210 results about "Knowledge sharing" patented technology

The invention discloses a numerical control machine tool machining environment monitoring system, and belongs to the technical field of intelligent monitoring. Comprising the following modules: a multi-dimensional sensing monitoring module for realizing omnibearing data acquisition of an interaction state of a cutter and a workpiece in a machining process; the feature extraction module is used for converting the time sequence data into a key feature set for representing the state of the cutter; the wear type identification module is used for accurately identifying and classifying the wear type of the cutter based on a deep learning classifier and judging the specific wear type; the wear progress prediction module calls a corresponding special prediction model according to the identified specific wear type, quantifies the wear progress rate and estimates the residual life of the cutter; the decision support module is used for integrating the tool wear state and the prediction result, balancing the production efficiency, the machining quality and the tool cost, and providing optimization suggestions of parameter adjustment and tool changing opportunities; and the self-learning optimization module is used for continuously collecting actual production data to carry out model evaluation and incremental learning so as to realize multi-machine knowledge sharing.

A system and method for real-time team intent modeling using persistent cognitive machines with federated human profiles which processes individual team member behavioral signals through geometric intent analyzers that generate high-dimensional vector representations of individual objectives and preferences. A team intent orchestrator aggregates individual vectors into collective representations within a dynamic geometric manifold that evolves based on team coordination patterns. Federated human profiles enable privacy-preserving knowledge sharing across teams through geometric abstraction techniques that preserve coordination utility while protecting individual privacy. The system implements proactive conflict detection through trajectory analysis that identifies potential coordination issues before performance impact, and provides real-time synchronization mechanisms that maintain team coordination coherence despite individual behavioral changes. Cross-team learning capabilities enable organizational intelligence development through pattern abstraction and context-aware adaptation of successful coordination strategies. The persistent cognitive architecture maintains coordination patterns across sessions and team composition changes, enabling continuous improvement through accumulated team experience.

The invention provides a multi-park energy consumption prediction scheduling method based on digital twinning and a control system, and systematically solves the problem of the pain point of multi-park energy consumption management by constructing a technical chain from data perception to closed-loop optimization. The method comprises the following steps: firstly, by constructing a global unified digital twinborn model, standardized integration of dispersed and heterogeneous park assets and data is realized, and the problem of information islands is solved; secondly, prediction is carried out by adopting federated learning, cross-park knowledge sharing and joint modeling are realized on the premise of ensuring data privacy and security of each park, and the prediction precision of a single park under the condition of limited data is remarkably improved; and finally, through a'prediction-decision-execution-update 'closed-loop process, traditional passive and static energy consumption management is converted into active and dynamic prediction scheduling, so that the energy consumption peak can be stabilized prospectively, the energy distribution can be optimized, and the comprehensive energy consumption cost and carbon emission can be effectively reduced.

The invention discloses an industrial data analysis system and method based on digital twinning and causal inference, and the system comprises a physical sensing layer which is used for collecting multi-source heterogeneous data of an industrial site; the digital twinborn platform layer is used for constructing and operating a virtual twinborn model corresponding to the physical entity; the intelligent analysis engine layer is integrated with a causal analysis module and a federal learning module which are associated; the application and interaction layer is used for visualizing the analysis result and issuing a control instruction; wherein the causal analysis module is used for constructing a causal graph based on the multi-source heterogeneous data and performing causal inference. Through federal learning, on the premise of protecting data privacy of all parties, cross-organization and cross-region collaborative modeling and knowledge sharing are realized.

The invention discloses an automatic business travel itinerary optimization method, and relates to the technical field of intelligent itinerary planning, and the method comprises the steps: integrating the multi-source heterogeneous data of enterprise policies, personal preferences and real-time traffic through a federated learning framework, and achieving the cross-domain knowledge sharing; the method comprises the following steps: constructing a staged optimization engine by adopting an attention mechanism to dynamically balance cost, time, comfort and sustainability targets: in the first stage, modularly disassembling a travel through sparse constraint linear programming, and quickly generating a Pareto frontier candidate set; in the secondary stage, on the basis of a multi-agent reinforcement learning framework, complex interaction is simulated through a Markov decision process, and strategy iteration is driven through a special reward function for quantifying a comfort index; in order to cope with real-time disturbance, event-driven edge computing nodes are deployed, flight delay and traffic jam emergencies are responded in real time, an incremental topology updating algorithm is triggered, and only affected sub-modules are reconstructed to reduce computing complexity. According to the invention, the bottleneck of dynamic adjustment efficiency and multi-target balance capability is solved.

A scalable expert foundry system enables creation, management, and coordination of multiple specialized expert domains, each developing autonomous cognitive capabilities through geometric manifold formation while maintaining hierarchical oversight and cross-domain knowledge transfer. The system utilizes a Persistent Cognitive Machine architecture with hierarchical supervisory networks that provide multi-layered coordination, conflict resolution, and quality management across distributed expert domains. Cross-domain coordinators orchestrate communication and knowledge sharing between domains through geometric abstraction and manifold projection techniques that preserve semantic integrity while enabling beneficial knowledge propagation. Executive manifold supervisors implement second-order control architectures managing meta-cognitive capabilities and system-wide reasoning strategies. The system supports enterprise deployment across multiple geographic regions with distributed computing resources. Expert domains achieve operational readiness through statistical observables monitoring including cache hit rates, distance distribution shifts, and trajectory coherence measurements that validate manifold maturity. The architecture enables scalable expert-level performance across diverse knowledge domains while maintaining coordination effectiveness and quality standards.

A system and method for deep learning using a large codeword model with hierarchical caching is disclosed. The system processes input prompts into tokens, maps them to codewords using a codebook, and processes these through a machine learning core to generate responses. A sophisticated caching architecture stores and retrieves responses across both local and global cache tiers. The local cache maintains frequently accessed responses on edge devices through short-term and persistent storage components, while the global cache enables knowledge sharing across multiple devices. A context aggregator identifies relationships between cached responses to form comprehensive contextual representations. This hierarchical caching system significantly reduces computational requirements by reusing previously generated responses for similar prompts, while continuously optimizing cache contents based on relevance scoring and usage patterns. The approach enables efficient scaling across distributed environments while maintaining response quality.

The invention relates to the technical field of computers, and discloses a multi-agent reinforcement learning fault diagnosis method based on edge-center hybrid optimization, and the method comprises the following steps: S1, collecting and preprocessing equipment data; s2, layered fault diagnosis: an intelligent diagnosis agent adopts a layered reinforcement learning structure and is composed of a high-layer strategy network and a low-layer strategy network; s3, edge-center hybrid optimization: designing a center strategy optimization agent, training a high-layer strategy network and a low-layer strategy network in stages, and performing compression and distillation on the trained strategy networks by adopting a teacher-student strategy structure; executing strategy fusion and global updating based on the received strategy execution information; and S4, strategy migration. According to the method, continuous learning and strategy updating are carried out in a scene in which fault samples are extremely scarce, diagnosis knowledge sharing, strategy synchronization and cross-device migration are realized by using a multi-agent cooperation mechanism, efficient deployment and operation at an edge device end are supported, and field state change is adapted in real time.

System and method for managing information compliance and relevance using autonomous artificial intelligence (AI) agents in data transfer and communication environments. Some embodiments may include a core orchestration engine with multiple autonomous AI agents configured to manage and evaluate the compliance and relevance of information in communication and data transfer environments. The system may use weighted metrics to assess if information and actions comply with regulations and are pertinent to recipients, monitor email and data transfer, ensure regulatory compliance, and enhance information and knowledge sharing within organizations. The system may use semantic embeddings, part-of-speech analysis, and language models to extract and apply regulatory rules efficiently. These features may significantly reduce search space, computational overhead, and manual effort while improving security and accuracy.

The invention belongs to the technical field of artificial intelligence and multi-modal emotion calculation, and discloses a multi-modal emotion recognition method based on a Mama state space model and cross-modal self-distillation. Through the organic combination of the efficient sequence modeling capability of the Mamba state space model and the knowledge sharing mechanism of cross-modal self-distillation, the advantages of the state space model in the aspects of time sequence modeling and calculation efficiency are fully played, and meanwhile, the limitation of a single model architecture is made up through a cross-modal attention mechanism; the technical bottlenecks of an existing multi-modal emotion recognition method in the aspects of long sequence processing efficiency, cross-modal information fusion and knowledge transfer sufficiency are effectively solved, and an efficient and reliable technical solution is provided for further development and practical application of the multi-modal emotion recognition technology.

The invention relates to a permanent magnet synchronous host bearing state detection method and system, and the method comprises the steps: 1, enabling a sharp end of a special measurement rod to directly contact with the surface of a host bearing end cover, and collecting an audio signal and a sound wave signal in the operation of a bearing in real time; secondly, the elevator is made to operate under the three different load working conditions of full load, 50% load and no load, and bearing operation signals under all the working conditions are collected; and step 3, transmitting the collected signals to an intelligent AI analysis platform for preprocessing and feature extraction. According to the invention, the intelligent decision-making system can generate a gradient maintenance scheme considering economy and reliability based on an optimization algorithm of reinforcement learning, effectively prolongs the service life of the bearing, reduces the maintenance cost, guarantees the data safety and traceability through the application of the block chain technology, achieves the collaborative diagnosis and knowledge sharing of cross-brand equipment, and improves the reliability of the bearing. The method has remarkable advantages in the aspects of improving the equipment reliability, optimizing the maintenance strategy, reducing the operation and maintenance cost and the like.

The invention discloses an urban operation monitoring method based on multi-modal data analysis. The method comprises the steps of collecting urban operation monitoring data including event logs, disposal effects and citizen satisfaction; adopting a PC algorithm to construct a causal directed acyclic graph to generate causal analysis data; based on a PPO algorithm, performing dual-objective optimization on the disposal effect and the degree of satisfaction of citizens to generate dynamic weight data; key influence path features and an index weight matrix are extracted, and a weight adjustment basis is generated through SHAP value analysis; a federated learning framework is adopted to fuse differential privacy and security multi-party calculation, and a cross-domain knowledge sharing network is constructed to generate enhanced evaluation data; performing space-time trend prediction based on a GraphSAGE + TCN hybrid model to obtain a final evaluation result; and outputting a visual report including performance prediction, risk early warning and resource suggestion. The deep fusion of causal reasoning and dynamic weight is realized, and the accuracy, adaptability and interpretability of urban operation monitoring are remarkably improved.

The invention provides a customer service system based on large-model multi-agent. The system comprises a semantic sharing platform, a task scheduling strategy module and a large-model agent module. By rebuilding a customer service system architecture and introducing a large-model multi-agent collaboration mechanism, a customer service system with global semantic understanding and task self-adaption capabilities is constructed, and closed-loop optimization from user intention recognition, task execution to feedback learning is realized by a plurality of agents through division of labor, cooperation and context and knowledge sharing. Therefore, the customer service experience and efficiency are comprehensively improved.

The invention discloses a distributed BMS battery pack health assessment method based on federated learning. According to the method, the federal learning architecture is introduced, so that each edge node can finish data acquisition, feature extraction and model training locally, only model parameters instead of original data are uploaded, and the leakage risk caused by concentrated transmission of a large amount of sensitive data is fundamentally avoided. And meanwhile, the coordination server adopts a weighted aggregation strategy to allocate weights according to node sample sizes, so that knowledge contribution of large sample nodes is fully embodied, and global model bias caused by node number difference is avoided. According to the mode of information fusion instead of data fusion, data privacy of all nodes is protected, cross-node knowledge sharing is achieved, and the system can fully utilize multi-source heterogeneous data in the distributed BMS to conduct collaborative modeling while the strict privacy protection requirement is met.

The invention provides a personalized federated learning method based on personalized enhancement and sharing self-adaption, which comprises the following steps: enabling a sharing module in a Transform model to participate in federated aggregation, promoting knowledge migration of each client, storing a personalized module in a local client, and improving the adaptability to heterogeneous data; a Lora low-rank adapter is introduced into a personalized module, only Lora parameters are uploaded, feature expression homogenization caused by global aggregation is avoided, initial global shared module parameters and local parameters of a shared module are subjected to self-adaptive fusion, semantic offset caused by model decoupling is effectively relieved, and the robustness of the system is improved. Therefore, the generalization performance is improved, and the stability and adaptability of a local model are kept; dynamic incremental clustering is performed on each client based on Lora parameters, and shared feature representation with cross-client migration value is mined in a personalized module, so that the calculation overhead is reduced, and the personalized knowledge sharing and migration capability among similar clients is enhanced.

The invention relates to the technical field of medical data management and analysis, in particular to a lymphedema treatment monitoring data management method and system. Comprising the following steps: synchronously acquiring bioelectrical impedance, tissue thermodynamic characteristics and subcutaneous tissue morphological data of a patient through multi-modal data acquisition equipment; transmitting the data to a cloud processing platform in a distributed manner, and generating a three-dimensional illness state assessment map containing edema quantitative indexes through a machine learning assessment model; generating a personalized treatment strategy adjustment scheme with a confidence score based on dynamic comparison between the current evaluation map and historical data; feeding back the evaluation map and the adjustment scheme to the medical staff through the interaction terminal, and receiving a clinical correction instruction; according to the method, on the premise of strictly protecting patient data privacy, cross-mechanism multi-modal knowledge sharing can be realized, and the contradiction between a data island and a model effect is solved.

The invention relates to the technical field of intelligent coal mine safety production, and discloses an intelligent coal mine safety early warning method and system based on deep learning, and the method comprises the steps: constructing a difficulty evaluation function, and achieving the progressive learning from simple to complex; based on a difficulty assessment result, a model-independent meta-learning algorithm is realized, so that the model quickly adapts to new mining area characteristics; constructing a privacy protection federated learning framework by using the meta-learning model, and realizing multi-mining-area cooperative training; a continuous learning module is constructed, and original experience is reserved when new knowledge is learned; constructing a meta-knowledge evaluation module to realize cross-mining-area safety knowledge sharing; according to the invention, the security risk identification accuracy is improved; multi-mining-area cooperative training is realized on the premise of protecting data privacy; the method has continuous optimization capability and effectively solves the problem of model drift; and efficient sharing and migration of cross-mining-area safety knowledge are realized.

The invention relates to an intelligent optimization method and device for a database cluster, equipment and a storage medium. The method comprises the following steps: generating a heterogeneous feature vector based on multi-modal data of the database cluster; inputting the heterogeneous feature vector and the constructed parameter dependent directed graph into a graph neural network model, outputting a high-priority parameter, inputting the heterogeneous feature vector into a parameter adjustment strategy network, generating a parameter adjustment action of the high-priority parameter, and updating the parameter adjustment strategy network on the basis of recording performance feedback data after parameter adjustment; uploading the encrypted encryption model gradient update vectors of the parameter adjustment strategy network to a federated learning server, performing weighted aggregation processing on the encryption model gradient update vectors of the plurality of database clusters to obtain a global model, and broadcasting the decrypted global model to the plurality of database clusters; according to the technical scheme provided by the invention, the precision, dynamic and knowledge sharing of database cluster parameter tuning can be realized, and the performance and operation and maintenance efficiency of the database cluster are remarkably improved.

The invention relates to the technical field of translation, and discloses a translation calibration method based on big data, which can accurately identify semantic deviation in a multi-meaning scene, for example, accurately judge that'cell 'should be translated into'cell' instead of'house 'in a biological text. The system can detect the language field mismatching problem of spoken expression in the official file, and provides term correction suggestions conforming to industry standards. Aiming at technical terms with multiple meanings in legal clauses, the calibration process can be combined with context window analysis to select an optimal translation scheme, the omission ratio of manual review is remarkably reduced, continuous optimization and cross-language knowledge sharing of a translation calibration model are realized, and the accuracy of translation calibration is improved. The problems that a traditional system is lagged in updating and low-resource language performance is insufficient are solved.

The application discloses a robot cognitive development method based on ontology semantics, and comprises the following steps: constructing a robot article identification professional knowledge base based on attribute function and ontology information representation of article definition; information determination based on attribute discrimination and semantic search; and robot cognitive development based on attribute information addition. The application simulates the process of human memory, learning and cognition of articles based on an ontology semantic knowledge base, and through machine learning and sensor attribute information, the robot can actively cognize, learn, expand and accumulate learned knowledge and experience according to information data; the robot can continuously develop its cognitive ability through learning, automatically construct a robot article identification professional knowledge base of unknown articles, and based on a semantic structure of triplets, share knowledge and exchange operation logic between man and machine, so that the cognitive level of the robot is improved and the operation experience of an operator is improved.

The invention belongs to the technical field of intelligent control of industrial heating equipment, and particularly relates to a big data self-learning evolution method for an intelligent burning furnace model of a hot blast stove. According to the method, multi-source data such as fuel components and temperature fields are collected in real time, and a standardized input data set is constructed after wavelet noise reduction and principal component analysis processing. The model can dynamically optimize the air-fuel ratio, predict the hot air temperature and correct the equipment parameter deviation, and stable combustion is achieved. When effective samples are accumulated to a threshold value, the system automatically triggers parameter iteration, key parameters are reserved and secondary parameters are updated by utilizing transfer learning, encryption gradient aggregation and knowledge sharing among multiple furnaces are realized by virtue of federated learning, and distribution and deployment are performed after a global optimization model is formed. And finally, according to the model output, a closed-loop regulation and control fuel valve, a fan and other execution mechanisms are realized, the temperature and energy consumption are monitored in real time to evaluate the evolution effect, and a continuously optimized intelligent control cycle is formed. According to the method, the control precision and the combustion efficiency are remarkably improved, and energy conservation and consumption reduction are effectively achieved.

The invention discloses an ABAC-based power enterprise intelligent office knowledge authority management method, which comprises the following steps of: designing user attributes, knowledge attributes and an access mechanism by adopting an ABAC authority control framework, and constructing authority control models of companies, departments, organizations and personal documents; obtaining the authority control model, constructing an access control strategy library, and performing authority control on large model question and answer content through a question and answer control technology in document vectorization; establishing an authority control performance optimization strategy according to the authority control, and reducing the occupation of authority control check resources; according to the scheme, the strict safety control requirement of a power enterprise can be met, the requirements of knowledge sharing and intelligentization in the enterprise can be met, the consistency and real-time performance of user attribute data are ensured, the effectiveness of authority management is ensured, authority management and control of knowledge ontology and large model questions and answers are realized, knowledge safety is ensured, occupation of authority control check resources is reduced, and the enterprise experience is improved. And the system response speed is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a cross-model knowledge editing and updating method, device, equipment and medium, and the method comprises the steps: receiving a knowledge updating instruction, and generating a knowledge editing vector; writing the knowledge editing vector into an intermediate layer parameter of the target model set to obtain a parameter-updated target model set; generating an input sequence with a regulation mark when the input request and the knowledge update content meet semantic matching conditions, and inputting the input sequence into the target model set to obtain a model output set; performing output alignment and fusion through a consistency fusion module to generate a fusion output result; and when the output conflict is not eliminated, performing a re-reasoning operation based on the knowledge editing vector, and outputting a final result. According to the method, knowledge sharing and dynamic triggering among multiple models are realized through a cross-model mapping and consistency fusion mechanism, and the efficiency, timeliness and credibility of knowledge updating are improved.

The invention discloses a multi-modal knowledge base system construction method oriented to a large language model, which comprises the following steps: S1, creating a knowledge base instance, including setting a knowledge base name, description and visibility parameters; s2, adding a multi-modal document to the knowledge base, wherein the multi-modal document comprises a text, a PDF (Portable Document Format), an image and a table format; s3, configuring large language model parameters including a model identifier, an API key and a basic API address; s4, realizing a knowledge-based question and answer function, and enhancing a generation technology by combining a standardized API (Application Program Interface) with a retrieval; s5, realizing a dynamic scoring function of knowledge entries, and performing multi-dimensional scoring on the entries through a large language model; and S6, managing knowledge base authority, distinguishing a personal knowledge base mode and a shared knowledge base mode, and setting document visibility. According to the method, a multi-modal analysis engine is adopted, unified storage of multi-modal data is supported, lightweight document indexing is adopted, automatic scoring is performed through LLM, manual auditing dependency is reduced, and meanwhile high-quality knowledge sharing is ensured in combination with permission control.

The invention discloses an end-end self-control-based transformer area light storage and charging coordinated regulation and control system and an autonomous method. The system constructs an end-end-transformer three-level autonomous architecture: an end-level autonomous unit is responsible for local millisecond-level reflection control; the edge autonomous nodes realize intra-zone collaboration through federated learning, and physical security constraints are embedded into AI decisions by using digital twin bodies; the court-level federation learning center realizes knowledge sharing under cross-court privacy protection through secure multi-party computing, the method further integrates transfer learning to solve the cold start problem of a new court, and a three-time scale regulation and control mechanism is adopted to cope with disturbance of different rates; the autonomous capability, the response speed, the safety level and the intelligent degree of the system are improved.

The invention provides a Parkinson's disease early recognition system and method based on multi-task learning, and belongs to the technical field of machine learning. The method comprises the following steps: firstly, constructing a Parkinson's disease discrimination data set covering actions, languages and visual modalities, and providing comprehensive symptom data and identities for subsequent analysis; then, in combination with a medical priori rule, constructing a symptom recognition module integrated with a plurality of traditional machine learning models, and performing preliminary judgment on action and language data; a short-term recognition model is constructed to enhance the multi-modal symptom perception ability and improve the discrimination effect of Parkinson's disease short-term recognition; and finally, carrying out mathematical modeling analysis based on the recognition result sequence of the multiple time periods, fusing statistical trend and group difference, and realizing robust comprehensive judgment on the Parkinson's disease. According to the invention, through cooperative training and knowledge sharing among tasks, the comprehensive discrimination capability of the model on the multi-modal pathological signals of the Parkinson's disease is enhanced, so that more accurate and more sensitive early recognition is realized.

The invention relates to the technical field of ISO authentication, and provides an ISO system service authentication process method. Comprising the following steps: constructing an enterprise system association analysis model based on a graph neural network, establishing an authentication strategy optimization mechanism based on reinforcement learning, designing a cross-modal fusion intelligent auditing system, constructing an industry authentication knowledge sharing platform based on federated learning, and implementing a continuous compliance monitoring system based on digital twinning. The method is supported by various frontier artificial intelligence technologies, systematically solves the core problems of low efficiency, inaccurate risk identification, insufficient privacy protection, limited knowledge sharing and the like in the traditional ISO system authentication, and greatly improves the intelligent level and management effect of the authentication process.

The invention relates to the technical field of electric vehicle charging infrastructure intelligent management, and discloses a charging pile load balancing method and system based on a neural network, and the method comprises the steps: analyzing the state, environment and user behavior data of a charging pile through a multi-mode auto-encoder network; generating a unified scene representation; recognizing a typical scene by using a variational auto-encoder and a density clustering algorithm; decomposing the charging load balancing knowledge into cross-scene shared knowledge and scene specific knowledge; automatically generating a neural network architecture adaptive to the current scene based on the scene similarity; using a meta reinforcement learning algorithm to train a meta strategy network to generate a load balancing decision; scene smooth transition is realized through gradual model switching; according to the method, multi-scene adaptation, knowledge sharing and smooth switching are realized, the problems of system redundancy, knowledge isolation, scene switching performance fluctuation and the like in a traditional method are effectively solved, and the operation efficiency of the charging infrastructure is improved.

The invention discloses a multi-source data driven intelligent life body model design method and system for power transformation operation inspection, and the method comprises the steps: collecting global multi-source data from a transformer substation through various sensors, and carrying out the data cleaning, normalization and feature extraction preprocessing operation of the multi-source data; constructing a multi-dimensional data base under global perception; the method comprises the following steps of: constructing a general and special fused industry large model, reversely capturing an application effect of a search result on a search strategy by utilizing a self-adaptive feedback efficient search method, and carrying out cross-domain fusion of brain-like thinking through external information absorption and integration; the system comprises a perception layer, a cognitive layer, a decision-making layer, an execution layer, an interaction layer and a knowledge sharing learning layer. According to the invention, in a man-machine cooperation scene, based on a closed-loop operation mechanism of bidirectional feedback, intelligent assistance and supervision of the model on operation and inspection work are realized; and through a group adaptive learning mode, functions of sharing and iteration of internal cluster knowledge of the model and absorption and integration of external information are realized.

The embodiment of the invention relates to the technical field of traffic flow prediction, and provides an anti-poisoning traffic flow prediction method based on federated learning, and the method comprises the steps that a traffic region client trains an anti-poisoning dynamic graph neural network model; and the traffic area client calculates the credibility of the central server and the credibility of other traffic areas. And selecting a centralized aggregation mode or a decentralized aggregation mode for aggregation according to a credibility calculation result. And each traffic area client updates local model parameters according to the corresponding aggregation result to obtain a local anti-poisoning dynamic graph neural network model. According to the method and the system, the interference of malicious clients can be resisted, the traffic knowledge sharing effect between normal clients is ensured, an optimal balance point is found between prediction performance and safety protection, and the safety of model training is improved.