324 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 relates to the technical field of airport intelligent management, in particular to an airport intelligent operating system, which comprises a multi-modal data acquisition layer for acquiring multi-source data such as flights, passengers and luggage in real time through a heterogeneous sensor network; the intelligent analysis layer processes data by using deep learning and a unique algorithm, and generates information such as flight delay prediction and the like; the collaborative scheduling layer realizes cross-domain dynamic optimization of flight-boarding gate-luggage system-ground service based on digital twinning and multi-agent reinforcement learning; the man-machine interaction layer is equipped with AR navigation and voice interaction equipment and provides personalized services; in addition, a block chain evidence storage module is arranged to ensure that key operation records are safe and traceable, and a federal learning framework realizes multi-airport knowledge sharing and privacy protection; through multi-module cooperation, the airport operation efficiency is improved, the safety is enhanced, the passenger service experience is optimized, many problems faced by traditional airport operation are effectively solved, and the system has remarkable innovativeness and practicability.

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.

The invention belongs to the field of machine learning, computer vision and target tracking, and discloses a multi-modal unified target tracking method based on modal unified representation. Visible light, depth, infrared, event and natural language modalities are expressed in a unified marking form through a multi-modal embedding layer, so that it is possible to train a Transform model to extract and fuse joint features of multiple modalities, and a target tracking model capable of processing different multi-modal input signals is developed. Besides, a task identification training strategy is introduced in the method to enhance the ability of the model to distinguish different modal tracking tasks, soft tag type embedding is proposed to provide accurate tag type information for the model, and the performance of the multi-modal unified model is further improved. Finally, according to the method, different multi-modal tracking tasks are solved through a unified scheme, architecture unification, model unification and knowledge sharing among different tasks are achieved, and good tracking performance is obtained on the five multi-modal tracking tasks.

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 relates to the technical field of natural language processing, and discloses a natural language processing system based on Transform, comprising: a neural architecture search assembly for automatically searching and optimizing a Transform model architecture for natural language processing; the multi-task learning component is used for simultaneously training a plurality of natural language processing tasks on the basis of the shared model layer; the meta-learning component is used for optimizing parameter initialization of the model, so that the model can quickly adapt to a new task through a small amount of data and training steps; and the dynamic data enhancement component is used for generating diversified training data and enhancing the generalization ability and robustness of the model. According to the method, knowledge sharing and resource efficiency are enhanced through the multi-task learning and meta-learning component, dependence on a large amount of data is reduced, the generalization ability and rapid adaptability of the model are improved, the adaptability to a new scene is enhanced through the dynamic data enhancement component, and the robustness of the model is improved.

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 discloses a network intrusion detection method based on multi-task deep learning, and the method comprises the steps: obtaining an intrusion detection data set, carrying out the mapping and one-hot coding of attack class labels of the intrusion detection data set, and obtaining the features of the intrusion detection data set; preprocessing the intrusion detection data set features to obtain a preprocessed intrusion detection data set; constructing a multi-task learning model based on hard sharing, and inputting the preprocessed intrusion detection data set into the multi-task learning model based on hard sharing for training to obtain a trained multi-task learning model based on hard sharing; and obtaining a target intrusion detection data set, inputting the trained hard sharing-based multi-task learning model for detection, and obtaining a detection result of the intrusion network. According to the method, through knowledge sharing among different tasks, the detection accuracy can be obviously optimized, and various network attacks can be effectively identified and responded.

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 present invention belongs to the fields of machine learning, computer vision, and object tracking, and discloses a multi-modal unified object tracking method based on modal unified representation. Through a multi-modal embedding layer, visible light, depth, infrared, event, and natural language modalities are represented in a unified token form, making it possible to train a Transformer model for joint feature extraction and fusion of multiple modalities, thereby developing an object tracking model that can handle different multi-modal input signals. In addition, a task recognition training strategy is introduced in this method to enhance the model's ability to distinguish different modal tracking tasks, and soft token type embedding is proposed to provide the model with accurate token type information, further improving the performance of this multi-modal unified model. Finally, this method solves different multi-modal tracking tasks through a unified scheme, achieving architecture unification, model unification, and knowledge sharing among different tasks, and achieving good tracking performance on five multi-modal tracking tasks.

The invention discloses a code review method, system and device, a storage medium and a program product, and the method comprises the steps: cleaning a to-be-reviewed code snippet, and obtaining an incremental code snippet and position information; vectorizing the incremental code snippets; semantic query is conducted on the vectorized incremental code snippet data based on an ES database according to the cosine law, development rules of similar semantics are obtained and ranked from high to low according to similarity values, and a preset number of first several snippets are obtained; recombining cue words, wherein the recombining cue words comprise incremental code snippets and a preset number of development rules; and sending the recombined cue word to a general generative large model to obtain an examination suggestion. The code snippets to be examined are pushed to the gilab warehouse by a developer, and are cleaned by the gilab warehouse through webhook triggering. According to the review method, the whole process is automatic, the review efficiency is high, review can be conducted according to the development rules in the team, knowledge sharing is achieved, the review result is objective, and the problem that the text exceeds the upper limit is avoided.

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.

The invention discloses a federal learning knowledge distillation method based on data-free driving, and belongs to the technical field of personalized updating of federal learning. Comprising the steps that each client constructs a classification network and a generative adversarial network; the server side constructs a global model and a teacher model; each client enhances the private data set, is used for training a classification network and a generative adversarial network, and feeds back trained generator and classifier model parameters to the server; and the server side updates the global generator and the global classifier, trains the global model and the teacher model by using the distillation data, and issues optimized model parameters of the global generator and the global classifier to each client. According to the invention, rich knowledge of a teacher network is utilized to guide mutual learning with a global model at a server side; and a MixDistrition data enhancement technology is introduced into a client to carry out personalized updating, so that bidirectional flow and knowledge sharing of data are realized, and problems caused by data isomerism are relieved.

The invention provides an adaptive clustering federal learning method based on knowledge migration. In each round of training of the central server, the server calculates a similarity matrix according to client model parameters, and performs adaptive clustering grouping on the clients; performing weighted average aggregation on the client model parameters in the same group to obtain an intra-group model; through a knowledge migration strategy, the group model is optimized, knowledge sharing between different groups is promoted, and the merging and clustering process is accelerated; and after the self-adaptive clustering is completed, carrying out intra-group federation training. And the server performs federal average aggregation on the model parameters of the clients in the group, and updates the group model. Inputting local data into the updated model to obtain a prediction result; and constructing a loss function between a prediction result and a real label, and repeating the training step by taking loss minimization as a target. According to the training method provided by the invention, the problems of deviation and difficulty in convergence caused by data heterogeneity can be effectively solved, and the individuation and generalization performance of the model can be improved.

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.