Current fire and smoke detection methods mainly rely on site inspection by staff， which results in low efficiency and poor real-time performance， so an efficient fire and smoke detection algorithm for complex scenarios based on YOLOv5s， called YOLOv5s-MRD （YOLOv5s-MPDIoU-RevCol-Dyhead）， was proposed. Firstly， the MPDIoU （Maximized Position-Dependent Intersection over Union） method was employed to modify the border loss function， thereby enhancing the accuracy and efficiency of Bounding Box Regression （BBR） by adapting to BBR in overlapping or non-overlapping scenarios. Secondly， the RevCol （Reversible Column） network model concept was applied to reconstruct the backbone of YOLOv5s， transforming it into a backbone network with multi-column network architecture. At the same time， by incorporating reversible links across various layers of the model， so that the retention of feature information was maximized， thereby improving the network’s feature extraction capability. Finally， with the integration of Dynamic head detection heads， scale awareness， spatial awareness， and task awareness were unified， thereby improving detection heads’ accuracy and effectiveness significantly without additional computational cost. Experimental results demonstrate that on DFS （Data of Fire and Smoke） dataset， compared to the original YOLOv5s algorithm， the proposed algorithm achieves a 9.3% increase in mAP@0.5 （mean Average Precision）， a 6.6% improvement in prediction accuracy， and 13.8% increase in recall. It can be seen that the proposed algorithm can meet the requirements of current fire and smoke detection application scenarios.

Compared with ordinary networks， hypernetworks have complex tuple relationships， namely hyperedges. However， most existing network representation learning methods cannot capture the tuple relationships. To solve the above problem， a Heterogeneous hypernetwork Representation learning method with Hyperedge Constraint （HRHC） was proposed. Firstly， a method combining clique extension and star extension was introduced to transform the heterogeneous hypernetwork into the heterogeneous network. Then， the meta-path walk method that was aware of semantic relevance among the nodes was introduced to capture the semantic relationships among the heterogeneous nodes. Finally， the tuple relationships among the nodes were captured by means of the hyperedge constraint to obtain high-quality node representation vectors. Experimental results on three real-world datasets show that， for the link prediction task， the proposed method obtaines good results on drug， GPS and MovieLens datasets. For the hypernetwork reconstruction task， when the hyperedge reconstruction ratio is more than 0.6， the ACCuracy （ACC） of the proposed method is better than the suboptimal method Hyper2vec（biased 2nd order random walks in Hyper-networks）， and the average ACC of the proposed method outperforms the suboptimal method， that is heterogeneous hypernetwork representation learning method with hyperedge constraint based on incidence graph （HRHC-incidence graph） by 15.6 percentage points on GPS dataset.

In view of the scenarios where the resources of cloud terminal users are limited， the traditional attribute based encryption schemes have the disadvantages of high computing cost and being unable to achieve real-time revocation. In order to realize the safe and efficient sharing of cloud data， an attribute based encryption scheme based on Elliptic Curve Cryptography （ECC） algorithm and supporting fine-grained revocation was proposed. In the scheme， the relatively lightweight scalar multiplication on the elliptic curve was used to replace the bilinear pairing with higher computational cost in the traditional attribute based encryption schemes， thereby reducing the computational cost of users during decryption in the system， improving the efficiency of the system and making the scheme more suitable for resource constrained cloud terminal user scenarios. In order to reduce the redundant attributes embedded in the ciphertext to shorten the length of the ciphertext， the more expressive and computationally efficient Ordered Binary Decision Diagram （OBDD） structure was used to describe the user-defined access policy. An attribute group composed of users with the attribute was established for each attribute， and a unique user attribute group key was generated for each member of the group. When the attribute revocation occurred， the minimum subset cover technology was used to generate a new attribute group for the remaining members in the group to realize real-time fine-grained attribute revocation. Security analysis shows that the proposed scheme has the indistinguishability of selective plaintext attacks， forward security and backward security. Performance analysis shows that the proposed scheme outperforms （t，n） threshold secret sharing scheme and Linear Secret Sharing Scheme （LSSS） in terms of access structure expression and computing capability， and has the decryption computational efficiency meeting the need of resource constrained cloud terminal users.

Unmanned Aerial Vehicle （UAV） aerial images have a wide field of vision， and the targets in the images are small and have blurred boundaries. And the existing Single Shot multibox Detector （SSD） target detection model is difficult to accurately detect small targets in aerial images. In order to effectively solve the problem that the original model is easy to have missed detection， based on Feature Pyramid Network （FPN）， a new SSD model based on continuous upsampling was proposed. In the improved SSD model， the input image size was adjusted to \mathrm{320} \times \mathrm{320} ， the Conv3_3 feature layer was added， the high-level features were upsampled， and features of the first five layers of VGG16 network were fused by using feature pyramid structure， so as to enhance the semantic representation ability of each feature layer. Meanwhile， the size of anchor box was redesigned. Training and verification were carried out on the open aerial dataset UCAS-AOD. Experimental results show that， the improved SSD model has 94.78% in mean Average Precision （mAP） of different categories， and compared with the existing SSD model， the improved SSD model has the accuracy increased by 17.62%， including 4.66% for plane category and 34.78% for car category.

Concerning the filtering problem of trees, buildings or other ground objects in field terrain reverse engineering, the disadvantages of conventional angle-limitation algorithm were analyzed, which accumulated errors or used a single threshold and could not meet the requirement of wavy terrain. Therefore, a self-adaptive angle-limitation algorithm based on scanning line was put forward. This method worked through limiting the angle of scanning center, reference point (known ground point) and the point to be sorted, which was adaptive with the wavy terrain. Then the modified point cloud was optimized with a curve fitting method by moving window. The experimental results prove that, the proposed algorithm has a sound control of the macro-terrain, and it can filter the wavy terrain point cloud much better.