In response to the issues of vehicle toll evasion caused by license plate cloning and potential user privacy leaking to centralized entity due to centralized data storage in the current intelligent transportation highway free-flow tolling schemes， a highway free-flow tolling method based on blockchain and zero-knowledge proof was developed. Initially， a video surveillance mechanism for toll evasion detection was designed to ensure the compliance of vehicles on highways. Subsequently， smart contracts in the blockchain were designed to encrypt and store vehicle Location Certificate （LC） and payment data in a distributed ledger， and zero-knowledge proof technology was introduced to ensure the correctness of payment while protecting privacy. At the same time， an algorithm for charging tolls based on the vehicle's mileage was designed within the zero-knowledge circuit. Theoretical analysis and simulation results demonstrate that under normal conditions， the proposed method can achieve correct tolling based on the actual mileage with zero-knowledge of location privacy， and in the event of exceptions， the proposed method can provide timely warnings and record anomalies on the blockchain； compared to traditional manual tolling method， the proposed method has the average tolling time reduced from 38.0 s to 1.8 s， and has the decrease of about 0.1 s compared to the tolling method combining 5G and electronic non-stop toll collection system ETC （Electronic Toll Collection） in average tolling time. For the same entry and exit， the lower the overlap ratio in the number of information network trusted third-party Information Collection Point （ICP） of different routes， the more accurate the mileage-based tolling.

To address the limitations of the existing deep learning methods in handling scenarios where both geometric boundaries and initial conditions vary in physical simulation problems， a technical approach was proposed to decouple the representation of geometric boundary constraints from the physical system simulation， and a two-step technical route of geometric feature representation learning and physical system simulation was designed. After constructing an independent geometric feature extraction module which was unaffected by external physical conditions， the extracted geometric features were fused with physical features， and finally a neural network-based physical system simulation model was designed. In stress field prediction experiments， the proposed method achieves a prediction time of only 2.63 ms， which is much lower than 0.6 s of Finite Element Method （FEM）， and has a Mean Absolute Error （MAE） only 0.389 times of that of MeshNet. Experimental results demonstrate that the proposed method maintains high simulation accuracy while effectively adapting to different geometric boundaries and initial conditions.

In order to adapt to the the requirement of the Instruction Set Simulator (ISS) simulation speed in embedded system development, an improved ISS technology was put forward.The technology introduced instruction preprocessing, dynamic decode cache structure, multi-thread C function generation and dynamic scheduling technique based on the existing static multi-core simulator to achieve the optimization of the simulator performance. This technique has been applied successfully in forming OPT-ISS, which is based on IME-Diamond multi-core DSP processor. The experimental results show that this technique improves the simulation speed indeed.

In order to reduce the outage probability and enhance the stability of cognitive system, an improved algorithm of spectrum allocation based on classical graph coloring model was proposed. A difference factor of spectrum's idle time and user's request time was introduced. For every cognitive user, the algorithm allocated spectrums according to two factors: the spectrum efficiency and the time difference factor. Cognitive user with greater product value of the two factors was prior. The simulation results show that the outage probability of improved algorithm is far below that of the previous algorithm.