Composite materials have wide application prospects in large equipment in multiple fields， such as aerospace， shipping， and high-speed rail， due to their advantages of lightweight， high strength， and performance designability. However， current commercial software mainly focuses on single-scale functions， such as digital design of composite materials， performance prediction， macroscopic response simulations， and structural optimization. And combined use of multiple software leads to version incompatibility， inconsistent data format and other problems， which makes it difficult to satisfy the current high-end equipment development requirements of on-demand design for the integration of composite material-component-system. Therefore， a cross-system common support platform SWJTU-CAX for design-simulation-optimization software research and development of composite materials and structure was designed and self-developed by combining object-oriented method with modular design idea. In the platform， cross-system development environment Qt and data visualization library VTK were used as basic tools， and data layer， logical processing layer， and presentation layer were used as overall architecture to deal with core difficulties of data management and circulation， functional organization and module expansion， efficient access and visualization of large-scale data in whole process of composite material and structure designs， cross-scale simulation， performance prediction， parallel solving， and optimization design. Besides， code organization and functional testing of SWJTU-CAX were carried out using modular approach and CMake tool， with the help of XML file for management， addition and removal of required functional modules on the platform， and rapid integration and expansion of composite material and structural design-simulation-optimization， as well as other functional modules were implemented. Finally， ceramic composite materials and components were taken as examples， and integrated optimization design and software development of digital modeling， cross-scale performance prediction， response simulation， and structural materials for ceramic composite materials with arbitrary weaving forms were realized on SWJTU-CAX platform， verifying the feasibility of this cross-system platform architecture， data organization， and project management method. It can be seen that the development of SWJTU-CAX platform provides a powerful tool for in-depth exploration of cross-scale failure mechanism of heterogeneous materials and on-demand design of integrated equipment-component-material as well as service safety assessment of equipment composite components， and provides technical ideas and references for domestic industrial software architecture and research and development.

Aiming at the problems of Byzantine Fault Tolerant （BFT） consensus mechanisms in the blockchain such as high communication complexity， complex view change and poor scalability， a consensus algorithm based on binomial swap forest and HotStuff named HSP （HotStuff Plus） consensus algorithm was proposed. In order to realize signature batch verification and signature aggregation， the Boneh-Lynn-Shacham （BLS） signature algorithm was adopted； in order to reduce the communication complexity of the system， threshold signature technology was adopted； in order to reduce the communication complexity during view change， the consensus process adopted a three-phase confirmation method； in order to reduce the number of communications between the primary and secondary nodes and reduce the pressure on the primary node when aggregating signatures， an improved binomial swap forest technology was adopted. Test results show that when the total number of system nodes is 64 and the request and reply are both 256 bytes， the throughput of HSP consensus algorithm is 33.8% higher than that of HotStuff consensus mechanism， and the consensus delay of HSP consensus algorithm is 16.4% lower than that of HotStuff consensus mechanism. It can be seen that HSP consensus algorithm has better performance when the number of nodes is large.

In order to solve the problem of low matching quality of the traditional feature extraction and matching algorithm in Unmanned Aerial Vehicle （UAV） visual localization， a new image matching algorithm based on transmission tower area extraction was proposed. Firstly， the image was divided into several overlapping grid areas， and the feature points were extracted by a two-layer pyramid structure for each area to ensure the uniform distribution of feature points. Then， the Line Segment Detector （LSD） algorithm was used to extract the lines in the images， the transmission tower support areas were extracted on the basis of special structure of transmission tower. Finally， the feature points in the transmission tower areas and the background areas were matched respectively in continuous images to further estimate the camera motion. In the rotation and translation estimation experiment， compared with the traditional Oriented Features from Accelerated Segment Test（FAST） and Rotated Binary Robust Independent Elementary Features （BRIEF） （ORB） feature extraction and matching algorithm， the proposed algorithm has the feature matching accuracy improved by 10.1 percentage points， the mean value of relative pose error reduced by 0.049. In the UAV inspection experiment， the relative error of the UAV trajectory estimation by using the proposed algorithm is 2.89%， which indicates that the proposed algorithm can achieve the robust and accurate estimation of the UAV’s pose during the real-time flying around the tower.

Concerning the problems of the difficulties in obtaining， the limitation of labels， and the high cost of labeling of shale gas reservoir data， a Multi-standard Active query Multi-label Learning （MAML） algorithm was proposed. First of all， with the consideration of the informativeness and representativeness of the samples， the preliminary processing was performed on the samples. Secondly， the sample richness constraints including attribute differences and label richness were added， on this basis， the valuable samples were selected and the labels of these samples were queried. Finally， a multi-label learning algorithm was used to predict the labels of the remaining samples. Through experiments on eleven Yahoo datasets， the MAML algorithm was compared with popular multi-label learning algorithms and active learning algorithms， and the superiority of the MAML algorithm was proved. Then， the experiments were extended to four real shale gas well logging datasets. In these experiments， compared with the multi-label learning algorithms： Multi-Label Multi-Label K-Nearest Neighbor （ML-KNN）， BackPropagation for Multi-Label Learning （BP-MLL）， multi-label learning with GLObal and loCAL label correlation （GLOCAL） and active learning by QUerying Informative and Representative Examples （QUIRE）， the MAML algorithm improved the average prediction accuracy of comprehensive quality of shale gas reservoirs by 45 percentage points， 68 percentage points， 68 percentage points， and 51 percentage points， respectively. The practicability and superiority of the MAML algorithm in the prediction of shale gas reservoir sweet spots are fully proved by these experimental results.

For Resource-Constrained Project Scheduling Problems (RCPSP), the Cooperative Shock search Particle Swarm Optimization with Chaos (CSCPSO) was proposed. On the basis of particle attractor, a bidirectional cooperation shock search mechanism was established in the algorithm to enhance the search accuracy and diversity of population. The particles converged to particle attractor, meanwhile they adjusted the dimensions whose adjacent relationship were inconsistent with attractor's by shock search in the mechanism. Combined with topological sorting based on particles and serial schedule generation scheme, the gotten scheduling scheme could meet the project schedule constraints of resource and precedence relations. The tests on specific examples show that the proposed algorithm can get higher accuracy and better stability for RCPSP.

The braid group is a new candidate platform for constructing quantum attack-resistant public key encryption system. The proxy signature allows a proxy signer to sign messages on behalf of an original signer who delegates his signing power to the proxy signer. A proxy signature based on root finding problem over braid group was put forward, the analysis of its correctness, security, efficiency and parameter choice were subsequently presented, and afterwards the detailed formal security proofs in the Random Oracle Model (ROM) were given. The new scheme can resist current known attacks.