9 results about "Regression modelling" patented technology

The invention discloses a power grid transformer technical renovation and overhaul time domain interval evaluation method. The method comprises the steps of 1, establishing an evaluation basic data set through multi-source data acquisition and data preprocessing; 2, screening key influence variables, quantifying variable influence weights by adopting regression modeling, and realizing accurate quantification of the health state of the transformer in combination with a TOPSIS algorithm; 3, forming a dynamic fault rate model; 4, constructing a system dynamics-Monte Carlo cooperation model, a fault tree-Bayesian network dynamic and static diagnosis model and a long and short term memory network time sequence prediction model; 5, setting an optimization target and a constraint condition, and outputting an optimal time point and a reasonable time domain interval of technical renovation and overhaul of the transformer by adopting a multi-target optimization solution method; and 6, model evaluation precision is tested back and verified through historical data, and key parameters of the model are subjected to feedback adjustment by adopting a particle swarm optimization algorithm. The method effectively solves the problems that a traditional method is low in evaluation precision, single in target and poor in dynamic adaptability, and is convenient to popularize and use.

The invention discloses a regional resident load meteorological sensitivity analysis method, device and equipment based on fused meteorological indexes, and a medium, and relates to the field of power load characteristics, and the method comprises the steps: constructing a multi-factor fused meteorological index model of a single meteorological point, determining a multi-meteorological point fusion meteorological index of the target area according to the resident load proportion corresponding to the target area and a multi-factor fusion meteorological index model, and then constructing a target data set according to the multi-meteorological point fusion meteorological index and the resident load data; and segmenting the target data set to perform piecewise nonlinear regression analysis on the obtained target first data set and the target second data set, and determining the current residential load meteorological sensitivity of the target area through a target piecewise regression analysis function obtained through analysis and the target data set. Therefore, nonlinear segmented regression modeling and sensitivity analysis of the resident load and the fused meteorological indexes in the target area can be realized, and support is provided for refined analysis and management of the resident load.

A system and method for Medical Claims Risk Score (MCRS) algorithmic underwriting includes a predictive machine learning model configured to generate underwriting decisions on electronic applications. MCRS underwriting applies word embedding modeling, such as GloVe (global vectors), to transform high dimensional MC records into single-code word vectors. These single-code word vectors are employed in regression modeling, and may include summarized embedding coordinates aggregated at the applicant level. Regression modeling uses medical claim codes data and underwriting decision data stored for historical underwriting applicants to train a random forest model to predict relative mortality risk for underwriting applicants. A risk rating may be derived from the underwriting decision data based upon standard quantitative risk ratings of a plurality of risk classes. Other inputs to the random forest model may include cohort level applicant profile data, such as applicant issue age and sex.

This application discloses a method, system, device, and medium for production log analysis based on the Industrial Internet of Things (IIoT), relating to the technical field of IIoT. The method includes: extracting an environmental parameter set, a standard setting parameter set, and an actual processing parameter set corresponding to the target production line from the production logs; performing regression modeling based on the environmental parameter set, the standard setting parameter set, and the actual processing parameter set to obtain an initial SVM model; using the standard setting parameter set, the environmental parameter set, and the actual processing parameter set as model training data, and training and testing the initial SVM model based on the model training data to obtain an actual processing parameter prediction model; acquiring the environmental parameters of the area where the target production line is located and the processing parameters of the products, setting standard setting parameter constraints, calling the actual processing parameter prediction model, and selecting a set of standard setting parameters based on an annealing algorithm. This application has the effect of improving production efficiency.

The application discloses a two-stage large language model energy consumption analysis method and system for reasoning tasks, and belongs to the technical field of data mining.The application effectively solves the modeling problem of key dynamic characteristics by adding a response token number interval prediction stage, so that the energy consumption model can indirectly use output length information, and innovatively constructs and fuses enhanced features such as model total parameter count and theoretical floating point operation number, which have clear physical meanings.The system integrates multi-dimensional heterogeneous information such as prompt words, models and hardware, and realizes fine description of energy consumption differences of different reasoning tasks.The proposed two-stage framework of classification prediction and regression modeling has clear logic and is consistent with the energy consumption characteristics of the LLM reasoning process.The first stage focuses on predicting features strongly related to output, and the second stage performs comprehensive energy consumption regression, so that the model structure is reasonable and has strong interpretability.

The invention belongs to the technical field of traffic geographic information analysis, and discloses an urban crowd activity and space structure dynamic deduction method and system based on single traffic flow, a terminal and a storage medium. Identifying a spatial distribution mode through a flow similarity measurement method in combination with a hierarchical clustering algorithm; carrying out quantity aggregation on the identified spatial distribution mode according to grid units, and constructing a spatial grade distribution model; combining the urban built environment features with spatial grade distribution in the spatial grade distribution model, and constructing a comprehensive data set suitable for machine learning regression modeling; and based on the comprehensive data set, in combination with a machine learning model interpretation method of a game theory, quantitatively analyzing a nonlinear influence mechanism of urban built environment characteristics on different spatial distribution modes, and deducing internal relevance between urban crowd activities and spatial structures. The dynamic evolution process of the urban space structure is comprehensively realized.

The invention discloses a typical tree species growth prediction method, system and device based on multi-stage multi-factor regression and a medium, and relates to the technical field of tree species growth prediction.The method comprises the steps that multi-source heterogeneous data are collected and preprocessed; dividing the independent variables into different types of influence factors, and performing statistical test on each factor to obtain a preliminary candidate variable set; calculating a variance expansion factor of the candidate variables, and when the expansion factor exceeds a threshold value, reducing the correlation among the preliminary candidate variables by adopting a collaborative path method to obtain a final variable; performing regression modeling through a three-stage modeling method based on the final variable to generate a regression model, and establishing a regression sub-model for each partition; and based on the obtaining mode of the final variable, extracting a judgment rule of the tree species and outputting the judgment rule in a structured format. According to the method, high-precision prediction of the growth under multi-factor driving can be realized, and the problems of multi-collinearity, unstable variable selection, insufficient nonlinear structure expression and the like in a traditional regression model are solved.

The invention discloses an inference task-oriented two-stage large language model energy consumption analysis method and system, belongs to the technical field of data mining, and effectively solves the modeling problem of key dynamic characteristics by adding a response token number interval prediction stage, so that an energy consumption model can indirectly utilize output length information, and the analysis efficiency is improved. The method innovatively constructs and fuses model total parameter counting, theoretical floating point operation times and other enhanced features with clear physical significance, the system integrates cue words, models, hardware and other multi-dimensional heterogeneous information, fine description of energy consumption differences of different reasoning tasks is realized, and a proposed classification prediction and regression modeling two-stage framework has the advantages of high efficiency and high reliability. And the logic is clear and accords with the energy consumption characteristics of the LLM reasoning process. The first stage focuses on predicting and outputting strongly correlated features, the second stage carries out comprehensive energy consumption regression, and the model is reasonable in structure and strong in interpretation.

The invention provides a new energy electricity price prediction system, which comprises a data acquisition module for acquiring historical electricity market data, new energy power generation data, meteorological data, load and economic data and policy parameters, a data preprocessing module for performing missing value filling and abnormal value elimination on the acquired data, lagging factors, seasonal indexes and policy influence factors are generated; the model integration module comprises an LSTM-Transform hybrid model, a reinforcement learning model and a time sequence prediction model; the transaction simulation module executes medium and long term contract segmentation regression modeling and spot market marginal clearing calculation, the segmentation regression modeling divides a price gradient interval based on a contract electric quantity proportion, and the marginal clearing calculation is combined with a unit clearing curve and a tie line transmission constraint; and the decision optimization module outputs weighted comprehensive electricity prices and energy storage scheduling strategies under different contract proportions. The system realizes quantitative fusion of policy parameters, meteorological factors and market data, and breaks through the external variable integration bottleneck of a traditional time sequence prediction model.