6results about How to "Reduce system overhead" patented technology

The invention discloses an information processing method and system of a digital vehicle key and a vehicle. The method is applied to a vehicle side and comprises the steps that a target request is sent to a server side, the target request is used for requesting the server side to preset a certificate on the vehicle side, the certificate is used for generating a plurality of digital vehicle keys, and the effective states of different digital vehicle keys are synchronized with the effective state of the certificate; acquiring a certificate issued by the server in response to the target request; the certificate is stored, a first notification message is sent to the server side, the first notification message is used for indicating that the certificate is successfully preset on the vehicle side, the state of the certificate is set to be an allocatable state by the server side, and the allocatable state is used for indicating that the certificate is allowed to be allocated to an application program bound with the vehicle side; the application program is used for executing locking operation or unlocking operation on the vehicle end through the digital vehicle key corresponding to the certificate. According to the invention, the technical problem of low information processing efficiency of the digital vehicle key is solved.

The invention relates to the technical field of network security, in particular to an attack detection method and device, electronic equipment and a storage medium, which are used for improving the attack detection accuracy. The method comprises the following steps: sampling feature information of data packets in a network according to a preset time period, obtaining a feature information set sampled in each time period, and obtaining sub-traffic data of the data packets using each network protocol in one feature information set in the time period corresponding to the feature information set, obtaining a flow difference degree corresponding to the feature information set based on the difference between the reference flow data of each network protocol and the corresponding comprehensive flow data according to the comprehensive flow data of each network protocol and the difference between the reference flow data of each network protocol and the corresponding comprehensive flow data; and obtaining an attack detection result based on the flow difference degree corresponding to each feature information set. According to the method, the comprehensive flow data and the reference flow data under the same network protocol are compared, so that the attack detection accuracy can be effectively improved.

The present disclosure relates to a bandwidth part configuration method, a bandwidth part configuration apparatus, and a storage medium. The bandwidth part (BWP) configuration method is applied to a terminal, and the method comprises: determining a first initial BWP pair and a second initial BWP pair, wherein the first initial BWP pair comprises a first initial up link BWP and a first initial down link BWP, the second initial BWP pair comprises a second initial up link BWP and a second initial down link BWP, and the bandwidths of the initial up link BWP and the initial down link BWP of the first initial BWP pair are less than or equal to a first threshold value. By means of the present disclosure, the flexibility of BWP configurations can be ensured, and system overheads can be saved on.

This application discloses a data access method and apparatus, and a first computing device, and belongs to the field of computer technologies. The data access method includes: The first computing device receives a second write operation command sent by a second computing device, where the second write operation command is obtained after the second computing device preprocesses a first write operation command generated by a host, and the first write operation command is used to write to-be-written data into a storage pool; the first computing device performs a data processing operation on the to-be-written data according to the second write operation command; and the first computing device writes processed to-be-written data into the storage pool. This application reduces system overheads of the host.

The present application relates to the field of network security technology, and in particular to an attack detection method, device, electronic device and storage medium for improving the accuracy of attack detection. The method includes: sampling the characteristic information of data packets in the network according to a preset time period, obtaining a characteristic information set sampled in each time period, using the sub-flow data of data packets of each network protocol in a characteristic information set, obtaining the comprehensive flow data of each network protocol in the time period corresponding to a characteristic information set, based on the difference between the reference flow data of each network protocol and the corresponding comprehensive flow data, obtaining the flow difference corresponding to a characteristic information set; based on the flow difference corresponding to each characteristic information set, obtaining the attack detection result. The present application can effectively improve the attack detection accuracy by comparing the comprehensive flow data and reference flow data under the same network protocol.

The application discloses a kind of federated meta-learning image classification methods based on sparse neural network, communication efficiency is high, algorithm performance is good.The method of the present application mainly includes the following steps: (10) set up fully connected neural network node and correlation coefficient, establish source node set and target node set;(20) the full connection neural network is sparsified, and the weight parameters of sparse neural network are initialized;(30) training internal update is carried out on the training set data of each source node;External update is carried out on the test set data of each source node;(40) remove the minimum positive number and the maximum negative number of each layer of weight matrix after external update of each source node, whether iteration number t is the integer multiple of the set local iteration number is judged to make corresponding processing;(50) gradient update training is carried out on the training set data in each target node based on the parameters obtained by training source node set, and image classification task is carried out using the fine-tuned model of target node.