7results about How to "Speed ​​up the calculation process" patented technology

The present disclosure provides a sampling circuit, a control method and a hardware accelerator applied to a lattice cryptography algorithm, and relates to the technical field of post-quantum cryptography. The sampling circuit comprises a padding module configured to pad a seed according to a hash padding rule; a hash operation module configured to perform a hash operation on the padded seed to generate a pseudo-random sequence; a cache module configured to store the pseudo-random sequence generated by the hash operation module; and a sampling module configured to sample the pseudo-random sequence stored in the cache module to obtain a sampling result. The present disclosure provides the cache module, and in the sampling process, the cache module provides data for sampling to the sampling module without relying on the hash operation module to provide data. Therefore, the sampling module and the hash operation module in the present disclosure are independent of each other and can work simultaneously, thereby improving the operation efficiency of the sampling circuit and effectively accelerating the calculation process of the lattice cryptography algorithm.

This invention discloses a network-based data weaving integrated machine system architecture, belonging to the field of data management and integration technology. It includes a hierarchically deployed basic node layer, composed of multiple physical or virtual device nodes, each node comprising a computing unit, storage unit, caching unit, network communication unit, and query execution engine; a node control layer, configured to uniformly manage all nodes, including a node registration module, a node discovery module, a task scheduling module, and a routing control module; a data weaving capability layer, configured to provide data management functions, including an active metadata management module, a federated query module, a semantic layer module, and an access control module; a network collaboration layer, configured to realize multi-node collaboration, including a cross-node query collaboration module, a metadata synchronization module, and an access control mapping module; and an intelligent collaboration layer, configured to provide intelligent computing interfaces. This invention employs the above system architecture to construct a scalable, networkable, and collaborative network-based data infrastructure.

The invention belongs to the technical field of nuclear power alarm monitoring, and particularly relates to a nuclear power alarm monitoring method and device based on event subscription. Basic data related to alarm monitoring is read from a DCS system through a one-way gatekeeper, a local database is established, an alarm measuring point and an alarm event are configured in a local system, and configuration files of the alarm measuring point and the alarm event are downloaded to an alarm service; the alarm events are divided into multi-measuring-point working condition events and single-measuring-point events; duplicate removal is carried out on the single-measuring-point events requested by subscription of different clients, and unified subscription is carried out on a single-measuring-point event set after duplicate removal; and the alarm service of the multi-measuring-point working condition event performs alarm calculation according to a preset alarm algorithm model, triggers an alarm event when an alarm calculation result meets an alarm condition, and actively pushes the corresponding alarm event to a corresponding client according to an alarm subscription table. According to the invention, the technical problem that the nuclear power alarm monitoring function is limited in the prior art is solved, and the health management of the DCS is enhanced.

The application provides a privacy protection federated learning method based on a blockchain anti-poisoning attack, and belongs to the technical field of federated learning, privacy protection and blockchain technology. The technical problems of poisoning attack and malicious aggregation in the privacy protection federated learning are solved. The technical scheme comprises the following steps: S10, system global initialization; S20, local model training; S30, malicious participant identification; MO and SM cooperate to determine whether the participant has performed the normalization operation; S40, gradient ciphertext aggregation; S50, participant and MO update model. The application has the beneficial effects that the application protects the privacy of the participant model gradient by using the fully homomorphic encryption algorithm CKKS, identifies the malicious gradient in the gradient ciphertext by using the cosine similarity method, and calculates and verifies the aggregation result by using the consensus mechanism of the blockchain.

The application provides an energy efficiency service deployment and delivery method and system for an edge computing network, and relates to the technical field of edge computing.The method comprises the following steps: extracting multi-dimensional service features, encoding and fusing the extracted multi-dimensional service features to generate a global context feature representation; combining system load dynamic prediction results to adaptively adjust an upper-layer decision time scale based on the global context feature representation; executing service deployment decisions and base station sleep / activation switching decisions under the adjusted upper-layer time scale; executing service delivery decisions and resource allocation decisions based on real-time service requests under a fine-grained lower-layer time scale; and coordinating the decision-making process through a double-time-scale hierarchical learning framework.The method can effectively reduce long-term network overhead, effectively respond to dynamic service requests, improve the processing capacity for time coupling relationships between deployment and delivery cycles, and reduce system costs.

The application provides a switch frequency determination method and device for an LCC resonant converter, the method comprising: obtaining an input voltage and an output voltage of the LCC resonant converter; determining a starting mode in which the LCC resonant converter is currently located by using the output voltage; determining a calculation program corresponding to the starting mode by using the starting mode in which the LCC resonant converter is currently located, wherein the calculation program is an operation logic program designed for the starting mode by using a field programmable logic gate array; inputting the input voltage and the output voltage into the field programmable logic gate array, and calculating the switch frequency of the LCC resonant converter by using the calculation program, so that the switch frequency determination method improves the adjustment rate of the switch frequency of the LCC resonant converter, the rising speed of the output voltage, and further improves the dynamic response speed of an existing power supply.

The invention relates to a construction method of a valve flow conductance model and an application method of the valve flow conductance model.The construction method of the valve flow conductance model comprises the steps that multi-dimensional valve data are obtained, the multi-dimensional valve data are processed through feature engineering to obtain target features, the multi-dimensional valve data comprise the air pressure and the valve opening degree, and the target features comprise the air pressure and the valve opening degree; the target characteristics comprise air pressure and air pressure standard deviation in a preset time period; obtaining a preset mapping relation among an air pressure range, an air pressure standard deviation range and working conditions, and determining the working condition corresponding to the target feature according to the mapping relation, the air pressure and the air pressure standard deviation; and training a gradient boosting decision tree algorithm by taking the target feature data under each working condition as input data and taking the valve predicted opening degree as output data to obtain a valve conductance model of the corresponding working condition, and the valve conductance models are in one-to-one correspondence with the working conditions.