Terrorists often take the chemical clusters as the attacking target because of the adverse impacts of a chemical accident on society and the environment. In addition to some fixed countermeasures, previous studies have verified the feasibility of a patrol in addressing adversarial attacks. However, the previous patrolling practices fail to tackle the terrorist attacking problems in a large-scale area cost-effectively. To further tackle the protection issue with cost-beneficial solutions in a large-scale scenario, i.e., in a chemical cluster, we propose an area-partition-involved collaborative patrolling (APCP) game. We first leverage the proposed greedy deployment algorithm to determine the initial deployment of defenders (patrollers), including the quantity and position of patrol vehicles. Then, the large-scale area is partitioned into multiple smaller areas by using the collaborative idea of static partitioning. In the meantime, corresponding patrolling graphs are constructed based on graphic modeling methods. Finally, the APCP game is built between patrol vehicles (namely defender) and potential terrorists (namely attacker), in which patrol vehicles aim at detecting attack behaviors of terrorists by intelligently scheduling the patrolling routes. After formalizing the problem into a sequential game, we compute the Stackelberg equilibrium through the MultiLPs algorithm. Through case studies of three practical chemical cluster scenarios, the results explicitly show the superiority of our proposed APCP game by saving up to 25.48 % patrolling costs in a one-shot game compared to the results before partition. As for the collaborative patrolling problem in a large-scale area, the methods and models proposed in this paper can facilitate the management department of chemical clusters with intelligently scheduled patrolling routes, which can effectively reduce the cost of patrollers, and better protect the chemical cluster.

Estimating gas source terms is essential and significant for managing a gas emission accident. Optimization method, as a kind of estimation methods, is helpful to figure out the source terms by solving the inverse problem. Significantly, the performance of optimization method on source term estimation is affected by the accuracy of forward dispersion model. To enhance the estimation accuracy, previous works have demonstrated the feasibility of using Back Propagation Neural Network (BPNN) trained by actual experimental datasets as a forward dispersion model. However, the overall accuracy of source estimation is still limited by backward estimation methods. Most related studies used a single optimization algorithm to estimate source terms, which usually fails to realize the requirements of both high calculation accuracy and satisfying computational efficiency. Therefore, a hybrid strategy was proposed in this study to combine optimization algorithms with different characteristics, including particle swarm optimization, genetic algorithm and simulated annealing algorithm, to not only achieve high accuracy in global searching, but also converge to a stable result efficiently. Finally, extensive experiments are conducted to testify our proposed hybrid optimization algorithms. The Skill scores of hybrid optimization algorithms decrease obviously compared to those of single optimization algorithm. Hence, the proposed hybrid strategy is potentially useful for guiding the combination of optimization algorithms for gas source terms estimation, which further contributes to deal with a gas emission accident with satisfying calculation accuracy and computational efficiency.