382 results about "Central node" patented technology

The invention discloses a multi-level governance method for real-time data of new energy station power generation equipment, and particularly relates to the field of power data governance, and the method comprises the steps: cooperatively generating an edge governance rule and a central processing logic according to a global data governance strategy, and transmitting the edge rule to an edge node; and meanwhile, the data quality is monitored in real time, and a result is fed back to dynamically adjust a global data governance strategy, so that closed-loop optimization of data governance is realized. According to the multi-level treatment method for the real-time data of the new energy station power generation equipment, an edge treatment rule set and a central processing logic set are generated through driving of a global data treatment strategy, and the strategy is dynamically adjusted based on third treatment data; the problem that the edge node processing rule and the central data processing logic lack collaboration and dynamic adaptation is effectively solved; by implementing cross-level timestamp synchronous calibration, secondary alignment processing of the center node on the multi-source heterogeneous data is avoided, and the equipment state evaluation precision is effectively improved.

The invention relates to the technical field of micro-grid energy optimization, in particular to a distributed federal reinforcement learning-based hydrogen-containing micro-grid energy scheduling method, which comprises the steps of constructing a multi-region hydrogen-containing micro-grid system model, designing a state space, an action space and a reward function of an intelligent agent, constructing an Actor-Critic network and an experience pool, and completing environment initialization. The intelligent agent inputs the operation state of the equipment into the Actor network, updates the state of the equipment according to the output action, verifies the constraint and outputs a reward value; tuples are extracted from the experience pool to update local network parameters, and the exploration rate is updated regularly; when a federation interaction period is reached, exchanging Critic network parameters and updating federation parameters; and when the training round arrives, outputting an equipment operation plan, deploying the model to the local hydrogen-containing micro-grid in the island mode, and outputting an equipment output value. According to the scheme, strategy sharing and learning collaboration are realized through neighborhood collaboration and local communication among the regional intelligent agents, so that dependence on a central node is avoided.

Disclosed in the present application is a blockchain-based distributed ensemble learning method. On the basis of a designed three-layer blockchain structure, which consists of a mini block, an ensemble block and a key block, by means of a consensus protocol, a miner in a network is enabled to train a base model on a training set after performing sampling with replacement, and aggregates models from other miners; and finally, information of the base model and information of an ensemble model are recorded in a blockchain, such that the whole process of model training, model ensemble and model evaluation is fused into a blockchain consensus, and the whole ensemble learning process is automatically executed in a blockchain network. In this way, the present application can increase the utilization rate of the computing power in a blockchain network by means of a proof-of-useful-work mechanism based on machine learning, and a center node is prevented from being introduced during a model aggregation process, thereby maximizing the degree of decentralization of a blockchain.

The invention discloses a multi-chain collaborative center node switching method for ensuring authentication efficiency and data reliability, which is applied to a system comprising an identity chain, an authentication chain, an edge computing center, a key generation center, a plurality of unmanned aerial vehicle clusters and at least two aerial directors, and is characterized in that the identity chain is used for identity management of all the unmanned aerial vehicle clusters; the authentication chain is jointly maintained by all the unmanned aerial vehicle clusters and all the air directors. According to the multi-chain collaborative central node switching method for ensuring the authentication efficiency and the data reliability provided by the invention, the secure communication between the clusters can be quickly established in the authentication process of the unmanned aerial vehicle clusters, and the switching of the central nodes can be quickly carried out when the central nodes are threatened and attacked in a multi-chain manner; the new central node continues to execute the flight task, and the forward and backward security is ensured in the switching process, so that the problems of low authentication efficiency of the unmanned aerial vehicle cluster, strong dependency of the central node and insufficient security in the switching process of the central node are solved.

The invention discloses a data hierarchical aggregation and query method and system for privacy protection in a 6G network, and the method comprises six stages: a system initialization stage, a data uploading and edge aggregation stage, an edge data preprocessing stage, an edge classification query request stage, a central data preprocessing stage, and a central node classification query request stage. The system initialization stage, the data uploading and edge aggregation stage, the edge data preprocessing stage and the center data preprocessing stage only need to be executed once, and then the edge classification query request stage and the center node classification query request stage can be responded unlimitedly. According to the method, the privacy protection problem in the data hierarchical aggregation and query process of the AI service in the 6G network is effectively solved.

The invention provides a task risk early warning method and system based on project management data, and relates to the technical field of project management early warning. According to the method, the task risk index weight is automatically adjusted to adapt to process states and external environment changes in different stages of a project, the accuracy and flexibility of risk assessment are remarkably improved, and good dynamic adaptability is achieved; performing grading evaluation on tasks in each stage of the project by means of a quantitative comprehensive risk value, determining a management priority, and assisting a project team to focus on key risks; a task cooperation relationship is visually presented by constructing a task cooperation network, a center node and a fragile link are accurately identified, and a risk propagation path is effectively grasped; and real-time monitoring and graded early warning are realized in combination with a dynamic risk early warning rule, so that the risk can be found and dealt with in time.

The invention provides a heavy-load unmanned aerial vehicle cooperative hoisting method, device and equipment and a medium, and relates to the technical field of edge calculation, and the method comprises the steps that a virtual pilot unmanned aerial vehicle in an unmanned aerial vehicle cluster calculates total load data of loads hoisted by a plurality of edge nodes based on load distribution data of each edge node; based on the total load data and the node position, determining an overall motion track of the unmanned aerial vehicle cluster, and sending the overall motion track to the edge node, so that the edge node finds a reference tension value needing to be provided and a node motion track based on an ant colony algorithm; if it is determined that the lifting rope of the first unmanned aerial vehicle in the multiple unmanned aerial vehicles is broken in the flight process, according to a preset load redistribution scheme, synchronous tension is distributed to each second unmanned aerial vehicle; and setting the first unmanned aerial vehicle as a proxy center node, and receiving an updated movement track of the proxy center node for the unmanned aerial vehicle cluster. According to the invention, the sudden fault in the cooperative lifting process of the multiple unmanned aerial vehicles can be quickly processed.

The invention discloses an agent collaborative generation method based on dynamic sharing of bureau-school interconnection teaching resources, which comprises the following steps: S1, constructing a bureau-school interconnection network system, and deploying a resource management agent which comprises a data acquisition module, a semantic analysis module, a decision module and a communication module; s2, collecting school teaching data, and sending the school teaching data to a central node of an education management department; s3, performing semantic annotation and classification on the received data, and constructing a dynamic teaching resource semantic database; s4, constructing an intelligent agent collaborative decision-making model, and dynamically adjusting a sharing strategy of teaching resources; s5, generating an optimal resource sharing scheme through the dynamic teaching resource semantic database and the intelligent agent collaborative decision-making model, and sending the optimal resource sharing scheme to the school child node; and S6, after sharing is completed, a resource sharing scheme is fed back to the center node, and the agent collaborative decision model is optimized. According to the invention, intellectualization, dynamics and precision of teaching resource sharing are realized, and a brand new technology is provided for balanced configuration of education resources.

The invention discloses a distributed privacy protection method and system based on federated learning, and relates to the related field of privacy protection, and the method comprises the steps: a hospital end carries out the homomorphic encryption of patient data, calls an initial model through the encrypted data, and carries out the incremental training, and generates a first model parameter set; sending to a central node, aggregating by using a secure multi-party computing protocol, and generating a first aggregation parameter group; after differential privacy noise is added, the data is transmitted back to a hospital end, local patient data is continuously called to carry out incremental learning after homomorphic encryption, and so on until model prediction precision meets constraints, and a target model parameter set is generated; and after differential privacy noise is added, the data is transmitted back to the hospital end for target model generation. The technical problems that existing privacy protection is large in calculation overhead and high in communication cost, and accurate aggregation of model parameters is difficult to achieve while privacy is guaranteed are solved, and the technical effects of small calculation overhead, low communication cost, privacy protection and accurate aggregation of the model parameters are achieved.

The invention belongs to the technical field of agent safety control, and relates to a distributed cooperative safety control method applied to a multi-agent system, and the method comprises the steps: S1, building a kinematic model of an omnidirectional mobile agent, and generating a nominal speed instruction according to a preset track; s2, determining position information of each agent and broadcasting the position information to other agents; s3, determining a candidate zero-crossing obstacle function; s4, generating a security constraint by using the candidate zero-crossing obstacle function; s5, determining a loss function based on the nominal speed, and solving a control quantity which enables the loss function to be minimum on the premise of meeting security constraints as a security control instruction; and S6, controlling the intelligent body to move according to the safety control instruction. The method has the beneficial effects that in a large-scale agent cluster, even if global information and a central node are lacked, collision among the agents can be effectively avoided, meanwhile, falling into a local optimal solution is avoided, and it is ensured that the agents are safely navigated to a specified target point.

The invention is suitable for the technical field of unmanned aerial vehicle cluster cooperative control, and provides an edge calculation distribution system and method for unmanned aerial vehicle cluster cooperative tasks, and the system comprises a multi-heterogeneous neural network module, a local fusion module, a global fusion module, a conflict resolution module, a task distribution module, and a self-adaptive optimization module. According to the invention, by constructing a multi-level semantic feature extraction and fusion architecture and combining an intelligent conflict resolution mechanism and a dynamic task allocation algorithm, efficient intelligent collaborative operation of an unmanned aerial vehicle cluster in a complex and changeable environment is realized, all core algorithms can be executed in a distributed manner in the local of the unmanned aerial vehicle through a distributed edge computing architecture, and the distributed edge computing architecture can be applied to the unmanned aerial vehicle cluster. Dependence on a central node is remarkably reduced, communication overhead and delay are greatly reduced, cooperative work can still be maintained even if part of nodes fail, and perfect balance between robustness and performance is achieved.

The invention relates to a distance perception-based graph division retrieval method and system and a readable storage medium. The method comprises the following steps of: constructing original data into an initial graph structure; distance measurement is selected, a weight is allocated to each edge, and the nearest neighbor node pairs are combined based on the weights to obtain a coarsened graph structure; performing hierarchical segmentation on the coarsened image structure, and projecting the coarsened image structure back to the initial image structure based on the segmentation condition to obtain a plurality of initial cut edge sets; finding a corresponding node according to each initial cut edge in the initial cut edge set, randomly putting the nodes into different partitions, and optimizing each initial cut edge set by taking the sum of minimum cut edge weights as a target function to obtain a graph index structure; and inputting a query, calling the graph index structure, respectively calculating the distance between the query and the central node of each partition of the graph index structure, and selecting the nearest partition for retrieval. According to the method, the number of times of cross-partition query is reduced, and the query efficiency is improved.

The invention belongs to the technical field of data processing, and provides a data management optimization method and system. The method comprises the following steps: based on an acquisition strategy, acquiring log and business data of each child node, and calculating a data acquisition success rate and a data acquisition delay evaluation value to measure an acquisition condition; transmitting the collected logs and service data of each child node to a central node, carrying out multi-class data processing, and calculating and determining a quantitative index of a data processing delay condition so as to optimize a data processing process; monitoring the data after processing the multiple types of data in real time, identifying a risk event by using a self-constructed risk event identification model, and calculating a risk score of the risk event, including analyzing and calculating a risk monitoring result so as to update a monitoring rule and a feature library of the risk event identification model; and when the calculated risk score exceeds a preset threshold value, an alarm mechanism is triggered, and alarm information is pushed to safety management personnel of the related child nodes. According to the invention, the multi-node data acquisition and data processing process is effectively optimized.

The invention discloses a power distribution network short-circuit parameter real-time monitoring method, system and equipment based on edge calculation and a medium, and belongs to the technical field of power system monitoring and protection, and the method comprises the steps: collecting data information in real time at each monitoring node of a power distribution network, generating a state vector reflecting a node operation state, and inputting the state vector to an edge calculation unit, processing through an internal first calculation model to obtain a power grid fault evaluation result of a corresponding node, and compressing and uploading the power grid fault evaluation result to a cloud; based on the uploaded data and the system operation state, a system performance evaluation result is generated in real time, and an adaptive optimization algorithm is used for dynamically adjusting internal model parameters of the edge calculation unit. According to the method, the composite algorithm model fusing feature extraction and parameter calculation is deployed at the edge node, the problems that a traditional centralized processing mode is large in data transmission delay and central node calculation pressure is concentrated are solved, and the technical effect of reducing bandwidth occupation of a backbone communication network is achieved.

The invention discloses a hybrid federal learning modeling method and device for a neurocognitive impairment evolution trajectory, and the method comprises the steps that a child node receives an expert global model distributed by a central node, and the expert global model is determined by the central node from an expert global model group according to metadata pre-registered by the child node; taking the expert global model as an initial model, and performing localization training by using the local multi-modal non-invasive physiological signal data to obtain a trained local model; extracting global sharing parameters in the trained local model, and uploading the global sharing parameters to a central node; and for the corresponding expert global model, the central node aggregates global shared parameters uploaded by all the child nodes training the expert global model to obtain an updated corresponding expert global model, issues the updated expert global model to the child nodes, and circulates until a termination condition is met. According to the method, the heterogeneous data of the child nodes are adapted, privacy is protected, and the adaptability and accuracy of trajectory prediction are improved.

The invention provides an intelligent question answering method and system based on a traditional Chinese medicine classical meta-term engine. The method comprises the following steps: identifying and extracting core traditional Chinese medicine terms and a user query intention in a user question; the core traditional Chinese medicine terms are input into a pre-constructed traditional Chinese medicine classical meta-term engine to be processed, normalized mapping and semantic association extension of the terms are completed, and the engine is constructed based on a classification-term-word list-semantic CTVS association framework. Comprising a traditional Chinese medicine classical book term ancient and modern double-classification system C layer, a meta-term extraction and standardization T layer, a dynamic word list V layer and a semantic association network S layer. Carrying out graph pattern matching and reasoning in the S layer, traversing and integrating a plurality of deep association paths by taking classical element terms and corresponding modern clinical terms as double center nodes, and retrieving answer information associated with the query intention; and carrying out integration sorting and structured assembly on the answer information to generate a structured answer. Intelligent questions and answers can be performed on traditional Chinese medicine related questions of the user.

The invention discloses a hybrid cloud-oriented multi-cluster scheduling method and device and a medium, and relates to the technical field of distributed system resource management. The method comprises the following steps: continuously collecting multi-dimensional state data of a cluster to be scheduled, and encrypting and transmitting the multi-dimensional state data to a central node to obtain a global state view; if the multi-dimensional state data meets a preset triggering condition, analyzing a service strategy associated with the global state view to obtain a structured constraint condition and weight configuration; removing clusters contained in the global state view based on a structured constraint condition to obtain a candidate cluster list; calculating a cost factor, a performance factor, a utilization rate factor and a stability factor of each candidate cluster in the candidate cluster list, and performing weighted calculation with the weight configuration to obtain a comprehensive score of each candidate cluster; and extracting the cluster with the highest comprehensive score as a target cluster, and calling the integrated scheduling executor to perform flow scheduling and resource scheduling on the target cluster.

The invention provides an equipment networking method and system based on a distributed soft bus, and the method comprises the steps: starting a distributed soft bus module and carrying out the discovery of adjacent equipment, and obtaining a trusted equipment set after identity verification; a point-to-point communication link between the devices is established according to the trusted device set, and a distributed mesh interconnection topology is formed; performing routing calculation and data forwarding on data transmission in the mesh topology to realize end-to-end communication; meanwhile, dynamic changes of equipment in the network are monitored, topology updating is carried out, and the network stability is maintained. The scheme does not depend on the dependence of the traditional star network on the central node, realizes direct interconnection between devices through the distributed soft bus technology, improves the fault-tolerant capability and transmission efficiency of the network, and solves the problems of poor network reliability and limited expansibility in the prior art.

The invention discloses an interpretable clinical decision support system based on label generation and a knowledge graph. The method comprises the following steps: based on a breast cancer domain knowledge enhanced version Qwen-BrCaAdapt of a general large language model Qwen, analyzing an unstructured medical record text of a patient, and generating a structured result containing tags, values, evidences and explanations; calculating a reasoning label through a path matching engine by utilizing an editable structured path rule table, and matching a candidate treatment scheme according to the reasoning label; and taking the matched treatment scheme as a central node, calling a medical knowledge graph to bind entity information including clinical evidence, recommendation levels, medical insurance information, medication risks, usage and dosage, and generating a traceable JSON structure and a visual report. The method has the beneficial effects that the accuracy and efficiency of tag generation in the breast cancer field are improved, the rule maintenance cost is reduced, the interpretability and traceability of a clinical decision scheme are enhanced, and the acceptability of a doctor to a recommendation result is improved.

The invention relates to the technical field of image processing, in particular to a retinal vessel extraction method based on a strong feature dynamic fusion gating network, which adopts strong feature dynamic fusion gating U-Net, designs a core module as a center node by introducing a centrality thought in a graph theory, dynamically aggregates key information of a multi-layer encoder, and improves the extraction efficiency of the retinal vessel. Therefore, effective fusion of global and local features is realized. The feature fusion hub module balances the relationship between global context information and local detail features by using the long-range dependency relationship of multilayer features, and the adaptive gating residual block screens key features and inhibits redundant information through a dynamic gating mechanism, so that the segmentation robustness in complex vascular morphology and lesion regions is enhanced, and the segmentation efficiency is improved. The parallel information focusing module combines a channel and a space attention mechanism, global position information and long-range dependence are effectively modeled, fine-grained and complex features of blood vessels are captured, and the segmentation performance is further improved.

The invention discloses a global unique identifier generation method and system under edge computing, belongs to the technical field of edge computing, and aims to solve the technical problem of how to generate a unique identifier under edge computing according to the environmental characteristics of edge computing, and the method has high efficiency, uniqueness, incremental performance and safety at the same time. According to the technical scheme, the method comprises the following steps: applying for a unique identifier: an edge node sends a node ID of the edge node to a center node, and further applies for a global unique identifier from the center node; calculating a timestamp: after the center node receives the application of the edge node, acquiring the current accurate time, and calculating the timestamp with the length of 40 bits; determining a service number: directly using the ID submitted by the edge node as a 12-bit service number by the center node; acquiring and progressively increasing a serial number: acquiring the current serial number from the database by the central node, and progressively increasing the current serial number by 1 to obtain a 12-bit serial number; encrypting the confused ID; and returning the unique identifier.

The invention relates to a task scheduling method and system, electronic equipment and a storage medium. The method comprises the following steps: respectively acquiring task attribute information corresponding to a plurality of to-be-processed tasks, and respectively acquiring resource attribute information corresponding to a plurality of edge nodes; based on the task attribute information and the resource attribute information, distributing the plurality of to-be-processed tasks to corresponding edge nodes for processing; in the task processing process, if it is monitored that the resource utilization rate of any edge node is overloaded, other edge nodes or center nodes are called for cooperative processing. According to the scheme, the task execution efficiency, the resource utilization rate and the task scheduling efficiency can be improved.

The invention discloses a high-dimensional data retrieval method based on distributed computing and mixed indexing. The method comprises the following steps: step 1, data preprocessing; step 2, cluster generation; step 3, index construction; step 4, consistent Hash: distributing each cluster to a virtual node of a Hash ring, dynamically adjusting data distribution on the virtual node according to a load strategy, and mapping the virtual node to a physical server; and step 5, hierarchical aggregation: mapping a query request to a node and adjacent nodes on a hash ring through a hash function, performing candidate set calculation in parallel, merging candidate sets sent by all the nodes by a Spark center node, and finally optimizing a result. According to the method, high-recall-rate and low-delay distributed retrieval is achieved through mixed index construction, dynamic consistency hash allocation and hierarchical aggregation strategies, meanwhile, system load balancing and index updating efficiency are guaranteed, and the method is suitable for large-scale high-dimensional data scenes.

The invention discloses a sample processing method, device and system and a storage medium, relates to the technical field of machine learning, and is used for improving the robustness and generalization ability of a network model and solving the problem of poor robustness of a network model of a neural network in related technologies. The method comprises the steps that a child node sends sample information of at least one sample to a center node, the center node receives the sample information of the at least one sample from the child node, then for each sample, indication information of the sample is generated based on the sample information of the sample, and the indication information of the at least one sample is sent to the child node, and after receiving the indication information for at least one sample from the central node, the child node processes the sample based on the indication information of each sample.

The invention is suitable for the field of power systems, and provides a power grid attack defense method, terminal equipment and a storage medium. The power grid attack defense method comprises the following steps: an edge node determines local abnormal information and a first response strategy according to local communication data, wherein the local abnormal information represents power grid attack information suffered by the edge node; determining local association information according to the local exception information and shared exception information of the adjacent nodes, wherein the local association information represents an association relationship between the edge node and the plurality of adjacent nodes; sending the local association information to the central node; obtaining traceability information obtained by the central node according to the local association information, wherein the traceability information represents attack source information and attack path information of the power grid attack; and determining a second response strategy according to the traceability information and the local abnormal information. Through the synergistic effect of the edge nodes and the center node, the full-chain identification and multi-point protection capabilities of the power system in a complex attack environment are improved.

A federated learning method includes a central node that separately sends a first model to at least one central edge device, receives at least one second model, and aggregates the at least one second model to obtain a fourth model. The at least one central edge device is in one-to-one correspondence with at least one edge device group. The second model is obtained by aggregating a third model respectively obtained by each edge device in at least one edge device group. The third model is obtained by one edge device in collaboration with at least one terminal device in a coverage area through learning the first model based on local data. The edge devices are grouped into edge device groups, and a central edge device in one edge device group sends the first model to each edge device in the edge device group.

The invention discloses an intelligent equipment management method and an equipment management system thereof, and relates to the technical field of equipment management.The method comprises the steps that key operation parameters of equipment are collected through multiple types of sensors, and data processing is executed; transmitting the encrypted data to a central node in real time through a communication module, and completing data cleaning and redundant backup; performing training, prediction, diagnosis and result generation on the equipment state by using a decision tree model in an artificial intelligence engine; according to the intelligent analysis result, fault warning, authority verification and control instruction issuing execution are carried out through the terminal module; and collecting execution feedback data, continuously optimizing the decision tree model, and realizing equipment management closed loop and intelligent evolution. The invention provides an intelligent equipment management method and system integrating multi-source sensing, decision tree intelligent analysis, security authentication and multi-level control, real-time monitoring, fault prediction and automatic control of the equipment state are realized, and the intelligence and reliability of equipment management are improved.

The invention discloses a collaborative awareness and recognition method and system based on an unmanned aerial vehicle cluster, and relates to the field of unmanned aerial vehicle cluster control and computer vision. The method comprises the steps of starting ground control software to load a route path file, constructing a sequence image optical scene and evaluating the effect, dynamically fusing and evaluating the effect of a wide-area optical scene, intelligently and collaboratively recognizing an optical target, confirming and judging collaborative recognition data, and counting and evaluating recognition accuracy. The system comprises a central node unmanned aerial vehicle subsystem, an edge node unmanned aerial vehicle subsystem and ground control software. According to the method, the central node and the edge nodes cooperatively work, the global scheduling decision-making capability of the central node and the real-time computing capability of the edge nodes are exerted, efficient coverage of a wide-area scene, high-resolution image splicing and cooperative recognition and tracking of a dynamic target are achieved, the problems of unbalanced computing load, information redundancy and the like of a traditional architecture are solved, and the method is suitable for large-scale popularization and application. The image quality and the target recognition accuracy are improved, the reliability is high, and the expandability is high.

The invention discloses a multi-cluster hot debugging method based on Service Mesh, and belongs to the technical field of cloud native software development. Through a debugging instruction, a multi-cluster intelligent routing gateway establishes a unified and logically isolated debugging session and a virtual network environment for a developer terminal and a plurality of target Kubernetes clusters; the gateway dynamically allocates a local port for a debugging task from a global resource pool, and instructs a target cluster to create a Pod; the gateway serves as a center node, performs cross-cluster routing conflict detection, generates a globally consistent dynamic routing rule and issues the dynamic routing rule to a target cluster; and finally, forwarding the online traffic matched with the rule to an appointed port of a developer local machine through a proxy Pod and a gateway. Through a centralized intelligent coordination architecture, various conflicts in a multi-cluster environment are fundamentally avoided, and the debugging efficiency, stability and automation level are remarkably improved.

The invention relates to the technical field of data processing, and particularly discloses a discrete data collection method and system based on hierarchical aggregation, by constructing a hierarchical aggregation architecture of computing nodes, intermediate aggregation nodes and a central node, the computing nodes cache discrete data to a local pre-write log queue, and the central node stores the discrete data in the local pre-write log queue; when a time window or a data volume threshold value is met, the data are subjected to batch processing compression and then sent to the corresponding intermediate aggregation nodes, the intermediate aggregation nodes decompress the data, aggregate and deduplicate the data and send the aggregated data to the center node through a pre-write log and a check point persistence state, and the center node can achieve non-repeated write-in of the aggregated data; and a back pressure signal can be sent to adjust the transmission rhythm of the intermediate aggregation node, and data collection is ensured to meet accurate primary semantics through cooperation of a transaction ID, a check point and an idempotent interface. The method can eliminate the I / O bottleneck of the central node, improves the collection efficiency and data reliability, enhances the fault-tolerant capability, and is suitable for discrete data collection of a large-scale distributed system.