The popularity of RESTful APIs within modern Web services makes API security a critical concern gradually. The mainstream tools for API discovery and vulnerability detection have effect limitations in discovering hidden or undocumented APIs due to relying on API documents or public paths for scanning， and have high false positive rates in complex or dynamic API environments. Addressing these challenges， A2A （Agent to API vulnerability detection）， an Agent system for hidden API discovery and vulnerability detection was proposed through agents communicating seamlessly via a Model Context Protocol （MCP）， so as to realize full-process automation from hidden API discovery to vulnerability detection. In A2A， adaptive enumeration and HTTP response analysis were employed to discover potential hidden API endpoints automatically， and a service-specific API fingerprint library was combined to confirm and discover hidden APIs， On API vulnerability detection， Large Language Model （LLM） and Retrieval-Augmented Generation （RAG） techniques were integrated by A2A， and high-quality test cases were generated automatically through a feedback iterative optimization mechanism， so as to verify whether the vulnerability exists. Experimental evaluation results indicate that A2A has the average API discovery rate of 91.9%， with an false discovery rate of 7.8%， and discover multiple hidden API vulnerabilities previously undetected by NAUTILUS and RESTler successfully.

In order to solve the problems that copyright owners maliciously frame purchasers up and purchasers know their own watermarks so remove them easily during the digital content copyright protection and tracking processes， a digital content copyright protection and fair tracking scheme based on blockchain was proposed. Firstly， Paillier homomorphic encryption algorithm and key distribution smart contract were used to change the purchaser’s watermark in ciphertext state， and the watermark was embedded in the encrypted digital content. Secondly， the key distribution smart contract and arbitration smart contract were called by the verification node in blockchain， which solved the single point of failure problem in the traditional copyright protection solutions. Finally， experiments were conducted to verify the performance of the proposed scheme. The results show that when the digital content size is 1 024×1 024， compared with the blockchain-enabled accountability mechanism against information leakage in vertical industry services， the proposed scheme has the total execution time of encryption and watermark embedding reduced by 94.92%， and the total decryption execution time reduced by 79.72%. It can be seen that the proposed scheme has low total time and operating costs with good efficiency， and can be widely used in the field of digital content copyright protection.

The core of Human-Object Interaction （HOI） detection is to identify humans and objects in the images and accurately classify their interactions， which is crucial for deepening scene understanding. However， existing algorithms struggle with complex interactions due to insufficient local information， leading to erroneous associations and difficulties in distinguishing fine-grained operations. To address this limitation， a Local Feature-enhanced Perceptual Module （LFPM） was designed to enhance the model's capability of capturing local feature information through the integration of local and non-local feature interactions. This module comprised three key components： the Downsampling Aggregation branch Module （DAM）， which acquired low-frequency features through downsampling and aggregated non-local structural information； the Fine-Grained Feature Branch （FGFB） module， which performed parallel convolution operations to supplement the DAM's local information extraction； and the Multi-Scale Wavelet Convolution （MSWC） module， which further optimized output features in spatial and channel dimensions for more precise and comprehensive feature representations. Additionally， to address the limitations of Transformer in local spatial and channel feature mining， a spatial and channel Squeeze and Excitation （scSE） module was introduced. This module allocated attention across spatial and channel dimensions， enhancing the model's sensitivity to locally salient regions and effectively improving HOI detection accuracy. Finally， the LFPM， scSE， and Transformer architectures were integrated to form the Local Feature Enhancement Perception model （LFEP） framework. Experimental results show that， compared with the SQA （Strong guidance Query with self-selected Attention） algorithm， LFEP framework achieves 1.1 percentage points improvement in Average Precision on the V-COCO dataset， and 0.49 percentage points improvement in mean Average Precision （mAP） on the HICO-DET dataset. Ablation experimental results also validate the effectiveness of each module of LFEP.

In order to realize the CAN bus communication with PC and remote monitoring, a design method of the embedded Ethernet-CAN communication transform card based on FPGA was proposed. The design chose the embedded soft processors Nios Ⅱ in FPGA as the main control chip, MCP2515 as the CAN bus controller and 88E1111 as the Ethernet PHY chip. A system hardware model was built with the SOPC (System-On-a-Programmable-Chip) technology, and the CAN controller, Ethernet initialization and the Ethernet-CAN conversion process were completed in the Nios Ⅱ IDE （Integrated Development Environment）.The experimental results show that the design completely meets the requirements of the Ethernet and CAN bus communication.

The Concurrency Control (CC) scheme employed in distributed database system can profoundly affect the performance of transaction-processing system. There are many different kinds of solutions to the problem of CC in distributed database system, and they have both advantages and disadvantages. The major methods of CC, basic knowledge of lock-based model and the project of 2-Phase Locking (2PL) were introduced. A score-based method was proposed based on lock-mechanism and in accordance with 2PL protocol, which could reduce the amount of data transmission and had desirable performance of concurrency. It could be used to deal with the problem of CC. The experimental results show that the proposed method can achieve much better performance than other available ones.