The deep integration of intelligence and communications has become the development trend of the sixth generation mobile communication (6G) and future communication systems. This study focuses on addressing three core issues in this deep integration：1) The inherent contradiction between increased communication bandwidth and a sharp rise in resource consumption；2) Improving the compression capability of the source to achieve information entropy reduction；and 3）Enhancing the adaptability of information systems to optimize communication system gain. Aiming at native artificial intelligence（AI）evolution，we propose，for the first time globally，a theoretical and technological system for modern semantic communication and 6G Intellicise networks. This system breaks through the limitations of classical information theory by establishing semantic information theory and expanding the boundaries of classical communication theory. It introduces a new pathway of “computation first，communication later”through modern semantic communication，ushering in a new communication paradigm. Furthermore，it constructs a 6G Intellicise network theory and technology system characterized by “intelligent endogenous and native simplicity”，providing core theoretical support and key technological approaches for overcoming future communication bottlenecks. Moreover，we have successfully established the world's first field trial network for 6G communication and intelligent integration，completing outfield trials in typical scenarios such as large-data-volume video transmission，unmanned vehicle communication，industrial Internet，and satellite-ground communication. In the realm of standardization，we have led the establishment of two core standards organizations—the China Communications Standards Association Technical Committee 630（CCSA TC630）Semantic Communication Promotion Committee and the International Mobile Telecommunications 2030（IMT-2030）6G Promotion Group Semantic Communication Task Force—to promote the integration and leadership of the independently intellectual property-rights-protected “Chinese solution”within the international standards system. These achievements have pioneered a new Intellicise evolution path globally in both industry and academia，achieving a full-chain progression from original theory and technological innovation to practical verification and international standards leadership. This work provides systematic technological support and a solid foundation for China to secure a leading position in 6G development，capture the strategic high ground in future communication technologies，and build a secure and controllable industrial ecosystem.

With the growing demand for understanding complex three dimensions (3D) environments in fields like Internet of things (IoT)-driven autonomous navigation and related applications, the automatic reconstruction of structured, editable computer-aided design (CAD) models from point cloud data has become a critical task. However, current research mainly focuses on reconstructing CAD models from CAD command sequences, sketches, and extrusion operations, and commonly faces challenges such as excessive reconstruction steps and strong platform dependence. To this end, a high-precision geometric and topological CAD reconstruction method is proposed, specifically for IoT 3D perception. First, the boundary representation of CAD is decomposed into parametric information of geometric primitives and their topological structure. Then, a primitive variational autoencoder (PVAE) and a topological variational autoencoder (TVAE) are designed to model their geometric features and topological relationships, respectively. Furthermore, PointNet++ is used to extract multi-scale local features from point cloud data and fuse them into global features. A topological decoder and primitive decoders are then used to predict topological tree sequences and primitive parameters, achieving high-precision CAD model reconstruction with clearer boundary details. To validate the method's effectiveness, metrics such as chamfer distance, edge chamfer distance, and structural consistency are used to evaluate its performance on two datasets. The experimental results show that the proposed model outperforms existing methods in terms of topological integrity and geometric reconstruction accuracy，enabling higher-precision CAD model reconstruction.

With the exponential growth of telecommunications business scale, customer complaint responsibility determination has become a key link in the compliance management of communication operators. Traditional methods face challenges such as semantic decoupling difficulties and low efficiency of knowledge reuse when dealing with complex complaints and unstructured historical cases. This study, based on the existing business process and large language model technology, proposes a linked retrieval enhanced generation framework. Through hierarchical semantic decoupling and dynamic retrieval of historical knowledge, it achieves the precision and efficiency of complaint responsibility determination. The framework proposes a two-level complaint point splitting mechanism：The first level uses a hybrid semantic parsing to strip off compound demands, and the second level extracts core complaint points from the first-level complaint list through large model prompt technology, while dynamically processing historical documents to adapt to the second-level complaint matching, completing the structural transformation of the database. Finally, experiments based on 423 actual complaint data from a provincial operator show that compared with several traditional retrieval augmented generation（RAG）models，this method improves the retrieval accuracy by 18.87% and the retrieval recall rate by 14.13% . This research provides an efficient and reusable technical path for intelligent responsibility determination under a strong regulatory background and has important practical significance.

Addressing challenges in complex structural health monitoring arising from heterogeneous sensor node sampling, feature drift, and limited model generalization ability in distributed vibration signals, this study constructs a distributed vibration signal with augmented generation (DVSAG) dataset. It utilizes cross-diffusion for adaptive sampling while preserving the spatiotemporal correlation of the original signal, combines the frequency domain to unify input dimensions, and enhances inputs by calculating residuals using fault-free reference signals. A fault diagnosis network with a convolutional block attention module (CBAM) is designed to extract multi-scale features from distributed vibration signals. These features are converted into word embeddings, combined with user questions, and input into a distributed vibration signal large language model (DVSLLM). Finally, a feature alignment and semantic mapping framework is used to achieve fine-grained interaction from vibration signals to natural language. Experiments show that the proposed method effectively improves fault diagnosis accuracy and model generalization ability under multiple operating conditions, providing reliable support for multi-task decision-making in complex structural health monitoring.

In the field of Internet of things (IoT) intrusion detection, federated learning has become an effective solution for implementing model weight integration updates. This distributed learning method allows devices to train models locally and transmit updated parameters to a central server for aggregation. However, existing intrusion detection methods based on federated learning still have limitations. In scenarios with non-independent and identically distributed data and heterogeneous client models, the intrusion detection performance of the global model will be severely affected. The significant communication overhead caused by simultaneously transmitting model parameters also hinders the actual deployment of federated learning schemes. To address the aforementioned issues, an efficient IoT intrusion detection method based on semi supervised federated learning is proposed. By utilizing unlabeled public data to enhance the model's understanding of the data, the performance of the client classifier is continuously improved. At the same time, a discriminator module is added to improve the quality of the client's predicted labels, and the combination of hard label strategy and voting mechanism effectively reduces communication overhead. The experimental results show that an accuracy of 86.97% is achieved in non-independent and identically distributed data and heterogeneous client model scenarios, which is superior to typical federated learning methods and achieves lower communication overhead.

To overcome the influence of beam split in terahertz communication, for the problems of a large number of large-range delay devices, high hardware complexity and high power consumption in traditional fully connected antenna structure schemes based on single-layer and double-layer delay devices, a multi-user hybrid analog/digital precoding scheme based on multi-layer delayers was considered. In this scheme, the time delays were divided into multi-layer arrangement at the base station, and hybrid precoding design was carried out based on the multi-layer delayers. Specifically, firstly the bit number of time delays and the set of discrete time delay based on the number of time delays in each layer and antennas were deduced. Secondly according to the array response vector at the center frequency, the phase shift of phase shifter was designed. Then the propagation delay of antenna array aperture corresponding to the subarray where the delayer is located was used as the delay of time delays, and the delayer delay was discretized based on the discrete delay set. Afterwards, analog precoding was designed based on the phase shift of phase shifter and the delay of time delays. Finally, low complexity zero forcing precoding technology was used to design digital precoding. Simulation and analysis results show that compared to traditional scheme, the proposed scheme can greatly reduce the number of large range delay devices and hardware complexity while sacrificing a small amount of rate performance.

A smart contract is a piece of computer program that runs on the blockchain, which has the characteristics of automatic execution, non-tampering, and publicity. Smart contracts control the flow of large amounts of high-value data, and attackers can exploit vulnerabilities in smart contracts to steal funds or resources. Existing detection methods, such as symbol execution, have problems such as path explosion and high false positive rate, while machine learning methods are black-box and uninterpretable. In order to solve the above problems, an expert mode based on expert mode and explainable machine learning was proposed to detect vulnerabilities in smart contract code, an expert mode for vulnerabilities was designed, and shapley additive explanations (SHAP) was used to explain the weights of multiple features, and the average detection accuracy of four vulnerabilities (re-entrancy vulnerability, timestamp vulnerability, integer overflow vulnerability, and permission control vulnerability) reached 90.36% , which achieved better detection results compared with classic tools such as Oyente and Mythril.

High-performance electromagnetic shielding materials can effectively protect against the increasingly prominent electromagnetic interference and damage, in order to overcome the shortcomings of traditional rigid electromagnetic shielding materials such as poor flexibility and controllability in practical applications, a flexible, mouldable and conductive silver nanosheets composite hydrogel shielding material was developed, and its three-dimensional structure and effective component composition were determined by scanning electron microscope (SEM) , X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) . A vector network analyser was further used to confirm the excellent electromagnetic shielding performance of the hydrogel in the frequency band of 8.0～12.4GHz (consistently stabilized above 35dB). Based on the controllable water content of the hydrogel, a dehydration-induced spatial interconnection of silver nanolayers to form a macroscopic conductive pathway is designed and proposed, which greatly improves the electrical conductivity of the composite hydrogel and thus achieves a high level of electromagnetic shielding, and to a certain extent, provides a practical and effective solution for the controllable design and preparation of high-performance electromagnetic shielding materials.

To effectively mitigate Sinkhole attacks in wireless sensor networks (WSN) , this paper proposes a novel Sinkhole attack detection and defense strategy (IF-GBO) that integrates isolation forest (IF) and gradient-based optimizer (GBO) . First, a detection threshold is established to trigger the IF-GBO intrusion detection mechanism, thereby reducing network overhead and improving detection efficiency. Second, considering the characteristics of Sinkhole attacks and the dynamic real-time nature of WSN data, a multidimensional feature dataset is designed, incorporating node hop count, energy consumption, packet reception/forwarding rate, and time delay. The model is trained using a sliding window sampling approach, which not only enhances the algorithm's operational efficiency but also improves the accuracy of malicious node identification. Finally, a multi-objective path selection function is developed，leveraging the GBO algorithm to assist nodes in rapidly identifying alternative transmission paths to counter Sinkhole attacks. This approach effectively ensures reliable data transmission，extends network lifetime，and resolves the issue of delayed delivery of anomaly detection results to the legitimate sink node. Experimental results demonstrate that compared to conventional anomaly detection models such as support vector machine（SVM），k-nearest neighbors（KNN）and local outlier factor（LOF），IF-GBO achieves higher accuracy in identifying malicious nodes with lower false positive rates and superior generalization capability. Furthermore，when compared to dedicated Sinkhole attack detection algorithms like hop count-based detection scheme for Sinkhole attack（HCODESSA）and a Sinkhole detection algorithm based on the random routes selected by minimum hop（RMHSD），the GBO-based defense strategy significantly mitigates the disruptive effects of Sinkhole attacks on the network，ensuring routing security and reliability.

Backscatter communication has received widespread attention in the field of the Internet of things due to its advantages of low cost and low energy consumption. However, with the gradual expansion of data services, new challenges have been posed to the transmission performance of backscatter communication. Multiple-input multiple-output (MIMO) technology has been introduced into backscatter communication due to its advantages in system reliability and transmission rate. One of them, a dual ended joint coding technology called block-level unitary query space-time block code (BUTQ-STBC) , can fully exploit the diversity potential of backscatter channels, but at the cost of sacrificing transmission rate. To combat this issue, a new signal transmission scheme that selects the optimal communication link for data transmission based on the idea of spatial matching is proposed. The simulation results show that the proposed transmission scheme is superior to the BUTQ-STBC scheme. The proposed scheme provides a solution for efficient and reliable transmission of MIMO backscatter communication.