274 results about "Knowledge sharing" patented technology

A system and method for implementing a convergent intelligence fabric (CIF) for distributed artificial intelligence operations. The CIF architecture integrates tensor-theoretic foundations, probabilistic cache management, precision-aware memory operations, quantum-resistant security, and neural-based optimization within a unified framework. The system orchestrates asynchronous, multi-hop data flow among computational resources while maintaining data security through per-block encryption and identity-based access control. Key components include a universal multi-model KV cache subsystem, agent-parallel disaggregation pipelines, reinforcement learning-based orchestration, and neuromorphic memory integration. Advanced implementations incorporate graphon-enhanced memory for sparse graph sequences, multi-modal cognitive persistent memory, and quantum-resistant asynchronous multi-domain trust protocols. The system enables efficient cross-agent collaboration, sophisticated knowledge sharing, and secure cross-domain operations while optimizing computational resources and maintaining strict privacy guarantees across distributed AI deployments.

The invention discloses a causal atlas-driven enterprise intelligent decision-making method and system, and belongs to the technical field of enterprise management. The method comprises the following steps: constructing an enterprise-level causal atlas by obtaining enterprise business processes, operating indexes and unstructured text data; executing path reasoning based on the to-be-decided data, and determining a multi-hop causal path chain; determining a current association rule and a decision suggestion in combination with an expert rule base; causal maps and rule tags of different industries are introduced, and a migratable causal path and an association rule are identified based on structural similarity and a semantic mapping rule; and finally, current and migration knowledge is fused to generate an enterprise decision execution scheme and a causal reasoning result. According to the scheme, based on comprehensive application of current and migrated knowledge, scientificity, accuracy and interpretability of decision making are effectively enhanced, meanwhile, dynamic updating and inter-industry knowledge sharing are supported, enterprise decision making efficiency and response speed are remarkably improved, and enterprises are assisted to achieve intelligent and fine management.

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 relates to the technical field of federated learning, and discloses a federated learning method and system based on multiple agents and knowledge distillation, and a medium, and the method comprises the steps: S1, training a local model; s2, knowledge distillation based on decoupling; s3, decentralized knowledge sharing based on a block chain: packaging the standardized distillation knowledge fragments extracted in S2 and metadata thereof into block chain transactions, submitting the block chain transactions to a block chain network, verifying the legality of the transactions, and writing the transactions into a block chain account book to realize decentralized distribution; s4, performing multi-agent collaborative knowledge management based on a large language model, and updating a student model; and S5, repeatedly executing the steps S1 to S4 until a training termination condition is met. According to the method, personalized selection, adaptive filtering and efficient sharing of knowledge in the federated learning process are realized, the client drift phenomenon caused by data non-independent identical distribution characteristics is effectively relieved, the convergence speed, the overall performance and the personalized level of the model in a heterogeneous data environment are remarkably improved, and the communication overhead between clients is optimized.

The invention discloses a multi-modal traffic large model real-time regulation and control method and system for vehicle-road cooperation, and relates to the technical field of artificial intelligence, and the method comprises the steps: combining multi-modal data fusion, edge intelligent reasoning, federated learning, cross-regional knowledge migration, reinforcement learning optimization and adaptive closed loop iteration; and efficient and accurate vehicle-road cooperative regulation and control are realized. The model is adopted to perform space-time alignment and high-dimensional feature extraction on vehicle-mounted, roadside and cloud data, so that the environmental perception precision is improved; cross-regional traffic knowledge sharing is realized through gradient aggregation and decentralized training, and data privacy leakage is avoided; a transfer learning and self-supervision mechanism is adopted, so that the model can quickly adapt to different cities and different road environments, and the generalization ability is improved; by adopting cloud multi-agent reinforcement learning, the optimal decision of signal lamp timing and path recommendation is realized, the traffic flow change is dynamically adapted, and the problem that efficient and real-time model adjustment cannot be realized in consideration of privacy and global optimization in the prior art is solved.

A system and method for cross-domain knowledge transfer in federated compression networks. The system enables efficient lossless data compression across diverse data types by intelligently sharing compression strategies between domains. A cross-domain knowledge transfer system identifies relationships between different data domains, adapts compression parameters accordingly, and optimizes learning processes to maximize knowledge reuse. The architecture may include a knowledge repository for storing domain features and compression patterns, domain mapping components that identify similarities, and transfer learning optimization that enables efficient adaptation with minimal examples. This approach significantly accelerates model training for new domains while improving compression performance. Applications include satellite telemetry systems where efficient compression is critical for transmitting large information sets between distant locations. The system may employ probability prediction driven arithmetic coding paired with long short-term memory networks, enhanced by cross-domain knowledge sharing that adapts successful compression strategies from one domain to another while preserving domain-specific optimization.

The invention provides a personalized federal learning method and system for a shared model, and the method comprises the steps: enabling a server to store and initialize the shared model, a global model and a global class prototype of a client, and enabling the client to initialize a local learning weight vector; the server sends sharing models of other clients to one client, and the client sets a local sharing model as a global model and trains the global model, and uploads the sharing model and a local class prototype to the server; and the server calculates and obtains a global class prototype and a global model according to all the received shared models and local class prototypes. According to the method, the knowledge sharing problem in federal learning is solved, and the performance of a personalized model is improved; the problem of offset in local model training of the client is solved, and the obtained personalized prototype contains more abundant global information than the personalized prototype; while state dependence and communication overhead are reduced, effective fusion of global knowledge and local characteristics is realized so as to improve robustness and adaptability of the model in a complex scene.

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 a network security situation awareness system based on federated learning driving, and relates to the technical field of distributed computing. The visual management center is in communication connection with a data acquisition module, a distributed federation learning module, a virtual storage management module, a security policy analysis module and an automatic response processing module, and all the modules are in electric signal connection. Through the federated learning technology, the security situation awareness model is locally trained on the multiple distributed nodes, centralized storage and transmission of data are avoided, the risk problem of data privacy leakage in a traditional network security situation awareness system is effectively solved, each node only processes and analyzes data locally, data uploading is not needed, and the network security situation awareness system is convenient to use. Therefore, on the premise of ensuring data security and privacy, cross-node knowledge sharing and model optimization are realized, and the data privacy protection capability of the system is greatly improved.

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.

The invention relates to the technical field of education, in particular to an artificial intelligence large model based on the preschool education field and a model training method. Comprising a multi-modal data acquisition module used for acquiring visual expression data, voice and intonation data, interactive behavior data and learning progress data of preschool children in real time; the emotional state quantification module is connected to the multi-modal data acquisition module and is used for performing feature extraction and fusion analysis on the acquired multivariate heterogeneous data; the federated learning coordination module is deployed on the cloud server and is used for coordinating model parameter aggregation and distribution of a plurality of preschool education institution clients; the personalized path generation module is used for generating an adaptive learning path based on a global model and local data features under a federated learning framework; according to the method, on the premise of strictly protecting child data privacy, cross-mechanism multi-modal knowledge sharing can be realized, and the contradiction between a data island and a model effect 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.

The invention provides a low-altitude multi-agent cooperative control method and system, and belongs to the technical field of cooperative control, and the method comprises the steps: carrying out the fusion of data collected by a plurality of sensors for any agent, and obtaining a fusion feature vector; wherein the plurality of sensors are deployed on the intelligent agent; performing semantic coding on the fusion feature vector, constructing a local knowledge graph, and performing semantic compression on the local knowledge graph to obtain compressed shared knowledge; transmitting the compressed shared knowledge to the other agents, and updating the local knowledge graph according to the received compressed shared knowledge transmitted by the other agents to obtain an updated knowledge graph; and performing multi-agent cooperative control according to the updated knowledge graph of all the agents. According to the method, the task execution efficiency of the multiple agents in the low-altitude scene is improved by combining the self-adaptive multi-modal fusion algorithm and a knowledge sharing mechanism among the multiple agents.

The invention relates to the technical field of home appliance maintenance, in particular to a cross-brand home appliance maintenance knowledge sharing system and method based on federated learning, a multi-mode fault data collection and intelligent maintenance system integrates image, text, fault phenomena and multi-mode instruction data collection, and after pre-processing, the multi-mode fault data collection and intelligent maintenance system is established. A feature decoupling model is established through a maintenance knowledge analysis module, a cross-brand maintenance knowledge base stores general and specific fault information and assists knowledge migration, a federal learning training module adopts differential privacy homomorphic encryption, data privacy is protected, gradient aggregation cooperative training is achieved, a reality maintenance guidance terminal is augmented to visually guide maintenance, the efficiency is improved, and the maintenance efficiency is improved. The preventive maintenance module calculates the equipment maintenance cycle, generates a prevention scheme, and reduces the failure rate and cost. The system significantly improves the maintenance accuracy, rapidly fuses into a new brand, realizes knowledge sharing and utilization, and brings revolutionary revolutions to the household appliance maintenance industry.

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 provides a block chain-based distributed federated learning Internet of Vehicles knowledge sharing method, and relates to the field of block chains and Internet of Vehicles. Comprising the following steps: firstly, adopting a neural factor decomposition machine (NFM) as a prediction network, and selecting a vehicle node most suitable for participating in learning in combination with a dynamic adaptive node selection algorithm; secondly, the vehicle node transmits the learning result as transaction data to a roadside unit, and the roadside unit generates candidate blocks and ensures transparency and non-tampering of the data through a consensus mechanism based on knowledge proof; thirdly, global model updating is carried out in the edge cloud service by utilizing a knowledge distillation technology, and the model performance is optimized; and finally, constructing a non-cooperative game excitation model of vehicle nodes and roadside units, performing reasonable excitation distribution by adopting a deep Q network (DQN), and encouraging the nodes to actively participate in learning and contribute. Through the block chain technology and the distributed federated learning mechanism, the efficiency and security of knowledge sharing in the Internet of Vehicles environment are significantly improved.

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 a collaborative distillation personalized federal learning method for multi-user semantic communication, and belongs to the technical field of artificial intelligence and wireless communication. The semantic consistency in a task group is ensured through a clustering method, meanwhile, personalized training is performed on a semantic encoder in the task group, the adaptability of a semantic communication model is improved in combination with a partial parameter sharing strategy, and finally, global semantic knowledge is shared through inter-group collaborative knowledge distillation; the method comprises the following specific steps: clustering and grouping clients participating in training, constructing a hierarchical semantic communication model, performing hierarchical personalized federated learning training in a task group, and performing knowledge sharing based on collaborative distillation to obtain a semantic communication model of collaborative distillation personalized federated learning. The method can accurately reflect preferences, and is high in individuation performance, high in semantic recovery accuracy, high in semantic communication model generalization ability, high in data processing heterogeneity and high in task diversity.

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.