Abstract: Objective　Formaldehyde, a key Group 1 carcinogenic pollutant, is the core driver of atmospheric photochemical pollution. With the increasing severity of global atmospheric environmental pollution and the growing public concern about air quality and health risks, the demand for high-precision real-time monitoring of atmospheric formaldehyde continues to significantly increase. Incoherent broadband cavity-enhanced absorption spectroscopy (IBBCEAS) has prominent advantages of high sensitivity and fast response in trace formaldehyde detection, but its practical engineering application is severely limited by the complex calibration and inversion workflow, as well as the long-term lack of a dedicated full-process integrated software systems. This study aims to develop a special software system for IBBCEAS-based formaldehyde mass concentration measurement device, to integrate the full core workflow of IBBCEAS measurement, realize rapid calibration of system parameters, real-time accurate formaldehyde detection, automated system performance evaluation and intuitive data visualization, and provide stable, reliable and efficient software support for the popularization and practical application of IBBCEAS technology in environmental formaldehyde monitoring. Methods　We focus on the formaldehyde mass concentration measurement device based on IBBCEAS, and present a dedicated full-process software system spectral optimization and analysis of formaldehyde using IBBCEAS (SOAFIBBCEAS) for IBBCEAS data processing and analysis. Firstly, based on the spectral characteristics of IBBCEAS in the ultraviolet band and the requirement for high-precision mirror reflectance calibration, the Rayleigh scattering differential method is employed to design a specular reflectance calibration module, and at the same time, spectral preprocessing including automatic dark spectrum subtraction and multi-time spectral averaging is introduced, thereby improving the accuracy of spectral data in low-light environments and realizing smooth and reliable calibration of the reflectances of highreflection mirrors. Secondly, by adopting the singular value decomposition (SVD) algorithm, a non-homogeneous linear equation set for concentration inversion is constructed based on the least square fitting principle of gas absorption crosssection and measured absorption coefficient, thus realizing efficient and high-precision inversion of formaldehyde mass concentration without relying on initial iteration parameters. Finally, based on the Allan variance analysis principle usually used for system performance evaluation, an automated detection limit analysis module is established utilizing automatic spectral data acquisition, grouping statistics and variance calculation. Meanwhile, based on Python language, PyQt5 and Django frameworks, the upper computer data acquisition software and data processing system are developed with Python- Seabreeze library for spectrometer communication and MySQL database for efficient data storage, completing the integration of all core functions of IBBCEAS measurement, and achieving full-process automated processing, real-time detection and visual display of formaldehyde spectral data. Results and Discussion　We conduct systematic validation and comparative analysis for the functional integrity, algorithm accuracy and operational performance of the proposed SOAFIBBCEAS software system, by adopting measured data from Maya2000Pro spectrometer, gradient concentration experimental data from standard gas distribution system, and public opensource dataset. Regarding data storage performance, a comparison is made between the MySQL-based storage scheme and traditional file storage schemes. For 1000 simulated storage operations, the proposed scheme reduces the total processing time from 66 s of the traditional approach to 39 s, achieving a 40.9% improvement in storage efficiency, and effectively eliminating the latency caused by frequent file operations. In terms of algorithm accuracy, the correlation coefficient R² between the formaldehyde mass concentration values inverted by the SVD-based algorithm and the dilution ratio of the gas distribution system is 0.9991, showing excellent linearity and inversion precision. For the detection limit analysis module, the calculation result of the proposed SOAFIBBCEAS software for the open-source dataset is 0.0245 μg/m³, which is highly consistent with the reference value of 0.0246 μg/m³, with a relative deviation within 0.41%. Furthermore, the proposed SOAFIBBCEAS software realizes smooth specular reflectance calibration in low-light ultraviolet environments, and stably completes the full workflow of spectrometer data acquisition, calibration, inversion and detection limit analysis, fully meeting the practical application requirements of IBBCEAS-based formaldehyde environmental monitoring. Conclusions　To address the complex calibration and inversion workflows, as well as the long-term lack of a dedicated fullprocess integrated software system for IBBCEAS-based atmospheric formaldehyde monitoring, this study developes a specialized SOAFIBBCEAS software system based on PyQt5 and Django frameworks. Key achievements of this study include: accurate and smooth mirror reflectivity calibration under low-light ultraviolet conditions by combining Rayleigh scattering differential method with targeted spectral preprocessing; high-accuracy formaldehyde mass concentration inversion with a correlation coefficient R² of 0.9991 using the SVD-based algorithm; reliable detection limit analysis with results highly consistent with the open-source reference dataset; and 40.9% improved data storage efficiency with the optimized MySQL-based storage scheme. The system adopts a modular architecture to separate upper computer acquisition from data processing, and a multi-thread design to ensure stable and continuous spectral acquisition without interface jams. The validation results confirm that SOAFIBBCEAS runs stably and completes the full IBBCEAS measurement workflow accurately, fully meeting the practical application requirements of environmental formaldehyde monitoring. In the future, this system can be extended to IBBCEAS detection of other atmospheric trace gases, providing a universal software solution for trace gas monitoring based on this technology.

Key words: incoherent broadband cavity-enhanced absorption spectroscopy, software system, Rayleigh scattering difference approach, singular value decomposition