Automated Guided Vehicle (AGV) indoor autonomous cargo handling and commodity transportation are inseparable from AGV autonomous navigation, and positioning and navigation in an unknown environment are the keys of AGV technology. In this paper, the extended Kalman filter algorithm is used to match the sensor observations with the existing features in the map to determine the accurate positioning of the AGV. This paper proposes an improved joint compatibility branch and bound (JCBB) method to divide the data and then randomly extract part of the data in the divided data set, thereby reducing the data association space; then, the JCBB algorithm is used to perform data association and finally merge the associated data. This method can solve the problem of the increased computational complexity of JCBB when the amount of data to be matched is large to achieve the effect of increasing the correlation speed and not reducing the accuracy rate, thereby ensuring the real-time positioning of the AGV.

Cities are the core carriers and key positions to achieve the dual carbon goals. It is of great significance to explore whether promoting urbanization can improve carbon emission performance, which is of great significance to comprehensively promote the goal of carbon neutrality. Based on the panel data from 2006 to 2021, this paper analyzes the spatial autocorrelation of carbon emission performance per unit space and the impact mechanism of urbanization process on it. The fixed-effect model was further used to identify the influencing factors of spatial carbon emission performance. The results show that: 1) China’s carbon emission performance per unit space is declining year by year. 2) There is a strong positive spatial correlation and stable path dependence on the performance of carbon emissions per unit space in each region. 3) To a certain extent, increasing the level of urbanization will reduce the carbon emission performance per unit space. 4) The urbanization process has a spatial spillover effect on the carbon emission performance per unit space of surrounding provinces, and the spatial spillover effect of industrial structure and energy consumption structure is more obvious than that of economic level, population density and urbanization rate. Based on the conclusions, this paper puts forward specific policy suggestions to reduce the carbon emission performance per unit space to help the low-carbon development of cities.

This article investigates the application and physical mechanism exploration of distributed collaborative optimization algorithms in building multi-energy complementary energy systems, in response to the difficulties in coordinating various subsystems and insufficient dynamic control strategies. On the basis of modeling each subsystem, the Dual Decomposition algorithm is used to decompose the global optimization problem of the system into several independent sub problems, achieving independent optimization of each subsystem. Through an adaptive dynamic scheduling strategy, real-time data and predictive information are continuously updated and controlled, effectively allocating system resources. The experimental results show that compared to the original system before optimization, the improved algorithm in this paper reduces the total energy consumption of the system by 6.9% and 2.8% on typical summer and winter days, respectively. The conclusion shows that the algorithm proposed in this paper can effectively solve the problem of system coordination difficulties, improve system resource allocation and overall operation level, and provide a new perspective for the optimization design and operation control of energy systems.

Methane exceedance events in underground coal mines are often associated with progressive multisensor abnormal evolution processes involving operational, ventilation, environmental, and methane-drainage subsystems. Existing studies primarily focus on methane concentration prediction and provide limited interpretability regarding how abnormal evolution patterns emerge before threshold exceedance. To address this limitation, this study proposes an interpretable event-driven multisensor risk-evolution analysis framework for methane early warning. Methane exceedance events are first extracted from multisensor monitoring data, and a continuous multisensor risk representation together with a persistence-based trigger mechanism is developed to identify sustained abnormal evolution prior to methane exceedance. Event-specific temporal dependency networks are then constructed using lagged dependency analysis to characterize multisensor interaction structures within event windows. Representative evolution paths and recurrent critical variables are further identified to reveal interpretable abnormal evolution patterns. Experiments conducted on a real underground coal mine monitoring dataset containing 784 methane exceedance events demonstrate that the proposed framework achieved the highest early-warning performance among all compared baselines. Under the Any-Target-Sensor criterion, the framework attained an effective warning rate of 0.848 and significantly outperformed benchmark methods in event-level McNemar tests (p < 0.001). The results further indicate that methane exceedance events are generally associated with structured multisensor abnormal evolution processes rather than isolated methane fluctuations, providing an interpretable system-level perspective for methane early warning.