## 1 Introduction  
With the development of communication technology and control theory, networked systems have been widely used in a variety of fields, such as distributed power systems, remote surgery, factory automation, and unmanned aerial vehicles [1; 2; 3; 4; 5]. Unlike traditional point-to-point systems, their feedback control exists within a network loop. We refer to such systems as networked control systems (NCSs). NCSs can be used not only for long-distance control systems, but also to greatly reduce unnecessary cabling as signals are transmitted through the network. Due to the limited precision and cost of sensors, as well as network bandwidth constraints, the information collected by sensors needs to be quantized before transmission [6]. The quantization process reduces the amount of information in the data, increasing the difficulty of identification, making research on the identification of quantized systems highly significant. [PERSON] et al. [7] studied the recursive identification of finite impulse response (FIR) systems, designed the algorithm using stochastic approximation under binary quantization and unreliable communication, and evaluated its convergence performance. [PERSON] et al. [8] discussed the identification algorithm for FIR systems where both input and output are quantized, proposing a maximum likelihood-based recursive algorithm for data loss scenarios. [PERSON] et al. [9] explored the adaptive tracking problem in strict-feedback switched nonlinear systems with quantized inputs, providing new solutions to enhance system robustness and tracking precision. [PERSON] et al. [10] discussed the security issues in identifying FIR systems with binary observations and provided an optimal defense strategy. [PERSON] et al. [11] considered the identification of nonlinear systems with Hammerstein structures and employed a Gaussian process to model the system impulse response. [PERSON] et al. [12] investigated defense against data-tampering attacks within the framework of system identification with quantized observations and designed the corresponding identification algorithm. For short sampling periods, the amount of data to be transmitted significantly increases, leading to a need for greater bandwidth and more efficient transmission channels. Thus, saving communications resources is essential, and event-triggered mechanisms are an effective solution. [PERSON] et al. [13] presented a discrete event-driven mechanism, studied the event-driven \(H_{\infty}\) control of networked singular systems with state and input quantization, and further analyzed the performance. In [PERSON] et al. [14], an event-triggered control for nonlinear systems with intermittent packet transmission was investigated, and it was demonstrated through a numerical example that the method substantially reduced the number of transmissions compared to periodic sampling. [PERSON] et al. [15] introduced an event-triggered communication scheme for binary-valued observations, to save communication resources; they proposed an algorithm for estimating unknown parameters based on weighted least-squares optimization and discussed the trade-off between average communication rate and estimation performance. [PERSON] et al. [16] researched the remote state estimation for linear discrete-time invariant systems with event-driven mechanisms introduced by communication, employing Gaussian-conserving, event-based sensor scheduling to optimally balance communication costs and estimation accuracy. [PERSON] and [PERSON] [17] introduced new adaptive tracking methods for systems within two variants of event-driven architectures. NCSs bring numerous conveniences by incorporating communication networks, but a network is not a reliable communication medium. Data packet loss, delays, and sequence disorders inevitably occur during network transmission [18]. It is a struggle to directly apply traditional control theories to the analysis of NCSs, due to these issues.