Protocol conversion is usually used to solve the problem of data interaction between different protocols， and its nature is to find mapping relationship between different protocol fields. In the traditional methods of protocol conversion， several drawbacks are identified： traditional conversions are mainly designed on the basis of specific protocols， so that they are static and lack flexibility， and are not suitable for environments with multi-protocol conversion； whenever a protocol changes， a reanalysis of the protocol’s structure and semantic fields is required to reconstruct the mapping relationship between fields， leading to an exponential increase in workload and a decrease in protocol conversion efficiency. Therefore， a general method of protocol conversion based on semantic similarity was proposed to enhance protocol conversion efficiency by exploring the relationship between fields intelligently. Firstly， the BERT （Bidirectional Encoder Representations from Transformers） model was employed to classify the protocol fields， and eliminate the fields that “should not” have mapping relationship. Secondly， the semantic similarities between fields were computed to reason the mapping relationship between fields， resulting in the formation of a field mapping table. Finally， a general framework for protocol conversion based on semantic similarity was introduced， and related protocols were defined for validation. Simulation results show that the precision of field classification of the proposed method reaches 94.44%； and the precision of mapping relationship identification of the proposed method reaches 90.70%， which is 13.93% higher than that of the method based on knowledge extraction. The above results verify that the proposed method is feasible， can identify the mapping relationships between different protocol fields quickly， and is suitable for scenarios with multi-protocol conversion in unmanned collaboration.

Multi-frame self-supervised monocular depth estimation constructs a Cost Volume （CV） based on the relationship between current frame and the previous frame， serving as an additional input source for the monocular depth estimation network. This approach provides a more accurate description of the temporal and spatial structure of scene videos. However， the cost volume becomes unreliable in the presence of dynamic objects or untextured regions in the scene. Overreliance on the unreliable information within the cost volume leads to a decrease in depth estimation accuracy. To tackle the issue of unreliable information in the cost volume， a multi-frame fusion module was designed to reduce the weights of unreliable information sources dynamically and mitigate the impact of unreliable information sources on the network. Besides， to handle the negative impact of unreliable information sources in cost volume on network training， a network was designed to guide the training of the depth estimation network， preventing the depth estimation network from overly depending on unreliable information. The proposed method achieves excellent performance on KITTI dataset， with absolute relative error， squared relative error， and Root Mean Square Error （RMSE） decreased by 0.015， 0.094， and 0.200， respectively， compared to the benchmark method Lite-Mono. In comparison to similar methods， the proposed method not only has higher precision， but also requires fewer computational resources. The proposed network structure makes full use of the advantages of multi-frame training， while avoiding the defects of multi-frame training （i.e.， the influence of cost volume uncertainty on the network）， and improves the model precision effectively.

Aiming at the single link failure problem in the vehicle-road real-time query communication scenario of Software-Defined Internet of Vehicles （SDIV）， a fast link failure recovery method for SDIV was proposed， which considered link recovery delay and path transmission delay after link recovery. Firstly， the failure recovery delay was modeled， and the optimization goal of minimizing the delay was transformed into a 0-1 integer linear programming problem. Then， this problem was analyzed， two algorithms were proposed according to different situations， which tried to maximize the reuse of the existing calculation results. In specific， Path Recovery Algorithm based on Topology Partition （PRA-TP） was proposed when the flow table update delay was not able to be ignored compared with the path transmission delay， and Path Recovery Algorithm based on Single Link Search （PRA-SLS） was proposed when the flow table update delay was negligible because being farless than the path transmission delay. Experimental results show that compared with Dijkstra algorithm， PRA-TP can reduce the algorithm calculation delay by 25% and the path recovery delay by 40%， and PRA-SLS can reduce the algorithm calculation delay by 60%， realizing fast single link failure recovery at vehicle end.

With the rapid development of e-commerce and the popularity of the Internet， it is more convenient to exchange and return goods. Therefore， the customers’ demands for goods show the characteristics of timeliness， variety， small batch， exchanging and returning. Aiming at Location-Routing Problem with Simultaneous Pickup and Delivery （LRPSPD） with capacity and considering the characteristics of customers’ diversified demands， a mathematical model of LRPSPD & Time Window （LRPSPDTW） was established. Improved FireWorks Algorithm （IFWA） was used to solve the model， and the corresponding neighborhood operations were carried out for the fireworks explosion and mutation. The performance of the fireworks algorithm was evaluated with some benchmark LRPSPD examples. The correctness and effectiveness of the proposed model and algorithm were verified by a large number of numerical experiments. Experimental results show that compared with Branch and Cut algorithm （B&C）， the average error between the result of IFWA and the standard solution is reduced by 0.33 percentage points. The proposed algorithm shortens the time to find the optimal solution， and provides a new way of thinking for solving location-routing problems.

It is difficult for the existing methods to get overall sentiment orientation of the comment text. To solve this problem, the method of multi-document sentiment summarization based on Latent Dirichlet Allocation (LDA) model was proposed. In this method, all the subjective sentences were extracted by sentiment analysis and described by LDA model, then a summary was generated based on the weight of sentences which combined the importance of words and the characteristics of sentences. The experimental results show that this method can effectively identify key sentiment sentences, and achieve good results in precision, recall and F-measure.

To solve the problem of transmission in wireless networks, a transmission and scheduling scheme based on W-learning algorithm in wireless networks was proposed in this paper. Building the system model based on Markov Decision Progress (MDP), with the help of W-learning algorithm, the goal of using this scheme was to transmit intelligently, namely, the package loss under the premise of energy saving by choosing which one to transmit and the transmit mode legitimately was reduced. The curse of dimensionality was overcome by state aggregate method, and the number of actions was reduced by action set reduction scheme. The storage space compression ratio of successive approximation was 41%; the storage space compression ratio of W-learning algorithm was 43%. Finally, the simulation results were given to evaluate the performances of the scheme, which showed that the proposed scheme can transport data as much as possible on the basis of energy saving, the state aggregation method and the action set reduction scheme can simplify the calculation with little influence on the performance of algorithms.

In view of the complex Container Loading Problem (CLP), the optimal loading plan with heuristic information and the ant colony algorithm was proposed. Firstly, a mathematical model was generated. Considering the strong search ability, potential parallelism and scalability of ant colony algorithm, the proposed algorithm was combined with the triple-tree structure to split the layout of space in turn. Then, the three-dimensional rectangular objects of different sizes were placed to the layout space under the constraints. An ant colony algorithm based on spatial partition was designed to solve the optimal procedure. Finally, a design example that 700 pieces of goods were loaded into a 40-foot (12.025m) high cubic was calculated. The experimental results show that the proposed method can enhance the utilization of the container and it has a strong practicality.

A task allocation and scheduling algorithm called Dynamic Dual-Directional Priority (DDDP) was presented. This algorithm considered the priority of real-time task and the sub synthetically, and constructed a dynamic dual-directional priority task allocation model, realized the task allocation and scheduling of the master/sub model in data transmission. In the simulation experiments with some typical data of various parameters under normal workload and overload situation, the DDDP algorithm has improved the performance of scheduling obviously compared with a classical Earliest Deadline First (EDF) algorithm which only considers the deadlines of real-time tasks.

Image splicing is a technique commonly used in image tampering. To implement image splicing blind detection，a new splicing detection scheme was proposed. Image splicing detection could be regarded as a two-class pattern recognition problem and the model was established based on moment features and some Image Quality Metrics (IQMs) extracted from the given test image. This model could measure statistical differences between original image and spliced image. Kernel-based Support Vector Machine (SVM) was chosen as a classifier to train and test the given images. Experimental results demonstrate that this new splicing detection scheme has some advantages of high-accuracy and wide-application.