## 1 Introduction  
With the increasing complexity of coal mining, the traditional mechanized and automatic mining technology can no longer meet the requirements of the coal industry to improve further. Therefore, intelligent coal mining has become inevitable [1]. As an important part of many pieces of equipment in a comprehensive working face, the scraper conveyor not only undertakes the important task of coal loading and transportation but also provides an operation track for the shearer. Therefore, realizing intelligent perception and decision-making of scraper conveyor straightness has become a key technical problem to solve the stope 'three machines' (hydraulic support, shearer, conveyor) equipment attitude monitoring. Straghness perception and straightening methods of scraper conveyors commonly used in the field are manual straightening and direct observation methods. In 2003, the Australian Federal Academy of Sciences (CSIRO) proposed a set of automated coal mining technology--LASC based on strapdown inertial navigation technology [2]. Since the shearer runs along the scraper conveyor, the spatial geometry of the scraper conveyor can be measured by the position of the shearer. Subsequently, the Landmark project carried out by CSIRO proposed the idea of inverting the shape of the scraper conveyor by detecting the three-dimensional path of the shearer through strapdown inertial navigation technology [3]. The position of the shearer has been accurately measured using an inertial navigation system (INS) or inertial measurement unit (IMU) [4; 5]. [PERSON] et al. [6] proposed an integrated inertial navigation system aiming at various drifts and errors of the inertial navigation system in applying inertial navigation systems based on open-pit coal mines. [PERSON] et al. [7] deduced the error compensation model of a strapdown inertial navigation system (SINS) for shearers based on vibration characteristics by establishing the dynamic model of double-drum shearers. Finally, the simulation analysis proved that the error compensation model of SINS using a three-sample algorithm and a four-sample algorithm could improve the calculation accuracy of a shearer strapdown inertial navigation system. [PERSON] et al. [8] proposed a closed path optimal estimation model based on the motion characteristics of the shearer and Kalman filter (KF). Through simulation experiments, the positioning accuracy of the shearer is improved by about one order of magnitude without using external sensors. [PERSON] et al. [9] constructed a geometric measurement model of the scraper conveyor based on the IMU installed on the shearer and developed a measuring instrument using the IMU and the axial encoder to meet the requirements of the straightness of the longwall face. [PERSON] et al. [10] used the dynamic graph convolutional neural network (DGCNN) to extract the geometric features of the sphere in the point cloud of the fully mechanized mining face (FMMF) to obtain the position of the sphere (mark) in the FMMF point cloud, which can provide an effective basis for the straightening and inclination adjustment of the scraper conveyor. By analyzing the movement law of the floating connection mechanism between the hydraulic support and scraper conveyor, [PERSON] et al. [11] established a Coal Seam + Equipment Joint Virtual Straightening System. In addition, the straightening simulation of the scraper conveyor was performed on the complex coal seam floor. In the late 1970s, countries worldwide began to study the new sensing technology based on fiber Bragg grating (FBG). [PERSON] et al. [12] first applied optical fiber to a concrete structure for structural detection. Since then, it has entered the upsurge of optical fiber sensing technology application, opening the era of optical fiber application theory research [13; 14]. In the past few decades, FBG has been widely used in the measurement of strain [15; 16], pressure [17; 18], tilt angle [19], and temperature [20].