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Improving the essential safety level of cables is an important basis to ensure the reliability of urban electricity consumption. According to the summary report of the cable operation of state grid corporation of China, aiming at the “six prevention” problem of cable, this paper puts forward that the key to improve the essential safety level of cable and channel is to solve the “fire prevention” and “external damage prevention” of cable. Based on the analysis of the current “fire protection” technology of cables, the Suggestions of developing a new type of nanometer fire retardant coating and a new type of cable intermediate head protection box are put forward. It is pointed out that the key to improve the ability of “external damage prevention” is to use the method of intelligent operation and inspection instead of the traditional manual patrol. At the same time, combining with the big data analysis method to analyze the historical fault data, it can also solve the cable life evaluation and cable fault hidden trouble troubleshooting problems.

High voltage cable is an important basic equipment for city power supply. Existing research shows that: the method of digital X-ray imaging can realize the non-destructive detection of power cable, it belongs to the new detection technology in the field of cable defect detection, at present, there is no deep X-ray digital image processing and defect recognition method, and it can not directly detect the cable ontology and defect recognition from the original digital image. Therefore, this article on the depth of power cable X-ray digital image processing and intelligent recognition technology carried out research, buffer layer defects using gray processing technology, the original image grayscale range compression to the human eye can recognize, and defect identification, and the traditional convolution neural network and the convolution neural network (CNN) FCN image data for training, implementation of the intelligent identification of power cable buffer layer defects. The results show that compared with traditional convolutional neural network (CNN), the full convolutional neural network (FCN) proposed in this paper has a clearer and more intuitive recognition effect.

As a component of the Internet of things, high-voltage cables are the power supply infrastructure for the modern development of cities. The operation experience shows that the high-voltage cable has been broken down many times, due to the defective operation. At present, due to the limitation of detection technology, the research on detection and identification of defects in high-voltage cables is progressing slowly. Therefore, a new DR technology based on X-ray digital imaging is proposed in this paper to realize real-time detection of defects in the semi-conductive buffer layer of high-voltage cables, and intelligent detection of DR images of high-voltage cables by using image depth processing technology to realize intelligent identification of defects in the buffer layer of power cables. The results show that using the new DR technique proposed in this paper, the accurate and intuitive DR image of high-voltage cable can be obtained quickly, and the intelligent identification of defects can be realized.

The original X-ray image of the high voltage XLPE insulated cable has low contrast due to its complexity and the limited imaging conditions. Therefore, an X-ray image processing method is proposed to address the high voltage cable’s buffer layer defect detection. One hundred seventy-seven high-resolution images from a cable tunnel are collected. The results indicated that our method could efficiently detect buffer layer defects.

Cable channel fire is an important factor affecting the safe operation and maintenance of power cables. This paper summarizes the current research situation of fire-extinguishing technology, analyzes the fire-extinguishing technology suitable for cable channel, and proposes to use S-type aerosol fire-extinguishing technology to extinguish the fire of cable channel..

With the rapid modernization of the city, power cable has been widely used in the process of urban construction. In the course of cable operation, cable faults become more and more frequent, which has a great impact on national economy and people’s life. The method of digital X-ray imaging can realize the nondestructive testing of power cable body and obtain clear and intuitive X-ray digital image. But it lacks the advanced processing and defect recognition method of X-ray digital image, and can not directly detect and identify the cable body and defect from the original digital image. Therefore, this paper studies the power cable X-ray digital image advanced processing and buffer layer defect intelligent identification technology. By using gray scale processing technology, the original image gray scale range is compressed to the human eye identifiable range, and then the defect identification is carried out. Then the traditional convolution neural network CNN and the full convolution neural network FCN are used to train the image data to realize the intelligent recognition of the power cable buffer layer defect. The research shows that compared with the traditional convolution neural network CNN, the full convolution neural network FCN proposed in this paper has more clear and intuitive recognition effect.