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The invention provides a method for obtaining the height of a point cloud pavement in Unity. The method comprises the steps of obtaining a point cloud map of a whole scene, segmenting the point cloudmap to obtain a plurality of blocks, carrying out point cloud height calculation on each block of point cloud, outputting a result and the like, and exporting height information of each block of pointcloud to obtain height information required by a road surface. According to the method, the advantages of the Unity engine are fully utilized; the point cloud ground height information can be rapidlyand accurately generated, data flow processing is accelerated by utilizing some parallel processing methods, the point cloud road surface height information and subsequently added road surface elements can be automatically obtained, the point cloud road surface height information and subsequently added road surface elements can be automatically attached to the road surface, the workload requiredby manual adjustment is greatly reduced, and the probability of personal errors is also reduced.

The invention relates to the technical field of image processing, in particular to an extraction method of image texture element feature a local shape mode based on a topological tree. The method comprises the steps of: carrying out Level Set layering on an image I according to a pixel gray value v, structuring a topological tree structure T, structuring an encoded concentric circle template, scaling node shapes to be equivalent to the size of the concentric circle template, overlapping the node shapes subjected to scaling with the concentric circle template and carrying out binary coding on the node shapes subjected to scaling with the overlapping relationship of each sector fnm, counting a frequency column diagram based on coded values of all the node shapes in all the M sectors of each circle and then splicing the frequency column diagrams of the N circles, and adding all the texture feature descriptions of the shapes subjected to coding in the image topological tree. The invention can avoid the loss of texture information in the process of filtering and transformation, carry out description on the image texture more comprehensively and completely, improve the accuracy of image processing applications based on the texture element features, such as retrieval, classification, partitioning and the like,.

The invention discloses a method for discriminating power oscillation types based on multi-dimensional characteristics of a power system, by calculating time-domain indicators, frequency-domain indicators, energy indicators, cross-correlation indicators, autocorrelation indicators, sample entropy indicators and modal indicators for low-frequency oscillation signals , a relatively complete index set is established, which can describe the characteristic information of power system oscillation more completely. The present invention uses a mutual information feature selection method. Compared with the widely used Fisher discriminant method, the mutual information feature selection can measure the nonlinear relationship between variables. Using the features obtained by the mutual information feature selection method for model training can help improve the generalization ability of the training model and reduce the complexity of the training model, thereby effectively preventing over-fitting. The invention uses a machine learning classifier to identify the power system power oscillation event type, and compared with the traditional classification method, it can effectively improve the classification accuracy and the generalization ability of the training model.

The invention discloses an optimization system for a virtual and actual interactive traffic evacuation control scheme. The system comprises a database, a model base, an actual traffic system, a virtual traffic system, a decision supporting platform and an evacuation scheme base, wherein the virtual traffic system synchronously interacts with the actual traffic system, so that parallel description of a traffic evacuation environment is realized; and the decision supporting platform comprises an evacuation scene generation module, a weather scene generation module, an evacuation scheme generation module and an evacuation result evaluation module and is used for finishing a traffic evacuation experiment function during a large activity, generating a traffic evacuation scene under normal and abnormal weather conditions and evaluating the an evacuation scheme effect. The optimization system supplies decision support to a traffic evacuation process through interaction between the virtual system and the actual system.