Enterprises operating across borders often face conflicting data protection regulations and must address coordinated execution across heterogeneous blockchains， making compliant cross-border data transfer complex and risky. Therefore， a smart contract generation framework driven by Large Language Model （LLM） and Semantic Web Rule Language （SWRL） was proposed for cross-border data compliance across heterogeneous blockchains. First， centering on formalized rule definition-driven access control， cross-chain interoperability between two heterogeneous blockchains was achieved by cooperating with notarization services. Additionally， the data subject's latest authorization was injected into access decisions in real-time by utilizing an integrated consent module. Second， a rule matching framework based on finite-state decision process was constructed to decompose Semantic Web Rule Language （SWRL） compliance rules into reusable and modular logical units. Finally， a fine-tuned LLM was introduced to complete the SWRL rules semantically， which were automatically converted into executable smart contract code on the blockchain. Experimental results show that in the smart contract code generation task， the fine-tuned LLM achieves a comprehensive code generation success rate （FullRate） of 76.1%， which is improved by 18.6， 3.2， and 1.2 percentage points compared to that of the baseline models LLaMA-3-8B-Instruct， DeepSeek-V3， and DeepSeek-R1， respectively. Furthermore， the performance of the prototype system was evaluated under varying network node counts and adjustable channel configurations， demonstrating that it meets enterprise-level scalability requirements. The proposed framework significantly enhances the efficiency of smart contract generation while maintaining high security and logical correctness， demonstrating the practical feasibility and engineering application value of the framework in efficient and compliant cross-border data sharing scenarios.

To address the issue of unauthorized forgery or tampering of facial images， an active defense method based on attention mask and feature extraction was proposed. This method was designed to take offensive measures to interfere with forgery models by adding adversarial examples into the image， so that the image was prevented forgery from the source and the visual quality of the protected image was enhanced. Firstly， an improved gradient descent method was employed to generate and add adversarial perturbations to the original image， resulting in the generation of a blurred false image after forgery processing the original image. At the same time， the attention mask was incorporated into the generator to enhance key feature channels， thereby reducing the influence of complex backgrounds and lighting. Additionally， the VGG16 pre-trained network was utilized to extract image features， thereby improving the visual quality of adversarial images at feature map level. Experimental results on CelebFaces Attributes （CelebA） dataset and Radboud Faces Database （RaFD） dataset show that， for StarGAN， the defense success rates of the proposed model are 99.80% and 99.63% respectively. Compared with the baseline method based on spread-spectrum adversarial attack， the proposed method has the visual quality of generated adversarial images improved by 30.86% and 26.63% respectively on Structure Similarity Index Measure （SSIM）， and the Peak Signal-to-Noise Ratio （PSNR） improved by 34.80% and 36.15% respectively. The above indicates that the proposed method defends against face image forgery effectively while enhancing the visual quality of adversarial images.

To satisfy the diverse demands of network slicing in smart grid, a slicing resource allocation model based on cloud-edge collaboration in smart grid was proposed. Furthermore, a two-phase cooperative slice allocation model was developed to optimize the allocation of the network slices. In the first phase, an optimization model for the resource allocation in local edge network was established to optimize the user experience, and the optimization problem was solved with the Lagrange multiplier method. In the second phase, the system was modeled as a Markov decision process, and then the deep reinforcement learning was adopted to adaptively allocate the resources to the slices of the core cloud. Experimental results show that the proposed two-phrase slice resource allocation model can effectively reduce the network delay and improve the user satisfaction.

In the advanced assisted driving device, machine vision technology was used to process the video of vehicles in front in real time to dynamically recognize and predict the posture and behavior of vehicle. Concerning low precision and large delay of this kind of recognition algorithm, a deep learning algorithm for vehicle behavior dynamic recognition based on Long Short-Term Memory (LSTM) was proposed. Firstly, the key frames in vehicle behavior video were extracted. Secondly, a dual convolutional network was introduced to analyze the feature information of key frames in parallel, and then LSTM network was used to sequence the extracted characteristic information. Finally, the output predicted score was used to determine the behavior type of vehicle. The experimental results show that the proposed algorithm has an accuracy of 95.6%, and the recognition time of a single video is only 1.72 s. The improved dual convolutional network algorithm improves the accuracy by 8.02% compared with ordinary convolutional network and increases by 6.36% compared with traditional vehicle behavior recognition algorithm based on a self-built dataset.

The self-driving mine locomotive needs to detect and locate pedestrians in front of it in the underground roadway in real-time. Non-visual methods such as laser radar are costly, while traditional visual methods based on feature extraction cannot solve the problem of poor illumination and uneven light in the laneway. To solve the problem, a pedestrian visual positioning algorithm for underground roadway based on deep learning was proposed. Firstly, the overall structure of the system based on deep learning network was given. Secondly, a multi-layer Convolutional Neural Network (CNN) for object detection was built to calculate the two-dimensional coordinates and the size of bounding box of pedestrians in visual field of the self-driving locomotive. Thirdly, the third-dimensional distance between the pedestrian in the image and the locomotive was calculated by polynomial fitting. Finally, the model was trained, verified and tested through real sample sets. Experimental results show that the accuracy of the proposed algorithm reaches 94%, the speed achieves 25 frames per second, and the distance detection error is less than 4%, thus efficient and real-time laneway pedestrian visual positioning is realized.

To meet the increasing demand of high-precision localization in coal mine, a set of underground positioning anchors and tags based on Ultra WideBand (UWB) communication were designed and implemented by applying high-precision wireless transceiver chip DW1000. Asymmetric Double Sided Two-Way Ranging (ADS-TWR) algorithm was used to improve the accuracy of ranging between the anchor and the tag, which effectively suppressed the error caused by clock drift. Aiming at the problem that a large amount of invalid communication was generated by the tag broadcasting request frame when starting the localization in the underground multi-anchor layout, a region determination strategy for tags based on ADS-TWR was proposed, which made a tag communicate with the anchors in its region only. At the same time, region abnormal self-checking and region correction mechanism for tags were introduced to ensure the efficient and stable operation of the system. In the coordinate analysis phase for tags, triangle centroid algorithm was used to further improve localization accuracy based on high-precision ranging and reduce localization processing time. Finally, the experimental results show that the localization accuracy of tags is within 15 cm, which meets the requirement of high-precision localization in underground coal mine.

Concerning the problems of complex certificate management, key escrow and key distribution in the existing public key encryption schemes with keyword search, a certificate-based encryption scheme with keyword search was proposed. Firstly, the framework of certificate-based encryption with keyword search and its security model were formally defined. Secondly, an efficient bilinear-pairing-free certificate-based encryption with keyword search scheme over the elliptic curve group was proposed, which was proved to be indistinguishable against adaptively chosen-keyword attacks under the hardness assumption of the Computational Diffie-Hellman Problem (CDHP) in random oracle model. Finally, the proposed scheme was simulated and compared with several exsiting public key encryption schemes with keyword search in terms of property and performance. The comparison and analysis results show that the proposed scheme not only has the merits of implicit authentication, key escrow freeness and key distribution freeness, but also outperforms the comparison certificateless encryption schemes with keyword search in both computation efficiency and communication bandwidth.

All the previous certificate-based Proxy Re-Encryption (PRE) schemes are based on the computationally-heavy bilinear pairings, and thus have low computation efficiency. To solve this problem, a certificate-based proxy re-encryption scheme without relying on the bilinear pairings was proposed over the elliptic curve group. Under the hardness assumption of the Computational Diffie-Hellman (CDH) problem, the proposed scheme was formally proven to be indistinguishable against adaptively chosen-ciphertext attacks in the random oracle model. Due to avoiding the time-consuming bilinear pairing operations, the proposed scheme significantly reduced the computation cost. Compared with the previous certificate-based proxy re-encryption schemes with bilinear pairings, the analysis shows that the proposed scheme has obvious advantages in both the computation efficiency and the communication cost, and the scheme is more suitable for the computation-constrained and bandwidth-limited applications.

To deal with the problem of the locomotive's dynamically wireless access to transmission network in the mine locomotive unmanned system, the Successive Interference Cancellation (SIC) region partition strategy was proposed. Firstly, the nonlinear region partition model was constructed in the Access Point (AP) communication coverage. Secondly, based on the theoretical derivation, the relationship between the number of region partitions and the AP communication coverage, and the relationship between the locomotive position and the transmission power were found. Finally, SIC region partition strategy was designed. The simulation results show that SIC region partition strategy can make one AP access three locomotives at the same time, and the overall optimization effect of locomotive's total passing time and AP coverage utilization increases at least 50%.

Electrocardiogram (ECG) signal has attracted widespread interest for the potential use in biometrics due to its ease-of-monitoring and individual uniqueness. To address the accuracy and real-time performance problem of human identification, a fast and robust ECG-based identification algorithm was proposed in this study, which was particularly suitable for miniaturized embedded platforms. Firstly, a dynamic-threshold method was used to extract stable ECG waveforms as template samples and test samples; then, based on a modified Dynamic Time Warping (DTW) method, the degree of difference between matching samples was calculated to reach a result of recognition. Considering that ECG is a kind of time-varying and non-stationary signals, ECG template database should be dynamically updated to ensure the consistency of the template and body status and further improve recognition accuracy and robustness. The analysis results with MIT-BIH Arrhythmia database and own experimental data show that the proposed algorithm has an accuracy rate at 98.6%. On the other hand, the average running times of dynamic threshold setting and optimized DTW algorithms on Android mobile terminals are about 59.5 ms and 26.0 ms respectively, which demonstrates a significantly improved real-time performance.

Opportunistic network routing algorithm based on geographic location information in DIrection based Geographic routing scheme (DIG) has the problems of large delay and low success rate, which is due to that DIG algorithm makes the waiting time of the data in the cache too long and cannot guarantee the data-carrying node move to the destination node. To solve these problems, Geographic Routing algorithm based on Directional Data Transmission (GRDDT) was proposed. The algorithm used a new data forwarding mechanism and a more effective use of the neighbor list information, effectively avoiding the appearance of the above circumstances, so as to reduce data packet transmission delay and to improve the success rate. OPNET simulation results show that, the performance of transmission delay and success rate of GRDDDT algorithm are improved compared with DIG.

Ad Hoc On-demand Distance Vector (AODV) routing protocol selects the shortestpath or minimum hop count from source to destination as route selection metric. A new route selection metric was proposed in Linkquality Aware AODV (LA_AODV). It took route layer and physical layer into account. LA_AODV was introduced, and then the method of simulating LA_AODV in NS2 was expatiated. Some key problems were discussed. The simulation results show that LA_AODV protocol introduces much lower routing overhead while still providing better performance than AODV protocol in terms of throughput,cost and delay.