4results about How to "Reasonable deployment" patented technology

The present application belongs to the technical field of photovoltaic equipment monitoring fault early warning, and relates to a photovoltaic equipment cluster benchmarking analysis and implicit fault early warning method and system. The system collects basic parameters and implicit associated data of photovoltaic equipment through a data acquisition module to ensure data comprehensiveness and timeliness. Through a data fusion module, the collected data is processed and fused to generate a standardized data sequence. Through a cluster benchmarking module, the photovoltaic equipment is divided into different clusters, a group health benchmark is constructed, and the model accuracy is improved through individual difference calibration. Through a dynamic early warning module, early warning information is generated, and a basis is provided for subsequent fault troubleshooting. Through an intelligent operation and maintenance module, early warning is prioritized and an operation and maintenance scheme is generated, which is pushed to operation and maintenance personnel for optimization guidance. The present application can realize accurate benchmarking analysis and implicit fault early warning of photovoltaic equipment clusters, and reduce the failure rate.

The present application relates to the technical field of smart grid, disclose a kind of multi-factor associated return home degree winter power load pre-control method and system, method includes: the historical power consumption data obtained in advance is transformed to obtain power load data, and power load data is identified to obtain power consumption problem;The influencing factor is obtained by analyzing power consumption problem, and the strong correlation factor data matrix is obtained by analyzing influencing factor, and the correlation influence coefficient is obtained by analyzing strong correlation factor data matrix;The trained power load prediction model is obtained by iterative training to power load prediction model, and the current power consumption data obtained in advance is input into trained power load prediction model to obtain load prediction data;The red yellow green classification area is obtained by dividing power grid;Power load pre-control scheme is formulated, so as to carry out power consumption prevention and control management.The present application can solve the problem that power grid overload is difficult to prevent and control during return home degree winter and power consumption prediction is not accurate enough.

This invention discloses an intelligent method and apparatus for calculating refined oil demand. The method includes: acquiring collected seasonal and historical demand information for refined oil; performing cluster analysis on the collected information based on a clustering algorithm to obtain data information at different regional category levels; performing feature engineering processing on the data information at each regional category level, and determining the feature importance value of each feature among the seasonal characteristics, trend characteristics, and cross-features of refined oil at the corresponding regional category level based on a decision tree model ensemble learning algorithm; determining the target features for different regional category levels and establishing a second training dataset; training a neural network model to obtain a refined oil demand analysis model; and calculating the refined oil demand data of different merchants at different regional category levels. This invention aims to improve the accuracy and efficiency of refined oil demand calculation.

The application discloses a kind of wisdom park collaborative service systems based on cloud computing and big data, it is related to wisdom park technical field. Including data security and privacy protection module, it is characterized by: data acquisition module: for real-time acquisition wisdom park multi-source data;Data processing module: for building data fusion and collaborative model, the feature extraction and fusion of wisdom park multi-source data are carried out, then based on big data, collaborative analysis is carried out, and collaborative decision suggestion is generated, and data intercommunication sharing is realized;Wisdom service and optimization module: for building wisdom service model, based on service request, automatically generate service, and real-time track and feedback service progress, optimize and update model, improve service response efficiency.The application builds wisdom service model, real-time analysis multi-source service request, automatically generate service, and optimize and update model, continuously improve service response speed and user service experience, and reduce user loss risk.