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Once data theft, it will bring immeasurable losses.
The chip achieves data transmission with standard physical interface and data transmission protocol.
In addition, the chip adopts 3DES method to encrypt data.
Research on computer network data security strategy [J].
Multisensor data fusion and its applications[M].

Data processing of the measured data is performed by computers, which are essentially digital data processing devices.
Data processing of uniform data is significantly easier and faster than data processing of nonuniform data because equal duration of time intervals between moments of measurements enables simplification of calculations performed during the data processing procedures.
However, when strong electromagnetic induction is present, long series of consecutive data are corrupted and lost during the data transmission process, and such data transmission losses are called heavy data transmission losses.
The received data in cases of heavy data transmission losses cannot be made uniform in a meaningful way.
It is obvious that data losses cause reduction of signal power and hence the reduction of spectral power, but even basic considerations show that the distribution of power losses depends on the signal spectra and duration and distribution in time of loss events.

Technological Transformation for Loss Reduction in Power Networks in Low-carbon Economy Energy Saving and Consumption Reduction.
Technical transformation for loss reduction could help optimize, refine and quantify loss reduction measures.
Tab. 1 Projected National Percentages of Thermal Power and Average Coal Consumption of Electricity Generating (×10-6) Year 2013 2014 2015 2016 2017 2018 Percentage/% 80.58 79.81 78.93 77.95 76.84 76.12 Coal Consumption/t/kWh 335 332 329 326 322 320 Data from: http://news.bjx.com.cn, http://www.lib.hust.edu.cn/xueke/slsd/wszy/dl.htm, etc.
Tab. 2 Predicted Electricity Saving Amount (×10-6) and Transmission Loss in Distribution Network of the Studied Company Year 2014 2015 2016 2017 2018 2019 Electricity Saving Amount/kWh 32.28 40.56 45.34 49.76 52.18 54.45 Overall Transmission Loss/% 4.8 4.6 4.5 4.4 4.3 4.2 Data from: the studied power supply company.
With the above data and assumptions substituted in the emission reducing model introduced in the last section, the developing trend of low-carbon benefit of the company, by the implementation of technical transformation projects for loss reduction, in the next five years was plotted, as shown in following Figure 1.

Crash Reduction Factor(CRF) CRF (Crash Reduction Factor), also known as Crash Reduction Rate, refers to the percentage of reduction in the number of traffic accidents when taking a particular security measures in transport system[1].
The data required is: the average number of accidents in many years before the implementation of safeguard measures which is called aB, the average number of accidents in several years after the implementation of safeguard measures which is called aA.
A certain improvement measures have classified the value of CRF to different type of accidents, which not only improves the accuracy but also provides a variety of research required data.
General types of accidents are: All, Fatality or Injury, Property Damage Only, Head On, Rear End, Side Swipe, Left Turn, Right Turn, Right Angle, Fixed Object, Pedestrian, Red Light Run, Run Off Road, Wet , Night[3].Table1 shows the Statistic data of CRF channelization portion in Missouri State，U.S.A.[4] Table.1 Statistic Data of CRF Channelization Portion in Missouri State，U.S.A.
If it is got that the historical traffic accident data of the road which has been implemented a certain safeguard measure, the number of reduction of accidents can also be calculated after the implementation of a certain safeguard measure, and the calculation formula(Eq.  2) is used for caculating the number of reduction of accidents.

It is proved that the application of fuzzy control in vehicle emission reduction is feasible through the processing and analysis of test data.
It does not need to the data processing of blur and eliminate blur in this study, because there are only 13 conditions measured in tests.
Put the original test data into formula (1) ~ formula (7), the urea solution needed theoretically to completely eliminate NOx under ESC conditions are obtained as shown in table 3.
Through look-up table, called the corresponding output values while in different conditions, measured the NOx values processed, the data are as shown in table 6.
The application of fuzzy control in vehicle emission reduction is feasible 2.

Therefore, the system model reduction is critical.
So the balanced truncation model reduction method is more suitable for order reduction of the large scale systems.
Fitting results and test data are shown in Fig. 1.
As can be seen from Fig. 1, vector fitting results fit the test data in a very high degree. .
Figure 1.Vector fitting results compared with test data.

The index system reflects not only current situation, but also situation before and after this work. 1.2 To establish a evaluation index system According to principles, national planning goal for as well as research bibliography on energy conservation and emission reduction for reference, reality of Jiangsu about environment and limits of data, here we divide indicator system into layers-goal layer, rule layer and indices layer.
The original index data standardization, standard matrix Z； Step 2.
Following with the Jiangsu province thirteen city above eighteen indexes data in 2008, this paper adopts the method of principal component analysis empirical evaluation, This paper uses the data from the" Jiangsu Province in 2008Statistical Yearbook", each city2008statistical yearbook,2008 each city Environmental Status Bulletin 2.1 City industrial energy saving and emission reduction investment performance comprehensive evaluation By means of statistical analysis software Minitab15.0, on 18 indicators, do principal component analysis, Evaluation of the results in Table 2 are obtained.
Conclusion According to the data table 2-5, The following Conclusions is obtained ,respectively
[4] Data Information on http://www.jssb.gov.cn/

The obtained results of thermodynamic modeling and experimental data showed that it is possible in principle to directly microlite boron steel by reducing it with aluminum and silicon contained in the metal.
The obtained TM results showed good convergence with the experimental data [21].
There is no data on the processes of boron reduction from slags containing B2O3, reducing elements that are part of the processed steel (aluminum, silicon, carbon).
In table 1 presents TM data on the degree of reduction of boron depending on the aluminum content in steel in the temperature range 1400–1700 0С, which indicate its decrease with increasing process temperature for all initial concentrations of aluminum and increasing the degree of boron reduction with increasing aluminum concentration in the metal.
An experimental study of the distribution of boron between the oxide and metallic phases confirms the data of TM on the possibility of boron reduction from slag containing 4% B2O3, aluminum and silicon in the steel, and the influence of the initial aluminum content in the melt on the degree of boron reduction.

The texture of the secondary recrystallized sample under 97.2% cold rolling reduction rate condition consists of {110}<112> orientation, which is quite different from Goss ({110}<001>) orientation obtained under lower cold rolling reduction rate conditions.
There are some experimental data demonstrating that these grain boundaries have high energy (HE) [3].
In order to change a primary recrystallized texture, it is effective to control cold rolling reduction rate.
The orientations after secondary recrystallization were measured by the back Laue reflection method, and its ODF was calculated from the secondary recrystallized orientation data using the series expansion method.
The sample under the 92.6% cold rolling reduction rate condition has highly oriented Goss texture.

The Investigation of Pipe Ends Formation under Reduction Mill Rolling D.
It is established that the greatest "contribution" to wall thickness data spread is made by the pipe facets, which is caused by the influence of the reduction regimes and the rolls calibration due to the metal flow into the tapers of groove.
The reduction regime is chosen so that the wall thickness increases from 4.5 to 4.8 mm.
Thus, at the rear end the average wall thickness is greater than at the front end, which corresponds to the literature data [2-8].
Summary In the course of the investigation of the longitudinal and transverse wall thickness variation of the oil-well tubing with the size of 60 × 5 mm rolled at EWPM 20-102 it was established that the greatest "contribution" to the wall thickness data spread is the pipe facetedness that is caused by the influence of the reduction regimes at the reduction mill and the rolls calibration due to the metal flow into the tapers of groove.