In aviation domain， stable communication between air units and ground units requires unstructured text messages be transformed into structured information in a fixed format for transmission. Although the existing methods use the instruction-following capabilities of Large Language Models （LLMs） to perform such transformation from text into structured data， they ignore the inherent hallucination and lack of interpretability of LLMs — which pose serious threats to information security. Therefore， a Message Consistency Parsing Framework for the Aviation domain （MCPFA） was proposed to integrate model self-reflection and human-machine collaboration to ensure semantic fidelity and operational safety during the parsing process systematically. First， a Consistency Verification Method based on Self-Reflection （CVMSR） was developed to establish a progressive validation mechanism across three dimensions： syntax， semantics， and pragmatics， so as to realize dynamic verification and correction of the structured output. Second， an Intelligent Aviation Message Human-Machine Collaboration Inspection System （IAMHCIS） was designed to use confidence awareness and cross-layer consistency analysis to trigger expert intervention precisely and incorporate human feedback into model iteration， so as to form a closed-loop optimization. Finally， the framework was evaluated comprehensively through systematic experiments. The results demonstrate that MCPFA outperforms the existing methods significantly in both consistency judgment and correction tasks， identifying and fixing critical errors caused by term confusion， unit misinterpretation， or regulation violation effectively. Furthermore， the framework exhibits strong generalization capability on unseen message types. Importantly， after integrating expert knowledge， performance gains continuously， confirming the proposed framework’s growability and practical value， and the proposed framework provides a new accurate， secure， and interpretable way for LLM application in aviation and other safety-critical domains.

Constraint handling strategies of the existing constrained multi-objective algorithms fail to solve the problems with large infeasible regions effectively， resulting in population stagnation at the edge of infeasible regions. Besides， the higher requirements are proposed for the global search ability and the maintenance of diversity of the algorithms by the discontinuous problems with constraints. To solve the above problems， a Constrained Multi-Objective Evolutionary Algorithm based on Multi-Stage Search （CMOEA-MSS） was proposed， with different search strategies used in three stages. To make the population across large infeasible regions and approximate Pareto front quickly， in the first stage， a convergence indicator was used to guide the population search without considering the constraints. In the second stage， a set of uniformly distributed weight vectors were utilized to maintain the population diversity， and an improved epsilon constraint handling strategy was presented to retain high-quality solutions in infeasible regions. In the third stage， the constraint dominance principle was adopted， and the search preference would focus on the feasible regions to ensure the feasibility of the final solution set. CMOEA-MSS was compared with NSGA-Ⅱ+ARSBX （Nondominated Sorting Genetic Algorithm Ⅱ using Adaptive Rotation-based Simulated Binary crossover） and other algorithms on MW and DASCMOP test sets. Experimental results show that CMOEA-MSS obtains the best IGD （Inverted Generational Distance） values on seven test problems and the best HV （HyperVolume） values on five test problems on MW test set， and obtains the best IGD values on three test problems， the second best IGD values on two test problems and the best HV values on five test problems on DASCMOP test set. It can be seen that CMOEA-MSS has obvious advantages in solving discontinuous and multi-modal constrained multi-objective problems.

Attackers can illegally open a vehicle by forgeing the Radio Frequency IDentification （RFID） signal sent by the vehicle remote key. Besides， when the vehicle remote key is lost or stolen， the attacker can obtain the secret data inside the vehicle remote key and clone a usable vehicle remote key， which will threaten the property and privacy security of the vehicle owner. Aiming at the above problems， a Vehicle RKE Two-Factor Authentication （VRTFA） protocol for vehicle Remote Keyless Entry （RKE） that resists physical cloning attack was proposed. The protocol is based on Physical Uncloneable Function （PUF） and biological fingerprint feature extraction and recovery functions， so that the specific hardware physical structure of the legal vehicle remote key cannot be forged. At the same time， the biological fingerprint factor was introduced to build a two-factor authentication protocol， thereby solving the security risk of vehicle remote key theft， and further guaranteeing the secure mutual authentication of vehicle RKE system. Security analysis results of the protocol using BAN logic show that VRTFA protocol can resist malicious attacks such as forgery attack， desynchronization attack， replay attack， man-in-the-middle attack， physical cloning attack， and full key leakage attack， and satisfy the security attributes such as forward security， mutual authentication， data integrity， and untraceability. Performance analysis results show that VRTFA protocol has stronger security and privacy and better practicality than the existing RFID authentication protocols.

To deal with the poor performance of word sense disambiguation in parsing, a Chinese phrase parsing approach was proposed based on disambiguation of Chinese part of speech. First, it expanded part of speech of TongYiCi CiLin and then substituted the original words in the training set and test set with semantics codes. In this process, it used part of speech of word for word sense disambiguation. The experimental results on Penn Chinese TreeBank (CTB) show that the proposed method achieves precision rate of 80.30%, recall rate of 78.12%, and F-measure of 79.19%. Relative to the no disambiguation system, the presented approach can effectively improve the performance of phrase parsing.

To avoid the limitations of the traditional fuzzy rule based on Genetic Algorithm (GA), a calculation method of fuzzy control rule which contains weight coefficient was presented. GA was used to find the best weight coefficient which calculate the fuzzy rules. In this method, different weight coefficients could be provided according to different input levels, the correlation and symmetry of the weight coefficients could be used to assess all the fuzzy rules and then reduce the influence of the invalid rules. The performance comparison experiments show that the system which consists of these fuzzy rules has small overshoot, short adjustment time, and practical applications in fuzzy control. The experiments of different stimulus signals show that the system which consists of these fuzzy rules doesnt rely on stimulus signal as well as having a good tracking effect and stronger robustness.

In the existing genetic algorithms for permutation flow shop scheduling problem, the crossover and mutation operator is complex because of the processing sequence, the offspring is not similar to parent, and the algorithm easily falls into local optimum. To solve these problems, an improved genetic algorithm with priority-based value coding method and optimum limited operator was proposed. The coding method based on the priority values of the workpieces could avoid illegal coding, and the optimum limited operator could limit the propagation of the best individual to prevent falling into local optimum. The experiments show that this coding method is feasible and it can solve the practical problem when urgent workpieces must be processed firstly. The simulation results on benchmarks demonstrate that the proposed algorithm has superiority of smaller relative error and higher stable solution quality.