# 农业传感网络数据差错分析算法研究Research on Data Error Analysis Algorithm of Agricultural Sensor Network

DOI: 10.12677/CSA.2018.812213, PDF, HTML, XML, 下载: 561  浏览: 3,593

Abstract: The accuracy of the agrometeorological observation elements has an important impact on the observation effect, especially in the complex environment of the field, especially the factors affecting the temperature change. At present, a large number of devices for field temperature observation are mostly wireless sensor networks with scattered nodes. The sensors used are low-cost, low-precision industrial sensors. Once they are in error, they are difficult to check and have a great impact on subsequent analysis. In order to mitigate the difficulty in searching the dysfunctional wireless sensors and targeting the problematic data, we analyze error from the perspective of agricultural sensor network data, and subsequently check out the errors in the wild wireless sensor network. By fully extracting and analyzing the standard data of the automatic station, it is found that the maximum temperature difference between different regions on the same day is approximately normal distribution on a one-year cycle, and the temperature data characteristics collected by the analog automatic station are collected by industrial sensors. The data were extracted and smoothed, and it is found that this property is also applicable to industrial sensors. Therefore we design a data error analysis algorithm, applied to wild industrial sensor calibration. By validating temperature, which is one of the essential factors in agricultural atmosphere observation, the result demonstrates the efficiency of the algorithm. Instead of directly manipulating on the current wire-less sensor network, the algorithm conducts dimensionless processing, followed by extraction, smoothing, and comparison to inspect error, to save the cost of error inspection.

1. 前言

2. 我国农业气象观测现状

3. 无线传感网络与观测要素的地域特征

3.1. 观测要素验证的确定

3.2. 温度的地域特征挖掘

${D}_{{A}_{n}{B}_{n}}={T}_{{A}_{n}}-{T}_{{B}_{n}}$ (1)

${\Psi }_{a,\tau }\left(t\right)=\frac{1}{\sqrt{a}}\Psi \left(\frac{t-\tau }{a}\right)$ (2)

${\Psi }_{a,\tau }\left(t\right)={a}_{0}^{\frac{-j}{2}}\Psi \left[{a}_{0}^{-j}\left(t-\tau \right)\right],\text{\hspace{0.17em}}\text{\hspace{0.17em}}j=0,1,2,\cdots$ (3)

$\tau =k{a}_{0}^{j}{\tau }_{0}$ (4)

${\text{WT}}_{f}\left({a}_{0}^{j},k{\tau }_{0}\right)=\int f\left(t\right){\Psi }_{{a}_{0}^{j},k{\tau }_{0}}^{*}\left(t\right)\text{d}t,\text{\hspace{0.17em}}\text{\hspace{0.17em}}j=0,1,2,\cdots ,\text{\hspace{0.17em}}\text{\hspace{0.17em}}k\in Z$ (5)

${\Psi }_{j,k}\left(t\right)={2}^{-\frac{j}{2}}\Psi \left({2}^{-j}t-k\right)$ (6)

${\text{WT}}_{f}\left(j,k\right)=\int f\left(t\right){\Psi }_{j,k}^{*}\left(t\right)\text{d}t$ (7)

$x\left(n\right)=\frac{x\left(n-2\right)+x\left(n-1\right)+x\left(n\right)+x\left(n+1\right)+x\left(n+2\right)}{5}$ (8)

$x\left(1\right)=\frac{3x\left(1\right)+2x\left(2\right)+x\left(3\right)-x\left(4\right)}{5}$ (9)

$x\left(2\right)=\frac{4x\left(1\right)+3x\left(2\right)+2x\left(3\right)+x\left(4\right)}{10}$ (10)

$x\left(m-1\right)=\frac{x\left(m-3\right)+2x\left(m-2\right)+3x\left(m-1\right)+4x\left(m\right)}{10}$ (11)

$x\left(m\right)=\frac{-x\left(m-3\right)+x\left(m-2\right)+2x\left(m-1\right)+3x\left(m\right)}{5}$ (12)

4. 仿真结果及特性分析

4.1. 模型建立与仿真

Table 1. A point daily maximum temperature table

Table 2. B point daily maximum temperature table

Table 3. Maximum temperature difference between A & B

Figure 1. Automatic station analysis model framework

Figure 2. Industrial sensor validation model framework

Figure 3. Error detection flow chart

4.2. 特性分析

Figure 4. Frequency distribution curve of the highest temperature difference between A and B

4.3. 检错可行性验证

Figure 5. Industrial sensor test environment diagram

Figure 6. Error detection decision flow chart

Figure 7. Error detection test result chart

5. 总结与展望

1) 数据的利用：由于数据的差异型，本文只用了人工观测的数据，未来可以从自动站与人工检测的数据同步方向入手，将自动站的数据进行相应的平滑、筛选、补偿处理，将其与人工观测的数据结合起来分析。

2) 工业传感器的矫正：本文仅是提供了通过数据检测传感器好坏的方法，但对于出错数据的矫正方法仍有待研究，如可以采用游走拓扑等方式。

3) 工业传感器的精度提升：在本文提出的检测方法基础上，可以利用最高温度差的回归特性，将工业传感器向计量传感器的精度进行修正，使成本低的工业传感器经过数据修正达到计量级精度。

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