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In order to solve the problem of data leakage and tampering in end-to-end power data security management, this paper proposes a Blockchain-based power terminal data security management model, which includes power terminals and Blockchain nodes. Among them, the power terminal is responsible for the collection of front-end substation data; the Blockchain node is responsible for data verification and data storage. Secondly, the data security management mechanism of power terminal based on Blockchain is proposed, including data aggregation, data encryption and transmission, signature verification for single Blockchain, aggregation signature for main Blockchain nodes, and intelligent contract storage. Finally, by applying the mechanism to the data storage process and data request process analysis, the data management mechanism proposed in this paper has a good application effect.

The traditional system has low recognition accuracy for the vibration signal of optical cable damaged by external force. Therefore, a power communication optical cable anti external force monitoring system based on potdr is designed. In terms of hardware, the modular structure of the system is constructed, the potdr distributed optical fiber sensor and power supply circuit are designed, and the vibration signal of the optical cable is collected. In the aspect of software, optical fiber communication transmits vibration signal, denoises and filters the signal, extracts the state characteristics of optical cable, and monitors whether external force damage occurs. The experimental results show that, compared with the conventional system, the designed system improves the identification accuracy of the vibration signal of the optical cable damaged by external force, and ensures the operation and maintenance management level of the optical cabl.

A new method of extracting rice cropping area from high resolution remote sensing image based on sample knowledge mining was proposed to aim at the fact that rice cropping area in high-resolution remote sensing images is actually mixed information of rice, soil, water, weeds and duckweed. Based on the spatial autocorrelation theory, this method makes use of the combined features of rice cropping area based on various basic units of spectrum and texture, to build an extraction strategy of rice cropping area: firstly, achieve image segmentation to obtain the basic unit of various mixed ground information. Then the rice basic unit types are determined by analyzing the basic unit types contained in the rice sample polygon, and the basic units of the corresponding types are all classified into the initial rice cropping area. Finally, eliminate the initial rice cropping area polygons which do not conform to the basic unit combination rule of rice cropping area learnt from the samples. The result of extracting rice cropping area shows that the method in this paper is precise and practical.

Flat surfaces or areas with no local gradient are general types of terrain in raster Digital Elevation Models (DEMs). When a DEM is used for automated hydrological analysis, it is necessary to assign flow directions over flat surfaces. This paper presents an improved algorithm for assigning flow directions over flat surfaces that builds on earlier work and offers efficiency improvements. The improved algorithm use distance transform method to calculate the distance values of cells in flat surfaces towards lower terrain and away from higher terrain, which can replace the recursive process in the earlier algorithm by a linear process, and reduce random data access. Furthermore, the improved algorithm assigns weights with different values and signs to the two distance values to guarantee that all flat surfaces are drained, which avoids the need to label each flat surface using the flood-fill algorithm in the earlier work. Comparisons of speed indicate that the improved algorithm is more efficient in assigning flow directions over flat surfaces.

Calculating drainage accumulation in a digital elevation model (DEM) is a common requirement for hydrology and terrain analysis. This letter presents a basin tree index (BTI) algorithm to improve the efficiency of this calculation, achieving the time complexity of $O(N)$ and the input–output efficiency of $O(\hbox{Scan}(N))$ . We have developed a BTI to guide the calculation sequence, allowing us to avoid invalid and repeat manipulation and to reduce random scattered data access. The BTI provides a one-to-one correspondence between a basin and an outlet, and it maintains cells orderly in terms of both the elevation and the spatial distribution, as it is built by tracing the drainage path from the outlet to the source directly. This is achieved according to the drainage direction for each basin extracted from the DEM, where basins are divided based on watersheds. Therefore, the drainage accumulation can be calculated by traversing the BTIs from their leaves to roots linearly and simultaneously. These BTIs divide the entire study area into several basins that can be processed in isolation, reducing the search scope for basins and allowing the algorithm to efficiently utilize the main memory and decrease the data swapping between the main memory and the disk. A DEM for the Zhejiang Province in China was used to validate the results and compare the processing speeds. The results show that the algorithm provides the same calculation result as alternative algorithms but becomes more efficient as the volume of the DEM data increases. Furthermore, the BTI algorithm in this letter is easy to implement.