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The study of the distribution pattern of fire risk in ancient buildings can provide a starting point for emergency measures to be taken in case of possible fires. Based on the fire characteristics, system complexity, and geographic information attributes of large palace ancient building complexes, this paper adopts a fuzzy comprehensive evaluation method combining fuzzy mathematics and analytic hierarchy process to construct a fire risk evaluation system for ancient buildings, which includes six criteria and 28 indicators such as the value index, and the fire likelihood. For the evaluation method of this system, the expert scoring and Analytic Hierarchy Process are used to determine the weights of various indicators. Then, the multiple rounds of expert analysis with a review of relative literature, the membership degree of each indicator is analyzed one-by-one, and the final risk model, and risk factor are determined. By combining MHMapGIS technology, this comprehensive evaluation method was applied as an example to the Imperial Palace (large Ming and Qing ancient architectural buildings) in Beijing (China) for grid and visual analysis, and the rationality of the results were verified. The evaluation results can intuitively and reasonably show the distribution of fire risk, indicating that the constructed evaluation system and its model method display a certain of feasibility.

In response to the early warning requirements of fire security technology in the Imperial Palace & large Ming and Qing ancient architectural complexes in China, a grid based fire warning algorithm is proposed by combining neural network YOLOv5s smoke detection technology. In this algorithm, the inverse proportional gridding algorithm based on building density is used to optimize the grid of buildings, and compared with the results of the equidistant grid algorithm, the risk distribution division is more detailed and reasonable. The smoke detection part uses YOLOv5s based smoke detection technology to detect the distribution of fire smoke in various areas, and the positioning of this area in the overall grid realized by the remote transmission module. With detection experiments on relevant datasets, the results show that its accuracy and mAP both reach 0.99. By utilizing the collaborative effect of inverse proportional gridding algorithm and smoke detection technology, a grid based visualization of smoke warning is achieved.