1830 results about "Chain network" patented technology

The invention relates to the technical field of server upgrading, and discloses a server firmware remote upgrading method and system, and the method comprises the steps: a management node obtains a device identifier list and target firmware version information of a to-be-upgraded server, and uses a distributed task scheduling algorithm to divide tasks; transmitting an upgrading instruction and a data packet through an encryption channel, and generating an incremental packet by using a differential upgrading technology; data transmission is guaranteed through heartbeat detection and breakpoint continuous transmission; after pre-verification of the server-side security sandbox, upgrading and isolating tasks in an idle period; verifying by adopting a gray release strategy, and automatically rolling back if the verification fails; and storing an upgrade state and a log by means of a block chain network. The system comprises corresponding function modules. According to the method, the upgrading efficiency, safety and reliability are improved, the influence on services is reduced, data cannot be tampered and can be traced, the problems existing in a traditional upgrading mode are effectively solved, and the method is suitable for various server firmware remote upgrading scenes.

The invention provides an unstructured file parallel synchronization method and system, and relates to the technical field of file synchronization, and the method comprises the steps: carrying out the intelligent partitioning of a target file through a pre-training file partitioning model, and generating a double-layer cascade index number and a data hash value; and then, according to the data transmission priority model, transmitting the data sub-blocks to a target end database through a self-adaptive parallel transmission channel, and performing asynchronous verification. And finally, recombining the data sub-blocks based on the double-layer cascade index number and the data block association graph by utilizing a pre-training file recombining model, generating a synchronous target file, and submitting confirmation information to the block chain network. According to the method, the unstructured file synchronization efficiency can be improved, the data integrity and security are guaranteed, and the network transmission pressure and the storage cost are reduced.

A system may include an Al convergence system of systems for a value chain network environment.

The invention discloses a trusted data space cross-domain collaborative analysis method based on privacy computing, which comprises the following steps: step 1, trusted data space construction: edge computing nodes are deployed in a plurality of data owners participating in cross-domain collaborative analysis, and each edge computing node is interconnected through a block chain network to form a distributed trusted data space; a strategy engine is configured in the trusted data space, and the strategy engine is used for defining a data use permission, a privacy protection level and a calculation task life cycle management rule; and step 2, dynamically selecting privacy computing protocols, and selecting at least one privacy computing protocol from a pre-configured protocol library according to the data type, privacy protection requirement and computing complexity of the collaborative analysis task. When the scheme is implemented, the data participates in calculation in a ciphertext or desensitization form in the whole process, and the original data is not out of the domain, so that the privacy compliance requirement is met; and data use rules are automatically executed through block chain evidence storage and the intelligent contract, so that malicious node tampering is prevented.

The invention discloses a digital asset secure transaction system and method based on a block chain, and belongs to the cross field of block chain technology and financial science and technology. The system adopts a layered architecture design and comprises an intelligent contract execution layer, a distributed account book storage layer and a cross-chain interaction layer. The transaction method comprises the following steps: generating a digital identity certificate based on asymmetric encryption, and realizing privacy protection through zero-knowledge proof; a secure transaction channel is constructed by adopting a multi-signature mechanism, and sensitive data processing is performed in combination with a trusted execution environment; and designing a dynamic fragmentation strategy to optimize the transaction throughput, and establishing an on-chain and off-chain collaborative verification mechanism. The innovation point is that a double-layer verification model combining a verifiable delay function and a threshold signature is provided, and millisecond-level confirmation is realized while the transaction irreversibility is ensured. The system supports multi-chain asset atomic exchange, and interoperability of different block chain networks is realized through a heterogeneous cross-chain gateway. The scheme has the characteristics of high transaction confirmation speed, high privacy protection level and high system expansibility.

The invention relates to the technical field of data processing, and discloses an AI knowledge base construction method and system for intelligent water conservancy. The method comprises the following steps: forming a water conservancy intelligent perception data set by integrating hydrological monitoring data, engineering operation records and expert literatures; knowledge sprouts are identified, and a water conservancy native knowledge unit is generated; key features are extracted, knowledge element decomposition is carried out, and a knowledge evolution chain network is constructed through correlation analysis; screening in combination with a historical application effect and expert evaluation to obtain a preferred knowledge body; performing intelligent reconstruction on a scheduling rule, risk prevention experience and an operation and maintenance method to form an innovative knowledge base; and finally, intelligently matching with real-time engineering requirements to generate an intelligent water conservancy AI knowledge base. According to the application, by establishing a self-adaptive knowledge acquisition, organization and application mechanism, the knowledge base can autonomously update the knowledge content according to the environment change, the knowledge structure is optimized, and the intelligent operation management level of the water conservancy project is improved.

The invention provides a big data mining method and system applied to supply chain financial businesses, and the method comprises the steps: firstly obtaining real-time heterogeneous data streams of a plurality of participants of a target supply chain network, covering structured orders, unstructured logistics texts and semi-structured settlement document data, carrying out the multi-modal feature fusion, and carrying out the real-time heterogeneous data streams, including the structured orders, the unstructured logistics texts and the semi-structured settlement document data, of a plurality of participants of the target supply chain network; the method comprises the following steps: generating a supply chain feature matrix containing static and dynamic features of entity nodes, constructing a supply chain time sequence diagram network based on the supply chain feature matrix, representing participants by nodes, representing transaction events with timestamps and transaction strength weights by edges, hierarchically aggregating features by using a pre-trained time sequence diagram convolutional network, extracting global transaction modes and local abnormal fluctuation features, and constructing a supply chain network; and finally, generating a supply chain risk conduction topological graph which comprises a risk propagation path and an intensity parameter, and triggering a real-time risk early warning signal so as to realize accurate insight and timely early warning of the supply chain financial risk.

The invention relates to the technical field of transaction processing systems, and particularly discloses a trusted monitoring auditing system fusing TEE and a block chain, which comprises the following steps: generating a transaction instantaneous state abstract containing a key information field at a key node of a business process through a trusted execution environment, signing, anchoring a hash value of the transaction instantaneous state abstract to a block chain network, and storing the signature abstract under the chain; and during auditing, by comparing integrity verification of on-chain hash and under-chain abstract and TEE signature verification, dual authentication of transaction authenticity and environment reliability is realized. Through the collaborative architecture of the TEE and the block chain, the transaction core data state and the credible environment proof are deeply fused, so that the audit verification can be independently completed without depending on a centralized database, the problem that post-event tracing depends on the credibility of the log in the traditional audit is fundamentally avoided, and the audit verification efficiency is improved. And a lightweight credible verification basis is provided for a cross-mechanism cooperation scene.

A value chain network control tower system comprises a processor and memory configured to execute a know your model system that manages the complete lifecycle of Al models in enterprise environments. The know your model system performs model intake and registration actions including model documentation collection, registration procedures, metadata collection, input / output interface standardization, legal and licensing validation checks, and security validation. The system conducts comprehensive model evaluation and risk assessment actions by analyzing foundational properties, task performance, safety and risk management, alignment and compliance characteristics, operational metrics, and tooling transparency capabilities. The know your model system executes model deployment actions through automated environment validation, predeployment approval processes, and controlled production deployment with continuous monitoring.

The invention discloses an agricultural product quality abnormity propagation path prediction system and method based on a graph neural network. The method comprises the following steps: S1, collecting a quality data set in an agricultural product supply chain; s2, forming a feature vector used for image neural network input; s3, constructing an agricultural product supply chain quality anomaly propagation path prediction model based on the graph neural network; s4, inputting the preprocessed historical quality data set into the abnormal propagation path prediction model; s5, monitoring the quality state of each node in real time; s6, dynamically updating the weights of the edges in the graph based on an edge weight adaptive adjustment mechanism; s7, the abnormal propagation path prediction model recalculates the optimal propagation path with abnormal quality and generates a prediction path; s8, identifying potential risk nodes according to the re-calculated abnormal propagation path; and S9, the system updates the abnormal propagation path prediction model through real-time data. According to the invention, accurate prediction and real-time adjustment of the abnormal quality propagation path in the agricultural product supply chain network are realized.

The invention provides a privacy protection method and system based on big data security and privacy calculation, and relates to the technical field of data processing, and the method comprises the steps: obtaining original data feature extraction, obtaining data features, selecting a data desensitization algorithm to carry out data desensitization, carrying out encryption processing through a hybrid fully homomorphic encryption algorithm, and carrying out encryption processing through a hybrid fully homomorphic encryption algorithm. Homomorphic encrypted ciphertext data is generated and sent to the privacy protection computing platform; a data preprocessing protocol for privacy protection is constructed, data filtering and data dimension compression are carried out, multi-party security calculation is executed, a ciphertext calculation result is generated and returned to a data provider, verification is carried out, and if verification passes, decryption and filtering are carried out through a decryption algorithm to obtain a pure plaintext calculation analysis result; and constructing a reverse data desensitization model for reduction to obtain an analysis calculation result, constructing a dual-target optimization model for solving, obtaining an optimal balance point and generating an optimal decision result, desensitizing, uploading to a privacy protection block chain network, and performing distributed credible storage and sharing.

The invention relates to the technical field of supply chain management, in particular to a block chain-based culture product tracing method and system, and the method comprises the following steps: collecting the type, producing area and growth cycle data of culture products, distributing an identifier for each data item, coding the data and the associated identifiers in an upload format, and storing the data and the associated identifiers in a database; and uploading the data to a block chain network through an encryption channel, and generating a coded data record. According to the method, the timestamps and the data records are bound, each record is processed by using the hash function, and the security of each supply chain node is verified, so that not only is the integrity of data processing improved, but also an effective verification means is provided for consumers, and the security of the consumers is improved by decrypting and verifying the encrypted data. The security and privacy protection of data in storage and transmission are ensured, the production and circulation process of products is tracked through a block chain query function, the food security is improved, the supervision compliance is enhanced, and a powerful tool is provided for solving fraud and pollution problems in a supply chain.

The invention relates to the technical field of dispatching and emergency response, in particular to a supply chain multi-node real-time collaborative dispatching and emergency response system and method, and the system comprises a distributed data collection module which is used for obtaining the inventory data, logistics state and equipment operation parameters of each node in real time; the digital twin modeling engine is used for constructing a dynamic virtual mapping model of the supply chain network; the collaborative decision center generates a multi-objective optimization scheduling scheme based on a reinforcement learning algorithm; the emergency response trigger is used for automatically starting a graded emergency plan through abnormal mode recognition; according to the method, second-level response is realized through millisecond-level data synchronization and edge calculation, so that decision timeliness is improved, cross-node cooperation efficiency is improved by adopting multi-agent game and federated learning, and the punctuality rate of orders and the toughness index of the network are improved through multi-target Pareto optimization on the premise of controllable cost.

A system is provided which includes a plurality of users, each having a mobile Web3 node associated therewith, wherein each mobile Web3 node is equipped with a tangible, non-transient memory device having a set of software instructions recorded. The software instructions, when executed by at least one compute processor, perform the steps of establishing a hardware root of trust, establishing a biometric binding which is unique among the mobile Web3 nodes, and creating an AI with a user interface. The user interface receives input from a user and uses the input to generate privacy preserved zero-ID transactions, and to create a private chain network on the mobile Web3 node to establish and secure a P2P control channel for all communication with at least some of the plurality of users.

The invention discloses a virtual power plant system design method based on multilevel federated learning, and relates to the technical field of power system planning, and the design method comprises the steps: designing and constructing a virtual power plant system architecture comprising an energy resource module, a local controller, a regional coordinator, a central coordinator and a regional block chain network. Intelligent prediction and optimal control of distributed energy resources are realized by using a deep learning framework and a model training algorithm, the intelligent level of the system is improved, automatic management of energy transaction and execution of an intelligent contract are realized by using a block chain technology, transparency and security of the transaction are ensured, and multi-level federated learning is adopted, so that the efficiency of the system is improved. According to the invention, distributed cooperation of data distributed processing and model training is realized, risks of data leakage and privacy invasion are effectively avoided, cooperative work of a local controller and a regional coordinator is cooperated, intelligent management and optimal scheduling of distributed energy resources are realized, and manual intervention and management cost are reduced.

The invention provides a block chain-based clinical test data integrity system, and relates to the technical field of data processing, and the system comprises the following components: a multi-source heterogeneous data acquisition and preprocessing module, a multi-level encryption and under-chain data storage module, an alliance block chain network, and an alliance block chain network maintenance and verification shared distributed account book. The distributed account book only stores all digitally signed clinical test data fingerprints, metadata and operation logs, does not store original data, and is linked with the intelligent contract automatic management module, so that the clinical test data cannot be tampered in the whole life cycle from the source to archiving. Hash anchoring, digital signature and decentralized verification mechanisms of the block chain technically eradicate the possibility of unauthorized tampering of data, and ensure scientificity and reliability of clinical test results.

The embodiment of the invention provides a multi-factor security authentication method and device based on AI adaptive recognition and a block chain, and the method comprises the steps: collecting user biological features, behavior data and equipment information, and extracting static and dynamic features through employing a convolutional neural network and a long-short term memory network; a feature fusion network is innovatively constructed, an attention mechanism is introduced to carry out dynamic weight distribution and fusion on different features, and feature correction is carried out based on a user behavior baseline model. And meanwhile, a block chain network and an intelligent contract are deployed, so that credible storage and risk level evaluation of authentication data are realized. According to the method, the limitation of traditional single authentication is broken through, the effective balance between safety and user experience is realized, and an advanced technical scheme is provided for the field of identity authentication.

The invention discloses a supply chain resource optimization method and system based on atlas analysis, and the method comprises the following steps: constructing a supply chain atlas, and updating the supply chain atlas based on real-time supply chain data; key paths and low-efficiency paths in the supply chain atlas are identified through an atlas analysis algorithm, and bottleneck nodes are confirmed; generating an initial optimization strategy set based on the critical path and the bottleneck node; receiving a user interaction signal, determining an optimization operation type according to the user interaction signal, and dynamically correcting the initial optimization strategy set based on the optimization operation type; and executing the dynamically corrected initial optimization strategy set, and feeding back and updating the supply chain atlas based on an execution result. The method has the following advantages and effects: dynamic global analysis and real-time response of the supply chain network are realized, the resource allocation efficiency is improved in a complex supply chain environment, and the logistics interruption risk is reduced.

An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.

The invention discloses a supply chain intelligent tracking system oriented to process monitoring, relates to the technical field of supply chain intelligent management, and is used for solving the problems of flexibility and anti-risk capability of supply chain breakpoint management. Comprising a supply chain data processing module, an energy transmission analysis module, a breakpoint prediction module and a supply chain adjustment module. According to the method, original data are collected from supply chain nodes, effective features are generated in combination with a time sequence, classification features and a high-dimensional data processing technology, a breakpoint prediction model is constructed through a gradient lifting decision tree, breakpoint risks are identified and evaluated, a supply chain network topology structure is constructed, and propagation paths and influence ranges of the breakpoint risks are calculated recursively. Determining an affected node set, formulating an adjustment and recovery strategy in combination with a prediction result, optimizing resource allocation and logistics routes, quickly recovering key nodes, ensuring continuity of a supply chain, and upgrading an early warning mechanism to dynamically monitor risks, thereby improving management flexibility and anti-risk capability of the supply chain.

The invention relates to the technical field of Internet of Things gateways, in particular to an Internet of Things gateway and a gateway control method. The method comprises the following steps: acquiring initial state information of a gateway node of the Internet of Things to obtain gateway initial state data; connecting each gateway node with a block chain network according to the gateway initial state data, performing device identity verification on each gateway node and an access device through a block chain technology, and allocating initial tasks and resources of each gateway node by using a smart contract to generate initial resource allocation data; and performing local data processing on the gateway node through edge computing according to the initial resource allocation data to generate a local data processing result. By combining the block chain, the edge calculation and the smart contract, the expansibility, the reliability and the security of the system are improved, intelligent task scheduling and resource management are realized, and a complex scene of the Internet of Things is effectively dealt with.

A method for processing a query for data stored in a distributed database includes receiving, at an edge device, the query for data stored in the distributed database from a query device. The method includes causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database. The method includes detecting, by the edge device, that summary data has been stored on the dynamic ledger. The method includes generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger. The method includes transmitting, to the query device, the approximate response.

The invention relates to the field of elevator monitoring, and particularly discloses an elevator equipment monitoring method and system based on a block chain, a terminal and a medium. Elevator equipment parameters are collected through sensor equipment, and data processing is conducted on the elevator equipment parameters; the elevator equipment parameters after data processing are uploaded to a block chain network through the Internet of Things gateway, and the block chain network adopts an alliance chain architecture and a consensus mechanism; the intelligent contract is pre-configured and deployed to the block chain, so that the block chain detects elevator equipment parameters based on the intelligent contract, when the elevator equipment parameters are abnormal, a fault alarm is given out, the elevator maintenance state is detected based on the intelligent contract, when maintenance conditions are met, a maintenance task is generated and issued, and a maintenance result is received. Real-time transparent monitoring of elevator operation data, non-tampering storage of maintenance records and intelligent management of fault early warning are achieved, the safety and reliability of elevator operation are improved, and the maintenance efficiency and the transparency of data management are improved.

The invention relates to a network security protection method and system based on a block chain, and aims to solve a series of challenges of identity authentication single-point failure, insufficient static block chain fragmentation, threat detection limitation, encryption algorithm security problem and the like in the existing network security protection technology. According to the method, an on-chain digital identity is generated by fusing an equipment unique identifier and user biological characteristics, dynamic authority management is performed based on the identity, network traffic is detected in real time by using a deep learning model of adversarial training, a threat detection result is generated, an authority level is updated according to the detection result through an intelligent contract, and meanwhile, the user experience is improved. A fragmented block chain network technology is adopted, the number of sub-chains is dynamically adjusted according to a network load, cross-chain threat intelligence synchronization is realized through a relay chain, in addition, a threshold signature and zero-knowledge proof technology is also adopted, permission change records and cross-chain data are encrypted and verified, the security and privacy of the data are ensured, and the security and privacy of the data are improved. According to the invention, the efficiency and reliability of network security protection can be effectively improved, and a safer and more reliable network environment is provided for users.

The invention provides a data synchronization method and system between trusted DCS terminals, and relates to the technical field of data processing, and the method comprises the steps: a server receives a data synchronization request, comprehensively analyzes historical behavior data and equipment fingerprint features, generates a dual anomaly detection result, judges the rationality of timestamp data, and if the data is reasonable, sends the data synchronization request to a server; if yes, the server obtains a public key from a block chain network for encryption to obtain first verification information and multiple pieces of second verification information; performing sensitivity analysis, generating a sensitive data subset and a non-sensitive data subset, performing disturbance processing on the sensitive data subset, generating a disordered sensitive data subset, performing encryption, generating a data synchronization message, and sending the data synchronization message to a target terminal; performing decryption to obtain the first verification information and the second verification information, performing verification, de-duplication detection and shared decryption to obtain to-be-synchronized data, performing synchronization, generating a synchronization completion voucher, uploading the synchronization completion voucher to the block chain network, analyzing a data synchronization condition, generating a synchronization analysis report and sending the synchronization analysis report to the first terminal.

The invention discloses an intelligent city management information visualization comprehensive management method and system, particularly relates to the technical field of city management, and is used for solving the problem of misalignment of emergency response caused by false association and causal confusion in the prior art. Precise management is realized through multi-modal time sequence data feature alignment, causal chain network dynamic construction and a visual decision mapping technology, firstly, multi-dimensional event features of video monitoring, sensors and text work orders are extracted, a cross-modal association chain is constructed, interference chains are screened out through causal strength evaluation and anti-fact verification, and a visual decision mapping algorithm is established. According to the method, dynamic correction parameters are generated in combination with space-time weights and feedback priorities, finally, a visual guidance scheme is output by fusing a causal chain network and a geographic information system, decision optimization is driven through causal logic, and city event disposal accuracy and cross-department cooperation efficiency are remarkably improved.

A robot fleet management platform includes a job configuration system that determines tasks to be performed by robots of a robot fleet based on a job request and a first fleet objective. A proxy service applies fleet configuration services to the tasks to produce a data structure. An intelligence layer activates intelligence services to produce a robot task and associated contextual information that facilitates robot selection and task ordering. A job workflow system generates a workflow defining a performance order of the tasks. A workflow simulation system simulates performance of the job request based on the workflow to recursively redefine the tasks, the data structure, or the workflow until the simulation result satisfies a second fleet objective. In response to the simulation result satisfying the set of fleet objectives, a plan generator generates a job execution plan based on the set of robot tasks, the data structure, and the workflow.

The invention provides a carbon transaction intelligent management and control method and system based on a block chain, and relates to the technical field of carbon transactions, and node analysis and block chain network construction are performed by analyzing carbon transaction participation associated information; acquiring carbon emission monitoring network sensing data, encrypting the data, uploading the data to a distributed account book of a participating node on a chain, and performing consensus verification; if the verification result meets the preset verification standard, carrying out history backtracking, activating a transaction contract in combination with transaction constraints, and carrying out quota division and quota updating; a plurality of carbon transaction platforms are connected, when a transaction request is activated, transaction opponent matching and transaction rate evaluation sorting are performed based on a transaction contract, and a first matching result of the sorting is used as a current transaction opponent for recommendation, so that the problems of low transaction credibility, low transaction efficiency and poor real-time performance and safety during carbon transaction are solved, and the transaction efficiency is improved. The technical effects of improving the transparency and security of the carbon transaction and improving the transaction efficiency and the response speed while ensuring the data authenticity are achieved.

The invention provides an engineering supply chain risk conduction analysis system based on a graph neural network and a method thereof. The method comprises the following steps: acquiring supply chain operation data from multi-level supply chain nodes; constructing a graph structure model between supply chain nodes according to the operation data, and identifying and quantifying an association relationship between the nodes; simulating the risk event occurrence of the supply chain node on the graph structure model; based on a graph structure model, calculating the probability and influence value of risk propagation from an occurrence node to other nodes, and identifying a main risk propagation path; using a Motif anomaly detection module to detect an abnormal sub-graph mode appearing in a risk propagation process in the graph structure model; performing representation learning and association prediction on the graph structure model by using a self-supervised graph neural network, and mining potential hidden risk association; by constructing a multi-level supply chain association map and introducing risk disturbance simulation, the system can quantify the conduction rule of the risk in a supply chain network from a global perspective, and can automatically identify hidden high-risk links and key propagation paths.

The invention discloses a method for enabling building supply chain information tracing and management based on a block chain, and relates to the technical field of block chains, and the method comprises the following steps: S1, constructing an information chaining platform for building supply chain management, enabling each participant node of a supply chain to access a block chain network, storing the full life cycle data of the supply chain through employing a distributed account book technology, and storing the whole life cycle data of the supply chain; the full life cycle data comprises data of decision making, design, construction, acceptance and operation and maintenance stages. According to the method for tracing and managing the full-life-cycle information on the building supply chain, the data is stored by adopting a distributed account book technology, the integrity and non-tampering property of the data are ensured, the accuracy and efficiency of data tracing are improved, the consistency and reliability of the data are further ensured through a Hash algorithm and a consensus mechanism, and the data tracing efficiency is improved. A credible data basis is provided for each participant of the supply chain, and the supply chain flow rule is automatically executed by deploying the intelligent contract.