,0.071 5]	[0.046 1,0.196 7]
${[m]}^{'}_{3}$	[0.054 7,0.099 9]	[0.081 5,0.176 1]	[0.200 5,0.382 1]	[0.064 8,0.196 0]	[0.033 7,0.091 2]	[0.090 7,0.236 3]

信度	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ	Θ
${[m]}^{'}_{1} \oplus {[m]}^{'}_{2}$	[0.035 3,0.135 6]	[0.098 1,0.334 8]	[0.224 4,0.544 8]	[0.029 5,0.171 7]	[0.012 9,0.076 8]	[0.011 2,0.069 8]
${[m]}^{'}_{1} \oplus {[m]}^{'}_{2} \oplus {[m]}^{'}_{3}$	[0.016 3,0.117 6]	[0.059 2,0.345 6]	[0.250 7,0.711 5]	[0.018 4,0.196 1]	[0.005 7,0.070 3]	[0.002 8,0.036 8]

信度	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ	Θ
${[m]}^{'}_{4}$	[0.214 6,0.362 9]	[0.141 1,0.289 4]	[0.047 9,0.141 9]	[0.031 1,0.073 9]	[0.020 3,0.050 5]	[0.081 4,0.229 7]
${[m]}^{'}_{5}$	[0.806,0.117 5]	[0.122 7,0.233 5]	[0.281 7,0.415 1]	[0.080 4,0.168 7]	[0.041 7,0.085 2]	[0.067 4,0.113 3]

信度	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ	Θ
${[m]}^{'}_{1} \oplus {[m]}^{'}_{2} \oplus {[m]}^{'}_{3} \oplus {[m]}^{'}_{4}$	[0.018 6,0.252 2]	[0.057 0,0.513 2]	[0.152 9,0.742 9]	[0.008 6,0.195 9]	[0.002 5,0.067 4]	[0.000 8,0.026 4]
${[m]}^{'}_{1} \oplus {[m]}^{'}_{2} \oplus {[m]}^{'}_{3} \oplus {[m]}^{'}_{4} \oplus {[m]}^{'}_{5}$	[0.005 5,0.180 8]	[0.029 8,0.517 7]	[0.199 1,0.883 0]	[0.003 7,0.116 2]	[0.000 6,0.043 3]	[0.000 0,0.008 3]

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水质参数	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ
DO	[7.5,10]	[6,7.5]	[5,6]	[3,5]	[2,3]
NH₃-N	[0,0.15]	[0.15,0.5]	[0.5,1.0]	[1.0,1.5]	[1.5,2.0]
TP	[0,0.01]	[0.01,0.025]	[0.025,0.05]	[0.05,0.1]	[0.1,0.2]
TN	[0,0.2]	[0.2,0.5]	[0.5,1.0]	[1.0,1.5]	[1.5,2.0]

传感器	DO	NH₃-N	TP	TN
S₁	[5.0,6.0]	[0.70,0.80]	[0.037,0.047]	[0.71,0.81]
S₂	[5.5,6.5]	[0.45,0.55]	[0.020,0.030]	[0.45,0.55]
S₃	[4.8,5.8]	[0.75,0.85]	[0.035,0.045]	[0.77,0.87]

信度	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ
[m]₁	[0.084 1,0.121 9]	[0.130 4,0.228 8]	[0.324 9,0.472 6]	[0.093 4,0.207 0]	[0.048 0,0.099 4]
[m]₂	[0.064 3,0.181 0]	[0.113 5,0.407 3]	[0.040 7,0.408 5]	[0.043 7,0.147 3]	[0.025 7,0.085 2]
[m]₃	[0.068 3,0.124 9]	[0.101 9,0.220 1]	[0.160 0,0.477 6]	[0.081 1,0.245 0]	[0.042 1,0.114 0]

传感器	DO	NH₃-N	TP	TN
S₄	[7.3,8.3]	[0.08,0.18]	[0.004,0.014]	[0.13,0.23]
S₅	[5.3,6.3]	[0.70,0.80]	[0.027,0.037]	[0.69,0.79]

信度	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ
[m]₄	[0.093 5,0.453 6]	[0.139 9,0.361 8]	[0.059 9,0.177 4]	[0.038 9,0.092 3]	[0.025 3,0.063 1]
[m]₅	[0.089 6,0.130 6]	[0.136 3,0.259 5]	[0.312 6,0.461 2]	[0.089 3,0.187 4]	[0.046 4,0.094 7]

Multi-sensor data fusion method for water quality evaluation based on interval evidence theory

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Figures/Tables 12

References 24

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方法	指标	传感器错误率
方法	指标	40%	35%	30%	25%	20%
	正确率	55%	70%	75%	85%	90%
本文方法	不确定率	45%	30%	25%	15%	10%
	错误率	0%	0%	0%	0%	0%
	正确率	45%	50%	60%	65%	70%
单因子水质标识指数法
	错误率	55%	50%	40%	35%	30%

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