The vibration signal is essential for accurate event recognition along submarine cables. However, rapid and concise identification still poses challenges for prompt recognition and classification of events in harsh environments. For such reasons, this article proposes a developed hybrid model combining the modern power spectrum estimation (MPSE) and improved gradient neural networks (IGNNs), to identify and organize events instantaneously. Three different categories of data are selected as data samples, collected using the phase-sensitive optical time-domain reflectometry ( $\Phi $ -OTDR) device. The signal characteristics of the power distribution are extracted in the frequency and time domains. These characteristics together form the eigenvector, used in a multiclass IGNN classifier, to initiate the extent and position of collision events on the cable. The results demonstrate that the proposed hybrid model has a better identification rate, compared to the existing schemes used for the same purposes. It is verified that the accuracy rate of identification has expanded by almost 1%; confirming the validity and reliability of the proposed hybrid model.

The high efficiency, flexibility, and low cost of robots provide huge opportunities for the application and development of intelligent logistics. Especially during the COVID-19 pandemic, the non-contact nature of robots effectively helped with preventing the spread of the epidemic. Task allocation and path planning according to actual problems is one of the most important problems faced by robots in intelligent logistics. In the distribution, the robots have the fundamental characteristics of battery capacity limitation, limited load capacity, and load affecting transportation capacity. So, a smart community logistics service framework is proposed based on control system, automatic replenishment platform, network communication method, and coordinated distribution optimization technology, and a Mixed Integer Linear Programming (MILP) model is developed for the collaborative and persistent delivery of a multiple-depot vehicle routing problem with time window (MDVRPTW) of swarm robots. In order to solve this problem, a hybrid algorithm of genetically improved set-based particle swarm optimization (S-GAIPSO) is designed and tested with numerical cases. Experimental results show that, Compared to CPLEX, S-GAIPSO has achieved gaps of 0.157%, 1.097%, and 2.077% on average, respectively, when there are 5, 10, and 20 tasks. S-GAIPSO can obtain the optimal or near-optimal solution in less than 0.35 s, and the required CPU time slowly increases as the scale increases. Thus, it provides utility for real-time use by handling a large-scale problem in a short time.

The battery electric passenger vehicle (BEPV) has the potential to conserve electric energy and reduce carbon emissions, making it an effective tool for achieving low-carbon development in the road transport industry by replacing the internal combustion engine vehicle (ICEV). Several factors, such as comprehensive electricity power generation efficiency, proportion of thermal power, vehicle technical performance, regional mileage credibility and low temperature, affect the BEPV’s electricity energy consumption and carbon emissions. In this study, an electricity conservation index model and a carbon emission reduction index model for multilevel BEPVs are established to evaluate their capabilities of electricity energy conservation and carbon emissions reduction, considering the electricity supply chain, including the generation and transmission of electricity. The research shows that the electricity energy conservation ability of BEPVs is not outstanding, but their carbon emissions reduction ability is strong. When the composition of energy for electricity generation is transformed from 2025 to 2035, with a 10% increase in comprehensive electricity generation efficiency, all levels of BEPVs show fruitful electricity energy conservation ability. When the proportion of thermal power decreases to 10%, the carbon emissions reduction is exponentially reduced to 1/25 to 1/30 of ICEV’s total carbon emissions. However, the regional mileage credibility weakens the BEPVs’ ability to save energy and reduce emissions in most Chinese provinces except for the southwest and the south regional provinces, where the regional mileage credibility parameter can increase the energy conservation and carbon emission reduction performance of A00+A0 level BEPV. Low temperatures make BEPV models lose their electricity energy conservation advantage, but most models still have the characteristic of carbon emissions reduction. On this basis, the electricity energy consumption and carbon emissions of all BEPV models are higher than those of ICEVs when the low temperature endurance mileage accuracy is added.

With the rapid development of e-commerce, warehousing and logistics systems are facing the dual challenges of increasing order processing demand and green and low-carbon transformation. Traditional manual and single-robot scheduling methods are not only limited in efficiency, but will also make it difficult to meet the strategic needs of sustainable development due to their high energy consumption and resource redundancy. Therefore, in order to respond to the sustainable development goals of green logistics and resource optimization, this paper replaces the traditional mobile handling robot in warehousing and logistics with a composite robot composed of a mobile chassis and a robotic arm, which reduces energy consumption and labor costs by reducing manual intervention and improving the level of automation. Based on the traditional contract net protocol framework, a distributed task allocation strategy optimization method based on an improved genetic algorithm is proposed. This framework achieves real-time optimization of the robot task list and enhances the rationality of the task allocation strategy. By combining the improved genetic algorithm with the contract net protocol, multi-robot multi-task allocation is realized. The experimental results show that the improvement strategy can effectively support the transformation of the warehousing and logistics system to a low-carbon and intelligent sustainable development mode while improving the rationality of task allocation.