5results about How to "Small time complexity" patented technology

The present invention proposes a subcircuit matching method for analog integrated circuits, including: reading a circuit netlist file, defining node labels for component nodes in the circuit to distinguish; topologically analyzing the components respectively, generating a directed circuit topology diagram; sorting the node search order of the subcircuit, and dynamically selecting several nodes of the analog integrated circuit accordingly, thereby forming multiple node pairs and organizing them into a search tree, wherein a tree node of the search tree represents a subgraph state; adding the node pair to the tree node to form a new subgraph state, and when adding node pairs composed of nodes of the same subcircuit and nodes of different analog integrated circuits, the search tree forms different branches; pruning invalid branches in the search tree, thereby quickly matching the subcircuit and the nodes of the analog integrated circuit. The present invention has the effect of being able to achieve circuit matching with high accuracy and low computational complexity.

The present invention discloses a method for predicting the remaining life of a rolling bearing, comprising the following steps: S1, constructing fuzzy entropy as a rolling bearing performance degradation feature for the original vibration acceleration data of the rolling bearing in the source domain and the target domain; S2, inputting the training fuzzy entropy sample sets of multiple different source domains and the training fuzzy entropy sample sets of the target domain into the AAET-Crossformer model for training the parameters of the AAET-Crossformer model at the same time; S3, using the trained AAET-Crossformer model to hierarchically decode and predict the performance degradation characteristic trend of the rolling bearing in the target domain in the future; S4, establishing a three-parameter Weibull distribution reliability model to predict the failure threshold time point and RUL of the rolling bearing in the target domain. The prediction method of the present invention can achieve higher RUL prediction accuracy, better generalization performance and higher computational efficiency.

A load forecasting method using an attention mechanism to fuse spatiotemporal features, wherein an electric load data set is obtained and divided into a training set and a test set; weather data is collected and sorted by importance using a random forest algorithm, and the top three significant features of the weather data are selected as model inputs; VMD is applied to decompose the electric load data into multiple local components, each component corresponds to a vibration mode within a frequency range, and the parameters in VMD are iteratively optimized using a RIME algorithm to obtain a subsequence component with the least complexity; an energy consumption forecasting model using an attention mechanism to fuse spatiotemporal features is constructed based on the decomposed subsequence components and the top three selected meteorological features as model inputs, and a BiTCN model and a BiGRU model are used in parallel to simultaneously process time series data, and feature enhancement is performed after passing through a cross-attention layer to obtain a forecasting result.

This disclosure provides a data storage method and apparatus. The method includes: acquiring target data to be stored; reading a bitmap structure from a database based on a target identifier in the target data; and determining the mode of the bitmap structure. When the bitmap structure is determined to be in a variable-length mode, writing a target time from the target data into a newly added time unit in the data portion of the bitmap structure, wherein the data portion of the variable-length bitmap structure contains at least one time unit. When the bitmap structure is determined to be in a fixed-length mode, setting corresponding data bits in the data portion of the bitmap structure based on the target time in the target data, wherein the storage location of each data bit in the data portion of the fixed-length bitmap structure corresponds to a time point. This method can reduce the space occupied by data storage, thereby reducing storage costs, and improve the efficiency of data retrieval, thereby reducing query latency.

The invention discloses an intelligent electric energy meter power consumption behavior analysis early warning method and device, and the method comprises the steps: generating a daily power consumption sequence of a user in the first seven days at a time interval of 0.5 h, and further generating a power consumption summary sequence in the first seven days; generating a comprehensive electricity consumption valley interval and a comprehensive electricity consumption peak interval according to the electricity consumption summary sequence of the first seven days, and calculating the average electricity consumption in the comprehensive electricity consumption valley interval and the average electricity consumption in the comprehensive electricity consumption peak interval; generating a real-time electricity consumption sequence of the ith day of the user by taking 0.5 hour as a time interval; delimiting a real-time electricity consumption trough interval and a real-time electricity consumption peak interval for a real-time electricity consumption sequence according to the comprehensive electricity consumption trough interval and the comprehensive electricity consumption peak interval; and according to the delimited real-time electricity consumption valley interval and the delimited real-time electricity consumption peak interval, judging an electricity consumption abnormal condition, and sending early warning information to the intelligent terminal of the user. According to the method, the electricity consumption behavior is subjected to statistical analysis, electricity consumption abnormity early warning is achieved, and the space-time complexity is small.