Abstract: A route planning method for large-scale capacitated arc routing problem is disclosed, belonging to the field of combinatorial optimization. The present disclosure firstly performs global optimization and proposes a low-cost decomposition optimization solution based on CARP, which pertinently preserves more excellent decompositions during iterations. And, the present disclosure is also applied to a local search stage, and proposes an improved route construction rule. In a process of route insertion, a problem of excessive useless cost caused by a vehicle with almost full load returning to a depot is considered. After improvement, local search can be carried out more effectively, thus further improving the solution quality. Compared with an existing route planning method, the present disclosure considers details and characteristics of CARP optimization problems in a more detailed manner, thus achieving solutions with a lower cost and improving the stability by about two to three times.

Abstract: The present invention provides a text classification backdoor attack method, system, device and a computer storage medium. The method includes: training a pretraining model by using a clean training set to obtain a clean model; generating a pseudo label data set by using a positioning label generator; performing multi-task training on a Sequence-to-Sequence model by using the pseudo label data set to obtain a locator model; generating a backdoor data set by using the locator model; and training the clean model by using the backdoor data set to obtain a dirty model. A pseudo label data set is generated by using a pretrained clean model without manual annotation. A backdoor attack location in a text sequence may be dynamically predicted by using a locator model based on a Sequence-to-Sequence and multi-task learning architecture without manual intervention, and a performance indicator obtained by dynamically selecting an attack location is better.

Abstract: The present disclosure discloses a deep spatial-temporal similarity method for air quality prediction, and belongs to the technical field of environmental protection. When the method predicts air quality-related indexes of a target site, a temporal change of air pollution and a spatial diffusion relationship are effectively combined, and then spatial-temporal similarity sites of the target site are selected; air quality monitoring data collected by the target site, the spatial-temporal similarity site of the target site and geographical neighbour sites of the target site and meteorological data are respectively taken as inputs of a long short term memory network (LSTM) model to obtain uncorrelated output results, and then predicted values of air quality-related index data of the target site are obtained in a mode of support vector regression (SVR) integration.

Abstract: The present disclosure discloses a deep spatial-temporal similarity method for air quality prediction, and belongs to the technical field of environmental protection. When the method predicts air quality-related indexes of a target site, a temporal change of air pollution and a spatial diffusion relationship are effectively combined, and then spatial-temporal similarity sites of the target site are selected; air quality monitoring data collected by the target site, the spatial-temporal similarity site of the target site and geographical neighbour sites of the target site and meteorological data are respectively taken as inputs of a long short term memory network (LSTM) model to obtain uncorrelated output results, and then predicted values of air quality-related index data of the target site are obtained in a mode of support vector regression (SVR) integration.