35results about How to "Localized" patented technology

The present invention provides a multi-level caching method based on historical de-escalation frequency. The present invention is based on historical hidden information of data blocks, and historical hidden information is one of the essence of multi-level cache system. Through hidden frequency, it can effectively The hot data block can be identified accurately, and it can be stored in a higher-level cache for a longer period of time, which increases the data block hit rate of the system and reduces the average response time; the present invention divides the traditional LRU stack into two dedicated The queue makes the hidden information local, avoids the mixing of hot and cold data blocks, and reduces the use of bandwidth between caches at all levels; the invention effectively identifies hot data blocks, and the algorithm makes hot data blocks in the high-level cache It can be stored for a long time, reducing the downgrade and upgrade operations between caches at all levels, and further reducing the bandwidth consumption between caches at all levels; the space consumption of the present invention is very small, which is better for the system under various loads. Read and write performance laid the foundation.

The invention relates to a wireless service traffic prediction method based on weighted federated learning. The method comprises: a control center pushes multiple pre-trained models to the base station side; the base station side performs model training according to local data, and transmits the trained models to Control center; the control center fuses the models according to the weighting rules and feeds them back to the base station. The weighting rules give more weight to the local model; the base station predicts the wireless service traffic in the future according to the obtained final model. The wireless service flow prediction method provided by the present invention uses weighted aggregation rules to replace the average strategy in the model aggregation strategy of the control center, which can avoid inaccurate predictions caused by data heterogeneity and improve distributed wireless service flow prediction. overall prediction accuracy.

The invention relates to an anti-electromagnetic homemade reinforced computer based on Loongson 3A, which belongs to the field of self-controllable vehicle control equipment for military localization. The reinforced computer of the present invention includes a Loongson 3A main control module, a power supply module, an SOC auxiliary module, a functional module, a filter interface module and a chassis; the Loongson 3A main control module can run a domestic operating system and provide various interfaces; the power supply module realizes power supply conversion, Supply power to Loongson 3A main control module, SOC auxiliary module, function module, and filter interface module; the function module realizes the interface expansion of Loongson 3A main control module; the SOC auxiliary module assists Loongson 3A main control module to realize high-speed digital signal and video signal processing; The filter interface module includes a signal filter module composed of a filter circuit and an external interface module composed of an electrical connector, which completes the filter processing of different input signals according to the characteristics of the input signal, and realizes the input signal filtering and signal output of the reinforcement machine.

The invention discloses an incidence relation excavation method for a text-oriented knowledge unit. The method comprises the following steps of: (1) carrying out aggregation for a text set, finding a text subset with a similar theme, on the base, utilizing nonsymmetry of term distribution in the text and excavating a linear incidence relation between texts; (2) utilizing locality of the incidence relation of a knowledge unit pair, and generating a candidate knowledge unit pair; (3) based on characteristics of term word frequency, distance and semantic types of the knowledge unit pair, carrying out a bi-level classification for the candidate knowledge unit pair, and distinguishing the incidence relation of the knowledge unit pair. In the incidence relation excavation method, numbers of candidate knowledge units can be greatly reduced, and time complexity of relation excavation can be effectively reduced on the premise that accuracy is ensured.

The invention discloses an image retrieval method based on a weight color-sift characteristic dictionary. The image retrieval method based on the weight color-sift characteristic dictionary comprises the following steps that training images are selected in images to be retrieved randomly, the edges of the training images are extracted, the color-sift characteristics of edge points of all the training images are extracted, and a characteristic dictionary is constructed according to the color-sift characteristics, an image which needs to be retrieved is input and the color-sift characteristics of the retrieval image and the edge points of the image to be retrieved are extracted, and weight histogram characteristics of the retrieval image and the image to be retrieved are extracted based on the characteristic dictionary; similarity matching based on the weight histogram characteristics is conducted on the retrieval image and the images to be retrieved in the database based on the weight histogram characteristics; whether all the images to be retrieved in the database are all traversed is detected,, the result is matched according to similarity and the image searching result is displayed if all the images to be retrieved in the database are all traversed, and the similarity matching is conducted again if all the images to be retrieved in the database are not traversed. The image retrieval method based on the weight color-sift characteristic dictionary improves accuracy and callback rate during searching of a large-scaled image database, has dimension invariance, translation invariance and rotation invariance, and has the advantages that the image retrieval method based on the color-sift characteristic dictionary has locality, particularity, multi-amount property and high-efficiency property.