6results about How to "Meet the complexity" patented technology

The present application proposes an implementation method for building a heterogeneous data warehouse, comprising the following steps: step 1: identifying the types of heterogeneous data sources to obtain data asset import dimension descriptions and measurement system descriptions through data model mapping; step 2: based on the set heterogeneous data integration strategy library, according to the data asset import dimension descriptions and measurement system descriptions, converting the data business transaction dynamic process into a dynamic fact table; step 3: based on the constructed heterogeneous data ETL template, generating parallel incremental ETL tasks according to the dynamic fact table; step 4: according to the parallel incremental ETL tasks, accessing the heterogeneous data sources through the constructed heterogeneous data access interface to obtain the heterogeneous data assets to be processed for processing, so as to build a heterogeneous data warehouse.

The utility model relates to a luminous shoelace and a shoe with the luminous shoelace, the luminous shoelace comprises a strip-shaped piece coupled with a shoelace body and a luminous device arranged between the shoelace body and the strip-shaped piece, and the luminous device comprises a circuit board module and a battery. The plurality of predetermined colored light emitting diodes are electrically coupled with the circuit board module and the battery and comprise a first predetermined colored light emitting diode and a second predetermined colored light emitting diode; a sensor detects an external force to generate a control signal; the control chip is arranged on the circuit board module, receives a control signal and drives the light-emitting diodes with the preset colored light to emit light in a light-emitting mode, and the circuit board module is a flexible circuit board or a hard circuit board with a flexible flat cable; therefore, the flexible circuit board or the circuit board with the flexible flat cable is combined with the high polymer material, so that the lightweight design is realized, the overall volume is greatly reduced, the diversified requirements of different age groups and foot types on the size of the shoelace are met, and diversified light-emitting modes and practicability are provided.

The invention discloses a driver behavior analysis safety early warning system and method based on deep learning, and relates to the technical field of driver behavior analysis. The method comprises the steps of collecting driving data of a driver, and preprocessing to obtain original data; after feature extraction is carried out by using a deep learning algorithm, model training is respectively carried out; analyzing the physiological and psychological states of the driver and obtaining a physiological value and a psychological value; the driving skill level of the driver is obtained, and an operation danger value of the driver is obtained by combining operation feature analysis; acquiring reflection conditions of crowds around a driver, and analyzing by combining environment characteristics to obtain an environment danger value; performing single judgment on the data trained by the model, and giving an alarm if the driver behavior is dangerous; and if the driver behavior is not dangerous, carrying out integration judgment on all data passing model training, and if the driver behavior is dangerous, giving an alarm. According to the invention, the accuracy of driver behavior analysis safety early warning based on deep learning is improved.

The application provides a database intelligent index recommendation method and system based on reinforcement learning, which comprises the following modules: (1) an initialization module; (2) an action space definition module; (3) a reward function optimization module, which selects appropriate index operations to minimize the reward function value; (4) a model training module; and (5) a deployment and optimization module. The application introduces a reward function calculation method that comprehensively considers multiple key factors such as the optimization effect and size of the index, and the reward function can more comprehensively evaluate the good and bad of an action (index operation). Compared with a single index, this multi-factor comprehensive consideration method is more in line with the complexity of the actual database performance, and is helpful to improve the accuracy and applicability of the index recommendation algorithm. The introduction of the evaluation of the index size avoids excessive consumption of system resources such as memory and disk space. Considering the resource consumption factors such as the index size in the reward function helps to avoid performance problems caused by excessively large or small index configurations, and improves the robustness and practicality of the system.

The invention discloses an agricultural biped robot and a pose control algorithm. The pose control algorithm comprises the following steps that basic parameters of a model prediction control algorithm are initialized; establishing a speed model, an angular speed model, an angle change model and a position change model; establishing a spatial state discrete model; building a model predictive control algorithm, wherein the model predictive control algorithm comprises a space state equation, a cost function and constraints; and performing attitude planning through the model prediction control algorithm, wherein the attitude planning algorithm comprises degree of freedom, standing, squatting, stepping, walking attitude, pause, translation and steering. According to the technical scheme, by optimizing the motion model, the spatial state discrete model, attitude planning and other control algorithms, the control efficiency of the agricultural biped robot is improved, the robot is prevented from sensing confusion and adapting to the complexity of the agricultural production environment, and the motion control capacity is improved.

The invention discloses a data processing method and device, electronic equipment and a computer readable medium, and relates to the technical field of big data processing.The method comprises the steps that received target parameter configuration is operated, and performance index data is obtained; in response to the condition that the iteration end condition is not met, updating the knowledge base according to the performance index data and obtaining updated configuration parameters; calling an interpretive framework to identify importance values of the updated configuration parameters to determine to-be-adjusted parameters and to-be-merged parameters, and merging the to-be-merged parameters to obtain merged parameters; and determining parameter types and discrete step sizes of the to-be-adjusted parameters and the merging parameters, carrying out tuning test on the to-be-adjusted parameters and the merging parameters in the database based on an iteration end condition to determine tuning parameter values of the to-be-adjusted parameters and the merging parameters, and outputting the tuning parameter values. The optimization efficiency and accuracy of database parameter configuration are improved, and the parameter configuration of the database meets the scale of the database and the complexity of an application program.