7results 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 utility model belongs to the field of computing cluster deployment technology, and discloses a computing cluster system, including at least one row of computing cluster structures. Each computing cluster structure includes at least one computing cluster component, which comprises a first and a second cabinet arranged back-to-back, and an assembly mezzanine component disposed between them. The assembly mezzanine component includes a fixed frame and high-speed cable connection groups, power distribution cable connection groups, and liquid cooling pipe connection groups disposed within the fixed frame. The high-speed cable connection groups connect to the first and second cabinets for interconnection between the two cabinets; the power distribution cable connection groups connect to the first and second cabinets for power supply; and the liquid cooling pipe connection groups connect to the first and second cabinets for coolant supply. This computing cluster system can achieve a reasonable layout to improve space utilization, manage cables, shorten interconnection cable lengths, and optimize maintenance efficiency.

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 provides a satellite-ground cooperative task reasoning method and system, which comprises the following steps: obtaining state information of a satellite and a terminal device, and executing a model deployment, task scheduling and resource allocation joint algorithm according to the state information to generate a reasoning model and a calculation resource allocation of the satellite, and to generate a model segmentation point of the terminal device, a target satellite and an offloading path; informing the target satellite of a deployment decision of the reasoning model and a calculation resource allocation scheme, so that the target satellite deploys the reasoning model according to the model deployment decision; informing the terminal device of the reasoning model, the model segmentation point, the number of the target satellite and the offloading path; sequentially generating intermediate characteristic values of a reasoning task from an initial layer of the reasoning model to a layer number of the model segmentation point; sending the intermediate characteristic values to the target satellite through the offloading path, continuing to calculate the intermediate characteristic values, generating a reasoning result, and returning the reasoning result to the terminal device. The time delay problem and the energy consumption limitation problem in cooperative reasoning are solved.

This application discloses a method and apparatus for data collection in a node used in wireless communication. A first node receives a first information block indicating a first RS resource set for data collection; executes a process for the data collection; performs measurements included in the first RS resource set as part of the process for the data collection, the data collection being associated with an inference configuration; the amount of inference resources occupied by the process for the data collection depends on at least one of the data types included in the data collection or the inference configuration associated with the data collection. This application solves the resource consumption problem of UE performing data collection, facilitates AI deployment, and improves overall system performance.

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.