5results about How to "Reasonable forecast" patented technology

The invention relates to a vehicle track prediction method based on interactive perception and map adaptation, and belongs to the technical field of automatic driving. The method comprises the steps of collecting a historical track and geographical semantic information of a vehicle; the method comprises the following steps of: modeling interaction between dynamic objects by utilizing Transform, and fusing map information when a state sequence is decoded; a hierarchical attention mechanism is adopted, the future state of the vehicle is considered, and the consistency and accuracy of prediction are ensured; in addition, a multi-resolution map coding module is also constructed, the module fully utilizes a Vision Transform module to efficiently capture local and global scene context information, and then map representation is closely associated with a vehicle state to accurately model an interaction process of a vehicle and a scene, so that a more reasonable future trajectory prediction result is generated. The method shows wide application potential, can effectively improve the calculation performance, and is suitable for a more complex traffic environment.

The application discloses a sewage treatment water quality monitoring method based on target domain prior fusion source domain knowledge migration Gaussian process regression, and belongs to the technical field of sewage treatment. In view of the problem that rain and storm samples are scarce and have large distribution difference with sunny day data, the application takes sunny day data as a source domain, extreme weather data as a target domain, constructs a joint Gaussian distribution of source domain and target domain output, obtains a target domain prior distribution fusing source domain information through conditional derivation, takes the prior distribution as a Gaussian process regression prior, and forms a migration Gaussian process regression model. A small amount of target domain samples are used to optimize model hyperparameters, and for new samples, water quality index prediction values and uncertainty intervals are output. The application can realize knowledge migration from sunny days to extreme weather while keeping high precision and interpretability of the Gaussian process regression, effectively cope with the sample scarcity and distribution difference problem, and improve rain and storm day effluent water quality prediction accuracy.

This invention relates to the field of meteorological forecasting technology, specifically a meteorological forecasting adaptive optimization method and system based on multi-algorithm fusion. This method constructs a parameter optimization framework based on multi-algorithm fusion, embedding physical constraints, and possessing adaptive capabilities, aiming to systematically solve key problems in the parameter tuning process of traditional meteorological forecasting systems. This framework achieves efficient and intelligent optimization through a series of tightly linked steps, ensuring the effectiveness and stability of the method in operational scenarios. This process integrates preceding modules into a unified platform through a microservice architecture, automating data and control flows. The evaluation system includes historical backtesting, real-time forecast testing, and extreme weather-specific testing, quantifying forecast accuracy improvement, resource consumption optimization, and cross-scenario generalization capabilities. Results are fed back to a knowledge base to form a self-optimization loop, ultimately verifying the comprehensive advantages of this method in improving forecast efficiency, ensuring physical rationality, and operational applicability.

The invention provides a cobalt-nickel separation coefficient prediction method and system based on structural knowledge embedding, and belongs to the field of cobalt-nickel resource recovery. The method comprises the following steps: constructing a cobalt-nickel separation coefficient original data set comprising leachate composition parameters, extractant structure information and operation condition parameters; performing data preprocessing and feature engineering processing on the original data set to obtain a feature data set meeting machine learning modeling requirements; respectively constructing a reference prediction model and a knowledge embedding prediction model in which domain knowledge features are introduced on the feature data set, performing model performance comparison, and determining a candidate prediction model; performing hyper-parameter optimization on the candidate prediction model based on a comprehensive evaluation loss function considering prediction precision and over-fitting suppression to obtain a target prediction model; and stable prediction of the cobalt-nickel separation coefficient under the unknown working condition is realized by using the target prediction model. The method can adapt to data distribution difference and stably predict the cobalt-nickel separation coefficient.

The invention belongs to the technical field of load prediction, and particularly discloses a heat exchange station load prediction analysis system based on deep learning. Comprising an indoor temperature calling module, a temperature setting information extraction module, a synchronous indoor temperature extraction module, a set temperature tendency association module, a set time tendency association module, a heating load information calling module, a heating temperature difference tendency association module and a heating load prediction module. According to the method, the indoor temperature data of the user is acquired, the set time is extracted, the preference set time of the user is identified, and the indoor temperature data of the preference set time is further acquired, so that the optimization of the temperature object and the selection range in the meteorological factors influencing the load prediction is realized, and the load prediction on the basis is more reasonable and accurate; and meanwhile, the problems that a large amount of meteorological data occupies storage space and the prediction efficiency is reduced are avoided.