This paper investigates the event-triggered distributed filtering problem for a class of linear systems with additive and multiplicative noises transmitted over sensor networks,in which the considered process noise and measurement noise exhibit one-step autocorrelation and two-step cross-correlation characteristics.Firstly,a recursive equation is used to describe the dynamic bias of the system,and random variables following a Bernoulli distribution are introduced to characterize the random packet loss phenomenon.Secondly,an event-triggered mechanism is introduced to reduce the information transmission frequency while ensuring filtering performance,and a novel consistency-based distributed filter is constructed.Then,a recursive equation for the upper bound of filtering error covariance is established using stochastic analysis techniques,and an expression for filtering gain is derived by minimizing the variance constraint index.Finally,the effectiveness of the proposed optimized filtering method is verified through numerical simulations.