8 results about "Oracle machine" patented technology

A method may include: receiving, by a smart contract, a request for a random number and a randomness seed from a user; deploying the request as a transaction that is written on a first block in the decentralized ledger; generating, by an oracle, a plurality of pseudorandom quantum circuits using the randomness seed; providing the plurality of pseudorandom quantum circuits to a quantum randomness source, wherein the quantum randomness source executes the quantum circuits and returns a sequence of random bits to the oracle; writing the sequence of random bits to a new block in the decentralized ledger; hashing the sequence of random bits and a block hash for the new block; and returning the hash of the sequence of random bits and the block hash to the user computer program, wherein the user consumes hash of the sequence of random bits and the block hash.

This invention provides a method and system for matching transactions based on a blockchain oracle. The method includes: each buyer and seller sequentially uploading transaction information to the blockchain; updating a first incremental hash value in the smart contract according to the hash value of the transaction information; calling an off-chain computing system via the oracle to calculate a second incremental hash value based on the hash value of each transaction, the calculation method of the second incremental hash value being the same as that of the first incremental hash value; matching the transaction information according to preset matching rules to obtain a matching result to be verified; uploading the matching result to be verified and the second incremental hash value to the blockchain; and verifying the matching result to be verified using the first incremental hash value, the second incremental hash value, and the preset matching rules through the smart contract to determine the matching result. Implementing this invention improves the security of transaction information and increases the matching efficiency of transactions.

Embodiments of the present application provide a data processing method and device based on a line segment tree structure, equipment and medium, the method comprises: after the oracle contract receives the first transaction request sent by the business contract, the first request condition corresponding to the first condition hash value is obtained based on the first transaction request, and the condition hash value matched with the first condition hash value is found in the line segment tree structure of the on-chain storage space. If not found, the oracle contract is called to send the first off-chain query request for the first request condition to the off-chain oracle, so that the oracle returns the first off-chain business data to the oracle contract. The received first off-chain business data is taken as the first business data by calling the oracle contract, and the first business data and the first condition hash value are updated to the tree node associated with the first request condition in the on-chain storage space. By using the embodiments of the present application, the utilization rate of off-chain data can be improved, and the waste of storage resources can be avoided.

This information processing device comprises: a prediction unit that calculates an objective function by converting input information comprising observation information of a predetermined physical quantity and a control variable for the predetermined physical quantity into a latent variable of a low-dimensional latent space, identifying a state equation by sparse regression to which Regularization with Oracle is applied using the latent variable, and performing time-series prediction using the state equation; and a control unit that calculates optimal control by solving an optimization problem based on the objective function.

This invention discloses a procurement cost optimization method applied to manufacturing supply chain management, comprising the following steps: Purchaser nodes publish procurement demands containing dynamic green standard thresholds; supplier nodes register their green attribute proofs and dynamic performance capability data, cross-verified by third-party collaborative nodes and marked with confidence levels, generating a unique on-chain trusted dataset; based on procurement scale, industry green policy level, and market supply and demand fluctuation coefficients, adjust the objective weights and constraint priorities of a multi-objective optimization model, and input the unique trusted dataset into the adjusted multi-objective optimization model to solve for the Pareto optimal procurement decision scheme; compile the optimal procurement decision scheme into a layered programmable smart contract and deploy it to the supply chain blockchain network; obtain on-chain and off-chain performance trigger data in real time through oracles, driving the contract to automatically execute tiered payment, tiered incentive, or gradient penalty operations. This achieves automated performance and effectively reduces risk.

This application provides an operating system compatibility testing method and system, relating to the field of operating system compatibility testing technology. The method includes: acquiring the kernel event stream and behavior verification certificate generated by the compatibility test to be tested; executing system calls on an event-by-event basis in a formally verified security sandbox using an oracle pattern and comparing the return values, while simultaneously performing hash chain verification on the kernel event stream; selectively replaying a subset of events based on audit rules defined by the tester; and finally determining the detection conclusion based on the comparison and verification results. This application achieves the separation of the detection conclusion from the trust base of the replay execution environment, eliminating the trust dependency on the replay environment and improving the objectivity, provability, and efficiency of the detection conclusion.

This application discloses a data management method, apparatus, and system based on blockchain. In response to a data write request from a first client, encrypted business data is verified through consensus and then written to the chained database of a distributed ledger, initializing the smart contract state machine. In response to external events detected by an oracle node, proof data signed by a second client is obtained and broadcast to trigger a transaction. The smart contract automatically verifies the signature's legality and its matching degree with preset rules; upon matching, the state machine undergoes an atomic transition. In response to the state transition, a digital payment interface is called to atomically execute the deduction of the first account, the addition of the second account, and the writing of the payment record within the same distributed transaction. The block containing the state transition and payment results is added to the ledger after consensus is reached. This application solves the technical problems of data consistency, state atomicity, and distributed transactions in a distributed environment.

The application relates to the technical field of power system operation and production management, and particularly discloses a power maintenance scheduling system and method based on big data, which comprises a data analysis module, a blockchain collaboration module and a scheduling execution module. The method mainly comprises the following steps: generating predictive maintenance events and real-time abnormal alarm events through big data analysis; converting the events into smart contracts on the blockchain through an oracle; automatically detecting resource conflicts by using a conflict negotiation engine, and generating a scheduling adjustment scheme through preset rules based on the emergency level of emergency events and the adjustable threshold of planned events; automatically updating the contract state after the on-chain consensus of relevant parties; finally, driving the scheduling instruction to be issued and executed, and feeding back, and utilizing a closed-loop data optimization analysis model. The application realizes the automation, collaboration credibility and continuous optimization of the model of the scheduling process, and improves the response efficiency, collaborative transparency and decision accuracy of power maintenance.