## 1 Introduction
[13] validated a recently developed cross model cross mode (CMCM) model updating the method for an offshore platform structure by using experimental data. [PERSON] and [PERSON] [14] presented an algorithm method based on the CMCM in combination with an iterative procedure along with introducing a simplified method that is named Pseudo simplified (PS) model technique. [PERSON] et al. [15] represented a methodology using the CMCM method in combination with an iterative procedure which uses limited, spatially incomplete modal information. [PERSON] et al. [16] proposed a new Bayesian model updating framework using incomplete frequency response function (FRF) data. In this methodology, the amount of data in the objective function is increased using FRF at different excitation frequencies to reduce the detrimental impacts of uncertainties on model updating and damage detection. [PERSON] and [PERSON] [17] proposed a combined method of cross correlation and principal component analysis (PCA) to detect the damage under the influence of wave excitations. [PERSON] et al. [18] proposed a combination method of GNSS and accelerometer to monitor the vibration of platform structures, which could complement each other's advantages and compensate for their shortcomings in the dynamic deformation monitoring of a structure. In the past few years, intelligent algorithm also has been used in numerous vibration-based structural damage detection approaches, e.g., genetic algorithm, artificial neural network (ANN), convolutional neural network (CNN), fuzzy logic system (FLS), long and short-term memory (LSTM), et al., which usually lead to considerable computational complexity problem for the damage detection in offshore platform SHM operations [19]. [PERSON] et al. [20] presented that an automated vibration monitoring method of detecting the occurrence and location of damage in offshore jacket platforms used artificial neural networks. [PERSON] et al. [11] used FLS and probabilistic analysis in SHM for offshore jacket platform. [PERSON] et al. [21] used substructural identification and genetic algorithms to damage address on offshore jacket platforms. [PERSON] et al. [22] proposed a health monitoring method for analyzing offshore wind power structures based on a genetic algorithm and an uncertain analytic hierarchy process (AHP). Both [PERSON] et al. [23] and [PERSON] et al. [24] developed CNN-LSTM combination method for model frequency identification used in SHM, and obtained good results. [PERSON] et al. [25] proposed an SHM method combining the random decrement technique (RDT) with long and short-term memory (LSTM) networks for offshore structures. [PERSON] et al. [26] developed an advanced risk-based maintenance (RBM) methodology for prioritizing maintenance operations by addressing fluctuations that accompany event data. There are already many monitoring systems. However, the current work either focuses on the marine environment and load monitoring, or focuses on the direct measurement of displacement, deformation, strain, and other structural responses. In the design of this monitoring system, more attention was paid to the selection and layout of sensors and the acquisition of monitoring data, and little was involved in the further professional application of data. That is, effective structural state assessment methods and safety early warning strategies are proposed based on the monitoring data, there is also a lack of a mature monitoring and evaluation system to fully grasp the time-varying performance of offshore platform structures. In this study, a complete structural health monitoring system is designed, and a jacket offshore platform is taken as the monitoring object for example. Methods for providing early warnings of safety hazards and evaluation of the overall performance of marine structures were proposed and the results are presented.