Coal is an important energy resource in China and has long been the primary source of energy consumption. However, most coal resources are buried underground, posing significant safety challenges for underground coal mining. Moreover, the high incidence of coal mine accidents has adversely affected the development of the coal economy. To effectively enhance rescue outcomes in coal mine accidents, it is crucial to quickly and accurately locate trapped individuals and devise efficient rescue strategies. Big data analysis underscores these priorities as pivotal for significantly improving rescue operations. However, the underground environment of coal mines is complex and variable, which makes it difficult for traditional positioning systems to attain high accuracy. Therefore, achieving high-precision positioning of underground personnel has become an urgent problem to be solved. Based on this, we are dedicated to researching a new photodiode (PD) array receiver and installing it on miners’ helmets to achieve high-precision positioning of underground workers in this study.

Firstly, we delve into how differently shaped PD array sensors affect the performance of the visible light positioning system in underground coal mines. The arrays are classified into a square 2×2 array, square 3×3 array, circular PD array (both square and ellipsoid), and umbrella PD array (both square and ellipsoid) based on their arrangement. Next, the neural network parameters are discussed to determine the optimal settings for positioning. Simulations of PD array sensors with the aforementioned arrangements are conducted to compare their positioning performance. Finally, a PD array receiver with optimal performance is selected to implement the visible light positioning system for underground coal mine operations.

We first discuss the network parameters of the SRU neural network to determine the optimal settings. The initial step involves simulating the square-type PD array receiver, where it is observed that the 3×3 square-type PD array receiver outperforms the 2×2 counterpart (Table 6). It is established that increasing the number of PDs improves the system’s ability to receive comprehensive information about the light source, thereby reducing positioning errors. Consequently, an array configuration of nine PDs is selected. Next, the positioning performance of the circular PD array receiver and the umbrella PD array receiver are separately considered and simulated to obtain their respective results (Tables 7 and 8). By comparing these simulation outcomes, it is determined that the elliptical umbrella PD array receiver exhibits optimal positioning performance under similar environmental configurations. Finally, the positioning algorithm based on the SRU neural network is compared with that based on the sparrow search algorithm optimized deep confidence network. It is found that the SRU neural network-based algorithm demonstrates superior positioning performance, which confirms the efficacy of the proposed algorithm in this study.

Our study focuses on designing and optimizing a visible light positioning system using PD array sensors for underground coal mines. Initially, the PD array sensor is selected as the primary light sensing component, and its structural characteristics and working principles are thoroughly studied to ensure high sensitivity and accuracy. Various PD array configurations (square, circular, and umbrella PD arrays) are introduced and analyzed in terms of their characteristics and suitable applications. Further discussed are network parameters of the neural network, followed by the construction of a comprehensive visible light positioning system tailored for underground coal mines. This system utilizes advanced SRU neural network technology and principles of visible light positioning to achieve precise localization of targets. Several sets of simulation experiments confirm an average positioning error of 0.94 cm within a 3.6 m×3.6 m×3 m space, with a training time of 1 s, meeting the requirements for underground positioning in coal mines.