The central region is an important strategic area that encompasses the east and the west and connects the south and the north. Promoting high-quality urban development in the central region plays a positive role in comprehensively upgrading the central rising strategy and realizing coordinated regional development. Based on the measurement index system result of the level of high-quality urban development in the central region, this study describes the regional gap and its dynamic evolution through the Dagum Gini coefficient and the kernel density function. In addition, it analyzes the causes of the gap in high-quality development of cities in the central region from the perspective of problem area identification. The result shows that the overall high-quality development of cities in the central region is increasing, with high-level cities clustering around the core cities. The relative regional disparities continue to narrow, but the absolute differences tend to expand. The super-variable density tends to be the main source of the overall difference, and the high-quality development of cities in each region is positively spatial correlated with each other. At present, the lagging economic development and outcomes sharing are the main obstacles to the high-quality development of cities in the central region.

This paper investigates the adaptive consensus problem of general linear multi‐agent systems. Finite‐time consensus and fixed‐time consensus are both discussed. In order to remove the dependence on global information about system topology, distributed adaptive mechanisms are introduced. Compared with the existing asymptotic consensus results, the system's convergence speed is improved by the proposed distributed finite‐time consensus protocol. Furthermore, a distributed fixed‐time consensus protocol is proposed to remove the dependence on system's initial state. Under the fixed‐time consensus protocol, it is proved that an upper bound of the system's convergence time can be estimated. Sufficient conditions are given to ensure that all agents can achieve consensus in finite time and fixed time, respectively. Finally, the effectiveness of the proposed protocols is illustrated by a simulation example.