## 1 Introduction  
Lithium (Li) is one of the rare metals associated with coal, which has extremely high strategic value. Li mining in coal is expected to become an important way to supplement Li [1; 2; 3; 4; 5]. XRD is a common phase analysis method, which can be used to identify Li in coal. The diffraction pattern is related to the phase, and different patterns are due to different phases. The phase composition of the sample can be determined by comparing the diffraction pattern with the standard powder diffraction file (PDF), and the location of the diffraction peak is one of the main indicators for comparison [6; 7].  
Spectral line peak search is the prerequisite of qualitative and quantitative analysis in nuclear, spectral, and chromatographic analysis, and the optimization of peak search algorithms is an inevitable requirement for the development of the spectral analysis field. Common algorithms include the comparison method, derivative method (first order, second order, third order), covariance method, etc. [8; 9; 10; 11]. The evaluation of peak searching algorithms mainly focuses on their ability to identify overlapping peaks and weak peaks. By constructing a symmetric \"window\" function with zero area, used for convolution transformation of the spectral data, and then using threshold screening to determine peak position, SZAC performs well in the above two aspects [12; 13].  
SZAC was derived from the second derivative method. [PERSON] et al. [14] gave a method to determine the conversion function and compared the peak search effect under different functions. The results show that a \"wide\" window conversion function can suppress high base and statistical false peaks, and a \"narrow\" window conversion function can improve the ability to distinguish overlapping peaks. [PERSON] et al. [15] discussed and compared the principles of various peak searching algorithms for X-ray fluorescence spectra, and confirmed that the Gaussian fitting method and SZAC were ideal algorithms, and SZAC was especially effective in identifying weak peaks superimposed on high substrates. [PERSON] et al. [16] took LIBS/Raman spectra as the research object to explore the recognition effect of peak-like conversion functions constructed by three different linear functions they pointed out that SZAC has strong adaptability to large-scale dynamic changes of signals, and its recognition ability of weak peaks reaches or is better than that of manual recognition. In general, research of SZAC has a certain basis. However, the above studies all analyzed a single determined spectrum and set parameters according to the research object. If the spectral data is changed, the parameters need to be greatly adjusted to achieve a better peak search effect. The strong dependence on the source spectrum and experience results in the limited use and reduced applicability of SZAC. This article aims to obtain an improvement method, ISZAC, by combining multi-source data fusion, which is effective for most of the sample without changing parameters.  
This article elaborates the principle of SZAC and analyzes its limitations in two aspects. On the basis of explaining the idea of passive anti-counterfeiting and data fusion, ISZAC is proposed and its implementation steps are analyzed. Finally, the accuracy and applicability of ISZAC are verified by a peak searching experiment, and the actual peak searching effect of ISZAC on lithium in coal XRD patterns is tested.