A novel optical mechanism and scheme of Raman optical time domain reflection (ROTDR) for improving achievable spatial resolution is proposed. Herein, the Raman backscattering characteristic of the optical fiber based on the amplified spontaneous emission (ASE) detection is first theoretically analyzed. Then, the propagation model of the ASE Raman anti-Stokes trace is established. Based on the propagation model, the time domain difference reconstruction is applied to the whole collected Raman anti-Stokes trace. The reconstructed Raman anti-Stokes signal and ASE signal, which are first proposed here, show a distinct correlation characteristic. For this characteristic, a higher signal-to-noise ratio (SNR) demodulation scheme, named compression correlation demodulation, is used to detect the temperature change along the optical fiber. The positive correlation peak generated by the proposed mechanism produces a multiplied amplification effect through compressing the whole intensity points of Raman anti-Stokes in the fiber under test (FUT) region. Furthermore, the proposed scheme reduces the crosstalk of the non-FUT in the demodulation process. Benefiting from this new optical sensing mechanism and scheme, a simulation experiment with 7.5 mm spatial resolution and 0.1 C temperature sensitivity is demonstrated, and the spatial resolution is independent of sensing distance. These advances greatly facilitate the practicability of ROTDR.

There is an optical interference noise in the conventional Raman-based fiber-optics distributed sensing, which results in a poor temperature resolution performance. In addition, the traditional whole-fiber demodulation principle complicates the operation steps of the system. In this paper, a novel dynamic difference attenuation recognition (DDAR) principle is operated in the DDP scheme (dual demodulation principle) and the SDP scheme (self-demodulation principle) respectively. It not only helps to eliminate the optical interference noise, but also omits the whole-fiber calibration process. In this experiment, a temperature resolution of 0.30 °C (17.0 km) is achieved through using the DDP scheme based on the DDAR principle, and the measurement time can be shortened to 1.5 s. Meanwhile, a temperature resolution of 0.18 °C (17.0 km) is obtained for the SDP scheme under the DDAR principle. The SNR of DDP and DSP schemes can be optimized to 12.82 dB and 13.32 dB by the proposed DDAR technology. Furthermore, the temperature resolution performance under a large temperature measurement range (0–1000 °C) is theoretically analyzed. The results indicate that the temperature responsivity for DDP and SDP schemes are parabolic and linear type respectively, which causes the temperature resolution of the two schemes to show a different trend with the change of temperature. The proposed DDAR method also can improve the temperature resolution in such a large temperature measurement range.

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Insurance is one of the three drivers of the financial industry and an essential part of the national economy. Its growth and expansion are of great significance to the healthy development of the national economy. Since the reform and opening-up, China's insurance industry has developed rapidly. Global premium growth is expected to stabilize at 4.4 percent over the next 10 years, while growth in the Chinese market is expected to average 9.5 percent [1]. However, unbalanced regional development has appeared in China's insurance market. Therefore, this paper will study the regional differences in China's insurance market growth and its influencing factors. At first, per capita disposable income, per capita pension of enterprises, per capita savings deposit, juvenile and child dependency ratio, elderly population dependency ratio, and the proportion of the tertiary industry were selected as explanatory variables to study the factors affecting premium income. The least-square method is used to build a regression model to calculate its correlation. The data are the cross-sectional data of provincial administrative units in China from 2018 to 2020. After the model establishment and test, the conclusion is drawn that per capita disposable income, per capita pension and per capita savings are the important factors that lead to the unbalanced development of regional premium income.