1154 results about "Node count" patented technology

The invention relates to the technical field of power network resource management, and discloses a power network resource intelligent management method and system based on 6G and deep learning. The method comprises the following steps: acquiring node data through a 6G technology to obtain a topological mapping table, preprocessing the data to obtain a feature tensor, training a model cluster by adopting a space-time diagram convolution and long-short-term memory network hybrid architecture, analyzing a network state based on a model, generating a scheduling scheme, and issuing a control instruction through 6G network fragmentation to realize intelligent management of a power network. According to the invention, through the combination of the 6G communication positioning integration technology and the space-time diagram convolutional network and long-short term memory network mixed architecture, the real-time data acquisition precision and intelligent analysis capability of the power network are improved. Based on a collaborative optimization mechanism of multi-source data fusion and a deep learning model cluster, the comprehensiveness of power grid state evaluation and the accuracy of resource scheduling are enhanced.

The invention discloses an active power distribution network fault positioning and identification method and system based on a space-time diagram network, and relates to the technical field of active power distribution network fault positioning and identification, and the method comprises the steps: obtaining data of each node of an active power distribution network after a fault occurs, building structured diagram data, marking fault nodes and types, and constructing a sample data set; pre-training the fault diagnosis model based on the space-time diagram network by taking the sample data set as source domain data; in each iteration process, a multi-scale adversarial disturbance addition method based on gradient optimization is adopted to generate adversarial sample data, through a data category dynamic balance mechanism and a confidence evaluation mechanism, the data are screened and added to a data set, and the updated data set is utilized to train a model; introducing target domain data, and finely tuning the pre-training model by adopting a transfer learning strategy based on dynamic coring maximum mean difference; and inputting actually acquired node data into the model to realize accurate positioning of fault nodes and accurate identification of fault types.

The invention discloses a node classification method and system based on a hybrid expert model.The node classification method comprises the steps that node data are input into a pre-trained hybrid expert model to obtain a classification result of the node data, and the hybrid expert model comprises a node mode extractor, a gating model and a plurality of classification models; the node mode extractor is used for extracting node mode information from given node data, and the gating model is used for receiving the node mode information and generating a model combination weight corresponding to each node; and dynamically selecting and combining the classification results of the node data by the plurality of different types of classification models according to the model combination weights corresponding to the nodes so as to obtain the final classification result of the nodes. The invention aims to solve the problem of node classification under a complex graph structure, accurately define a node mode under the condition that the node mode is complex, and properly combine different node predictors for different nodes.

The invention discloses a digital content tracing system based on a block chain technology, and particularly relates to the technical field of block chains. The tampering risk of the digital content in the distributed storage process is evaluated by analyzing the multi-node data synchronization behavior; the transaction overstock risk judgment module evaluates the transaction overstock and local congestion hidden danger according to the transaction verification rate in the propagation process; and the reliability evaluation module judges the reliability of the digital content tracing chain in combination with the tampering risk and the local congestion hidden danger. The abnormal risk assessment module analyzes operation behavior abnormity through the intelligent contract log, and the conflict possibility assessment module detects consistency deviation of circulation records; and the potential risk assessment module synthesizes the multi-dimensional risk, generates a potential risk assessment result of the tracing chain, and determines whether to trigger an on-chain responsibility node tracing verification mechanism, so that potential hidden dangers in the tracing chain can be comprehensively identified, and an efficient and reliable solution is provided for block chain digital content tracing.

The invention relates to the technical field of fault diagnosis, in particular to a big data-based bus duct fault diagnosis system, which comprises a data synchronization module, an anomaly identification module, an amplitude judgment module, a traceability positioning module and a risk classification module. According to the invention, automatic synchronization of multi-node data is realized through a time correlation mode, layered discrimination is carried out on an abnormal working condition state by means of continuous comparison of data streams, dynamic identification of node parameter fluctuation is realized through interval adjustment and trend aggregation, and abnormal signals are positioned and traced in a link topology. The node operation risk is comprehensively researched and judged in combination with historical and real-time parameters, linkage analysis and active classification of multi-dimensional information are formed, efficient connection of links such as monitoring, recognition, tracing and classification is promoted, operation and maintenance decision and data full-process response are supported, node difference judgment and risk prompt in a complex scene are facilitated, and the method is suitable for popularization and application. And intelligent operation and maintenance of power supply system data driving are improved.

The invention belongs to the field of fault monitoring, and particularly relates to a fault self-healing method and system under an intelligent operation and maintenance global P2P architecture, and the method comprises the steps: constructing a global operation and maintenance monitoring double-layer node network which comprises a basic monitoring subnet and a collaborative management and control subnet; the basic monitoring subnet collects platform operation data in real time according to a preset inspection strategy, determines an abnormal root node and an association node through causal association analysis, generates an early warning signal and uploads the early warning signal to the collaborative management and control subnet; after the collaborative management and control subnet receives the early warning, a fault self-recovery strategy is started, resources are dynamically allocated, abnormal node data batch migration and recovery are executed, and a self-recovery result is fed back to the basic monitoring subnet to trigger loop check until abnormity is eliminated; according to the method, real-time monitoring and intelligent analysis of operation and maintenance data are realized through cooperative work of the double-layer node network, and disaster tolerance and load adaptive adjustment capability of the system are effectively improved in combination with an automatic fault self-healing mechanism and data difference migration.

The invention provides a real estate full life cycle intelligent management method and system based on Internet of Things perception, and the method comprises the steps: distributing a unique hardware identifier of equipment, combining a lightweight digital certificate with a pre-shared key to achieve two-factor authentication, and storing a root certificate in a cloud end; an access authentication process is optimized, an identity label and a certificate signature request need to be submitted when a node accesses for the first time, a temporary session key is generated after the gateway verifies, and abnormal access is immediately isolated and reported; data integrity verification is implemented, and after a sensor collects data, an abstract is generated and data encryption transmission is carried out; after decryption, the receiving end recalculates the abstract for comparison, and if not, the abstract is discarded and nodes are marked to be abnormal; a data anomaly detection model is constructed, energy consumption fluctuation is determined based on historical data, a frequency baseline is collected, data reasonability is monitored in real time, secondary authentication is triggered when the data is abnormal, and node data access is suspended if the data fails; a hierarchical key system is established, hardware is solidified by a root key, session keys are alternated for 24 hours, and data encryption keys are dynamically derived and distributed through encryption channels.

The invention discloses a data transmission method and device, equipment and a medium, and relates to the technical field of computers. By adopting a redundancy architecture of multiple direct memory access channels and combining a channel transmission mode based on a trigger condition and a target screening strategy, the data transmission efficiency is improved. The appropriate target direct memory access channel is selected from the multiple direct memory access channels to execute the data transmission task, so that when a certain direct memory access channel has an error, the system can be automatically switched to other direct memory access channels to continue to execute the data transmission task; overall interruption of the non-transparent bridge link caused by a single direct memory access channel fault is avoided, meanwhile, for the direct memory access fault, the fault channel can be accurately repaired by adopting a fault-level-based hierarchical repair strategy, the whole non-transparent bridge link and all devices do not need to be reinitialized, and the fault channel repair efficiency is improved. The fault processing time is greatly shortened, the fault recovery efficiency is effectively improved, and the continuity of cross-storage node data transmission is guaranteed.

The invention relates to the technical field of edge computing, and discloses a network data efficient caching method and system based on edge computing, which realize dynamic identification of hotspot data and optimal caching position decision by analyzing edge node data flow characteristics and network topology states in real time. Firstly, data packet meta-information is extracted from an edge node, a content feature vector is generated, and data popularity is evaluated; secondly, analyzing a network topology structure and a link state, and screening candidate cache nodes; then, factors such as service priority, network reliability and topology stability are fused, and an optimal cache position is determined by adopting a reinforcement learning algorithm; and finally, the cache content is updated, the cache state is synchronized between the edge nodes, and the cache distribution is optimized. The data access efficiency and the resource utilization rate of the edge network can be effectively improved, and the method adapts to dynamically changing network environments and user requirements.

The invention discloses an electronic lead seal data management method based on a block chain, relates to the technical field of internet data services, and aims to solve the problems of node data asynchronization, state updating failure and potential safety hazards of an intelligent contract in the prior art. The data consistency is ensured through a distributed clock synchronization algorithm and a version number verification mechanism; the reliability and fault tolerance of state updating are improved by adopting an improved Byzantine fault-tolerant algorithm and an asynchronous two-stage transaction submission mechanism; data integrity and traceability are guaranteed through a state machine detection mechanism and transaction log records; the security and maintainability of the smart contract are improved based on an automatic code auditing algorithm and a proxy contract mode of a graph neural network; real-time data monitoring and exception repair are realized by using an under-chain auditing mechanism and a cross-chain data consistency verification protocol; according to the invention, the data consistency, the system safety and the reliability of electronic lead seal data management are obviously improved.

The invention relates to the technical field of execution traceability, in particular to a block chain-based flood storage and detention area ecological compensation execution traceability processing method and system, and the method comprises the following steps: extracting remote sensing data to generate a delay factor list, recognizing ecological trigger nodes to generate a calibration node data set, constructing a reconciliation path structure, and extracting boundary nodes; and generating a permission mapping and responsibility signature block, and writing the permission mapping and responsibility signature block into a responsibility tracking chain structure area. According to the method, logic reconciliation is carried out on compensation and payment behaviors through timestamps and hash records, node tracking and continuity inspection of fund payment paths are realized, operation authority matching is carried out in combination with a contract authority table after time jump is identified and boundary nodes are calibrated, and a responsibility tracking structure chain is constructed step by step; according to the method, responsibility binding of fund behavior nodes and whole-process signature traceability and chain traceability of a transfer behavior are effectively realized, the authenticity discrimination capability of compensation execution and the operation responsibility traceability capability are enhanced, and the verification precision and process integrity of a fund execution path are improved.

The invention relates to the technical field of automatic operation and maintenance, in particular to a visual operation and maintenance data analysis system which comprises a node data collection module, an index sorting and screening module, a multi-parameter linkage recognition module, a resource node screening module and an event track tracing module. And analyzing operation data of each node, and grouping node types according to time labels. According to the method, the operation data of different nodes are subjected to hierarchical grouping and archiving, dynamic linkage merging of various operation and maintenance parameters is realized in combination with time labels, operation and maintenance concerned objects are automatically screened under a multi-index change trend, and accurate screening of abnormal nodes and resource redundancy nodes is completed according to a comprehensive feature track. The operation and maintenance data is promoted to be converted from single-point monitoring to full-link linkage analysis, and the intuition and the globality of operation and maintenance event response are improved in the visual archiving and tracing process, so that systematic closed-loop monitoring of node states, resource allocation and operation and maintenance risks is promoted.

The invention discloses a multi-split cross-platform distributed monitoring system state data acquisition method, which belongs to the field of system operation and maintenance and data monitoring, and comprises the following steps: acquiring node data through a data acquisition module arranged on a distributed monitoring system node, and introducing a self-adaptive acquisition strategy into the data acquisition module, adaptively adjusting the node data acquisition frequency according to the resource utilization condition; a node data monitoring strategy is formulated according to user requirements, and the monitoring strategy is optimized by using a hybrid monitoring algorithm; a Delta compression technology is utilized to compress node data to obtain a compressed data packet, the compressed data packet is transmitted, and a multi-level transmission optimization mechanism is introduced in the transmission process of the compressed data packet. According to the method, the technologies such as message bus publishing and subscribing, a customized data acquisition strategy and data flow optimization are integrated, so that consistent state monitoring can be realized among different platforms and hardware architectures, and the efficiency and reliability of system operation and maintenance are greatly improved.

The invention relates to the technical field of data processing, and particularly discloses an intelligent cache replacement method and system based on application awareness, and the method comprises the steps: sensing a cache state of an application scene, and determining a candidate cache replacement strategy according to a cache replacement strategy matching rule; the method comprises the following steps: collecting data of a storage node data access mode, a cache access state, a capacity state and a power consumption state of an application scene, and preprocessing the data to generate historical data; according to historical data, training a GRU model to predict future cache hit information, and determining a cache hit rate of the application scene; calculating strategy benefits according to the cache hit rate and the evaluation index of the cache hit rate, and comparing the candidate cache replacement strategy with the current cache replacement strategy; and when the cache hit rate is lower than a preset threshold value and a strategy benefit comparison result exceeds a set value, configuring the candidate cache replacement strategy as a current cache replacement strategy.

The invention discloses a communication fault diagnosis method and device based on a power system, equipment and a medium. The method comprises the following steps: S1, collecting and outputting multi-source data; s2, preprocessing the multi-source data; s3, performing topological constraint attention mechanism weighting processing on the data matrix; s4, performing dynamic threshold calculation on the fusion feature matrix; s5, positioning the abnormal node list; s6, calculating to obtain a posterior probability; s7, matching the rule base to obtain a standardized disposal instruction; according to the method, the communication traffic is collected through the compressed sensing collection algorithm, the line impedance parameters are integrated into the observation matrix in combination with the topological parameters, key node data are collected preferentially, and the effective data acquisition rate is increased; the accuracy of switch false alarm identification is improved by outputting a time-space unified fusion feature matrix; the computing resources are focused to the abnormal nodes and neighborhoods thereof through the abnormal node mask matrix, so that the positioning precision of the aged equipment is improved, and the judgment omission rate caused by switch aging is prevented from being improved.

The invention relates to the technical field of intelligent manufacturing and block chains, in particular to a production process traceability method of a hub type transmission device, which comprises the following steps: acquiring three types of operation data and aggregating to generate a synchronous value, calculating a parameter rate to judge whether the parameter rate exceeds a limit and extract a sudden change section, comparing a trend with a threshold value and reconstructing into a clamping state node, and packaging the signature and broadcasting to generate a verification node, and analyzing the node to supplement a link to generate right confirmation data. According to the method, parameter synchronism is calculated and recognized through the offset rate, the linkage analysis capability among multi-dimensional data is improved, accurate recognition of a sudden change section is achieved through comparison of continuous period parameter variation difference and double threshold values, and abnormal fragments are effectively extracted and an independent structure is formed by combining trend consistency and pressure difference value judgment; a signature and broadcast mechanism ensures the consistency of node data, link comparison and breakpoint reconstruction maintain the integrity of a data link, and the anomaly recognition precision, the node credibility and the production data closed-loop management capability are enhanced.

The invention provides a computing power service dynamic resource allocation method and system applied to AI model training. The method comprises the following steps: firstly, collecting real-time computing power resource use data (including computing node load, memory occupation and data transmission delay) and model training state data (including training task stage identification, model parameter updating frequency and training data batch processing progress) in AI model training; generating a computing power resource demand association feature set, constructing a computing power resource dynamic allocation decision model including resource allocation priority judgment, adjustment amplitude calculation and scheduling opportunity selection units based on the set, and outputting a computing power resource allocation scheme (including computing node number, memory capacity and data transmission bandwidth adjustment instructions) through the model. Resources are scheduled according to the scheme, and new data are collected to update the feature set, so that dynamic and accurate allocation of computing power resources is realized, and the resource utilization rate and the training efficiency are improved.

The invention provides a computing power center resource centralized self-adaptive control scheduling system and method, and the method comprises the steps: S1, a user submits a computing power training task through an API, and sends the task to a DAG task composer, and forms a task list to be scheduled; s2, the intelligent monitoring analysis module is used for continuously collecting node data and calculating real-time CS and HS scores of each node; s3, analyzing the dependency relationship type data by a task composer, and calling a dynamic programming algorithm in an adaptive scheduler to make a decision; s4, the adaptive scheduler performs dynamic task distribution according to the task list of the DAG task orchestrator and the intelligent monitoring analysis module, and S5, the distributed runtime and environment management module is used for uploading the prepared Docker mirror image to the distributed storage; and S6, through the unified communication adaptation layer, the mirror image is pulled from the distributed storage on the target node and the container is started, the user does not need to concern the difference of the operating system in the whole process, and efficient and intelligent adaptive scheduling is realized.

The invention relates to the technical field of identity verification, in particular to an enterprise registration self-service terminal and an identity verification method and system thereof, and the method comprises the following steps: comparing dynamic two-dimensional code parameters item by item based on the enterprise registration self-service terminal, a verification serial number, a card issuer identifier and a validity period, and judging the consistency of each field; and according to the matching of the result and the registration system information, identifying the offset condition of the handling process, and outputting a sequence offset index. According to the invention, through synchronous acquisition of multiple parameters of the identity card, in combination with two-dimensional code dynamic feature comparison and chip data analysis, real-time association of identity data in three aspects of time, source and content is realized, each node is bound with a unique identifier, an equipment source and time sequence relationship, and a node data abstract series full-process log chain is handled; interrupt, offset and risk handling nodes can be positioned in the whole process, a data chain structure supports afterward tracing and node-level traceability, and handling safety, data integrity and traceability are remarkably improved.

The present application relates to the technical field of middleware, and in particular to a cache data semi-synchronization method, comprising comprises the following steps: writing and running a distributed cache proxy layer service using a preset programming language, receiving a read / write command request from an application client, parsing a Key consistent hashing value and sending same to a rear-end Redis sharding node. A master instance parses a message packet, marks a write operation and records same in an AOF file. Upon write to disk, the master instance sends an AOF file transfer command to a slave instance node, and the slave instance acknowledges reception. When receiving a reception response, the master instance updates an acknowledgment offset of the slave node and responds to the client. When network delay is high, data of a master node and a slave node is marked as "asynchronous", and it is automatically downgraded to asynchronous replication. According to the present application, by means of a self-developed Redis kernel, an asynchronous ack mechanism is used to optimize redis to achieve master-slave semi-synchronization, thereby enhancing data reliability of a single point of failure, and eliminating the influence introduced by a native Redis asynchronous replication mechanism such as data delay and data consistency.

Disclosed herein are methods and systems for generating and using a shared knowledge language. In one embodiment, a method comprises retrieving data generated as a result of at least one computing device accessing one or more applications from a set of applications; generating a computer model comprising a set of nodes where each node having an identifier corresponding to a series of nouns and verbs generated in accordance with a schema associated with a shared knowledge language; when the processor receives a request: identifying a node that corresponds to the request; retrieving a pair of related nodes within the nodal data structure the processor identifying whether the relevance threshold has been satisfied using each respective node's identifier corresponding to the schema.

The invention belongs to the technical field of cloud side-end collaborative data processing, and discloses a cloud side-end collaborative heterogeneous data fusion processing system, which establishes dynamic association of data features, resource states and service priorities through a scheduling module, and adopts a reinforcement learning algorithm combining DQN and an attention mechanism to perform fusion processing on heterogeneous data. Task allocation weights are dynamically adjusted with the minimum delay, the highest precision and the maximum business value as targets; the sensing module and the preprocessing module complete lightweight processing such as feature extraction and noise reduction redundancy elimination at the edge end, and the cross-node data transmission amount is reduced. The complex fusion task dispatches cloud computing power support, and reserves resources in advance by means of an LSTM-based task urgency predictor; the cross-modal fusion module designs a double-stage fusion algorithm, in the first stage, cross-modal semantic association is enhanced through multi-head self-attention, and in the second stage, the fusion weight is adjusted according to the reliability coefficient of the sensing module.

The invention relates to the technical field of data storage, in particular to a distributed data storage and access processing method. The method comprises the following steps: presetting a guide configuration file in a distributed storage cluster to carry out identity authentication and communication link establishment on each storage node and a cluster management center so as to generate a security data interaction channel; the data sending end equipment sends a data storage request to the cluster management center through the secure data interaction channel and issues data fragments to proper storage nodes; the data receiving end equipment sends a data access request to the cluster management center through the security data interaction channel, generates an access path and feeds back the access path to the data receiving end equipment; the corresponding resource use condition in each storage node is monitored in real time, and when a preset threshold value is reached, the resource is reallocated to other storage nodes according to a preset data migration strategy and a load balancing algorithm based on a resource use insufficiency result. According to the invention, efficient storage and rapid access of data in a distributed environment can be realized.

To provide a node that can maintain the number of nodes and continuity of transactions even when a specification change occurs in a distributed ledger program that causes inconsistencies in consensus formation.SOLUTION: Provided is a node for a first distributed ledger system that manages a first distributed ledger, comprising: a control unit for, together with some other nodes of the first distributed ledger system, generating a second distributed ledger and registering contents of a plurality of blocks included in the first distributed ledger as blocks of the second distributed ledger by performing consensus formation in order from the lowest block height; and a communication unit for, after the contents of all blocks included in the first distributed ledger have been registered in the second distributed ledger, synchronizing the blocks registered in the second distributed ledger with the remaining other nodes of the first distributed ledger system.SELECTED DRAWING: Figure 1

Provided in the embodiments of the present application are a human-computer interaction method, a human-computer interaction model training method and an electronic device. The human-computer interaction method comprises: determining a knowledge graph corresponding to input question data, and by means of a graph model, obtaining, from the knowledge graph, node data corresponding to a node matching the question data; on the basis of the node data, generating input data which can be accepted by a first interactive language model, wherein the first interactive language model is a language model used for executing an open-ended task; and on the basis of the input data and the question data, performing answer generation by means of the first interactive language model, so as to generate answer data corresponding to the question data. By means of the embodiments of the present application, not only can the accuracy of task processing be ensured, but the capability of an interactive language model processing an open-ended task can also be fully utilized, such that the solution has better openness and can be flexibly applicable to various scenarios and environments.

The invention discloses a fragment reconfiguration method and system based on node credibility election and electronic equipment, and the method comprises the following steps: S1, initializing Epoch, and generating and broadcasting a random number; s2, performing ascending sorting on all nodes, calculating right increment of adjacent nodes, storing the right increment into an increment array, and performing ascending sorting on the array; s3, recording the current pledge Stake value Si of the node in the transaction; smoothing processing is carried out on the Stake; s4, each Epoch calculates the credibility of the node i of the next Epoch through PoCE according to Si, Ei, Hi, Li and Pi pledged by any node i in each fragment; s5, eliminating consensus committee nodes; s6, the credibility of all candidate nodes is calculated by applying PoCE, and n / 10 + min (m / 10, 1024-n) nodes are selected as new consensus nodes through an FTS algorithm; the number of the consensus nodes after updating is n + min (m / 10, 1024-n). According to the method, PoCE based on node reputation is provided, and a part of nodes are recombined according to the node reputation, so that the consensus committee can reserve multiple honest nodes, the committee scale is optimized, the overhead is reduced, and the security of the consensus process is improved.

The invention provides a decision tree security reasoning method and framework based on an efficient element selection protocol, and belongs to the technical field of data security processing. According to the method, nodes and user attributes of a decision tree are converted into two-dimensional matrix representation from one-dimensional vectors, and a constant round communication conversion protocol is designed based on function secret sharing, so that a ciphertext selection index is converted into two ciphertext one-hot vectors; and performing ciphertext dot product operation on the two-dimensional matrix and the two one-hot vectors to complete an element security selection function. After a one-dimensional vector is converted into a two-dimensional matrix, through ciphertext dot product operation, the communication size is not directly proportional to the number of nodes and the number of attributes any more and is only directly proportional to the square root of the number of elements. According to the method, a secure dot product and function secret sharing technology based on secret sharing is combined, the communication size is reduced to be in direct proportion to a square root from being in direct proportion to the number of nodes and the number of attributes, communication and calculation expenses are remarkably reduced, and an efficient privacy protection decision tree reasoning framework is provided.

The invention relates to the technical field of prestressed structures, in particular to a fully parameterized prestressed steel beam model construction method and system.The fully parameterized prestressed steel beam model construction method comprises the following steps that stage vertical displacement data are obtained, abnormal values are screened out, a stage data set is obtained, node data are selected according to the steel beam path relation, and differences are calculated and sorted; the method comprises the steps of dynamically monitoring node displacement, correcting node tension values in combination with friction resistance and anchorage device slippage, screening support node abnormity, finely adjusting node positions, extracting temperature data, adjusting node tension parameters and obtaining a full-parameterization steel beam layout data set, and in the method, node displacement is dynamically monitored, the displacement difference of starting and ending nodes of a steel beam path is adjusted, node tension force is corrected in real time, and a full-parameterization steel beam layout data set is obtained. And a full-parameterization steel beam layout data set is obtained by combining the support node displacement and the temperature change and optimizing the tension parameters, so that the structure is matched with the dynamic change in the construction process, and the adaptability and the long-term reliability of the structure are enhanced.

The invention discloses a distributed data consistency management system and method based on a block chain, and relates to the field of block chain distributed data management, the system comprises a client layer, a block chain layer and a storage layer, the client layer comprises a user interface and a data request, the user interface is used for a user and an application to submit a data read-write request, and the block chain layer is used for storing the data request; the data request is used for formatting the request and signing. The data states of all nodes are ensured to be consistent through a block chain consensus mechanism, an intelligent contract automatically executes a consistency rule, human errors are reduced, content addressing ensures that stored data cannot be tampered, a decentralized architecture has no single-point fault, data multi-copy storage improves the fault-tolerant capability, the block chain cannot be tampered, and the data credibility is enhanced. The interstellar file system stores large files, reduces block chain storage pressure, a database efficiently manages metadata and supports complex query, local cache improves hotspot data access speed, and digital signature ensures identity authentication and non-repudiation.

The invention provides a gray-based continuous deployment method, device and equipment, a medium and a program product, which can be applied to the technical fields of artificial intelligence and software development. The method comprises the following steps: acquiring a first deployment strategy corresponding to a first deployment task, wherein the first deployment strategy comprises the number of nodes participating in gray deployment and the proportion of nodes of different risk levels; selecting nodes from the node pools of different risk levels according to a first deployment strategy to obtain a target batch of nodes; executing the first deployment task in the target batch node; wherein the node pools with different risk levels are obtained by continuously updating according to a second trigger logic, and the updating process of the node pools with different risk levels comprises the following steps: dividing the plurality of nodes into the node pools with different risk levels based on the node features of the plurality of nodes by using a trained risk prediction model, wherein the node features of the plurality of nodes are obtained by continuously updating according to the first trigger logic.