30 results about "Regression modelling" patented technology

Modeling appropriate comparable properties for a condo property includes accessing property data, and identifying candidate condo properties in the accessed property data to produce condo property data. A regression is performed using the property data, with the regression modeling the relationship between price and explanatory variables, and the explanatory variables including at least one variable that is specific to condo property assessment. The regression accommodates a modeling of comparable properties, such that a subject condo property and comparable properties may be identified and displayed.

The invention relates to an automatic regression diagnosis method on a big data platform, which is characterized in that it includes the following steps: importing data sources into the big data platform; performing random sampling of the data imported into the big data platform; On the node, the core algorithm is used to calculate each sample obtained by random sampling to obtain the regression model corresponding to each sample; the obtained regression models are cross-validated to obtain the final regression model. After adopting the method provided by the present invention, the regression modeling can be carried out completely automatically, that is, the work of regression diagnosis is directly completed by the algorithm without investing a large amount of modeling cost, and the efficiency of regression modeling and diagnosis is improved.

Aiming at the problem that the evaluation of the project popularity has one-sidedness since the defect report and the feature report are separately explored for the existing research, the invention provides a project popularity analysis method based on a mixed effect linear regression model, comprising: by collecting project data from GitHub and then using statistical analysis and regression modeling, giving the influence relation on the project popularity by the number of defect reports and the number of characteristic reports in the project, analyzing the relation between the improvement ofthe project popularity and the defect reports and feature reports through the difference of the influence factors of the defect reports and feature reports in the project to the project popularity; and progressively performing four dimensions of analysis on the description diversity of the defect reports and feature reports to find out the difference between the defect reports and the feature reports in describing the diversity. The invention comprehensively studies the project popularity by analyzing the difference between the number of defect reports and the number of feature reports in theproject, and may comprehensively evaluate the popularity of the project.

The invention discloses a soft sensor method based on the optimal selection and optimal regression of orthogonal components, aiming at optimally selecting the orthogonal components that are beneficial to the prediction quality index, and using the selected orthogonal components to establish a soft sensor for optimal regression. Measurement model. Specifically, the method of the present invention at first utilizes principal component analysis (PCA), independent component analysis (ICA) and partial least squares (PLSR) algorithms to obtain corresponding orthogonal components respectively at first, and then utilizes the nearest neighbor component based on genetic algorithm The optimal choice of orthogonal components is analyzed, and the optimal regression modeling based on the particle swarm optimization algorithm is implemented most effectively using the selected orthogonal components. Compared with the traditional method, the method of the present invention selects the feature components that are beneficial to the prediction quality index in an optimized manner and obtains the final quality index prediction value through the optimal regression vector. Therefore, the soft sensing performance of the method of the present invention is fully guaranteed, and it is a more preferred soft sensing method.

The invention provides a blind regression modeling method for protecting data privacy in a mobile group perception system. Blind regression modeling is achieved by the interaction between a mobile perception node and a mobile perception server and can be summarized as the following steps: selecting a ''clean'' perception data subset, constructing a rough global model and performing global regression model refinement. The invention further provides an updating method of a blind regression model built by the blind regression modeling method. Model updating performed by using new perception datacan be summarized as the following steps: constructing a new rough global model and performing new regression model refinement. According to the method provided by the invention, an aggregation resultis exchanged between the mobile perception server and the mobile perception node to ensure that the contents of the perception data are not disclosed; and the incremental model updating is adopted toreduce the communication and calculation costs of the mobile perception node. By adoption of the method provided by the invention, the effects of protecting the perception data privacy, weakening theinfluence of abnormal data on the regression model, improving the model accuracy and realizing lightweight model updating are achieved.

The invention discloses a VOD service cache optimization method based on an XGBoost algorithm in an edge network environment, comprising the following steps: collecting video data; taking the average visit volume as the prediction target, performing regression modeling with the XGBoost algorithm to obtain a prediction model; using the prediction model Predict the average access volume; establish a cache optimization model based on the prediction results; use the knapsack algorithm to solve the optimization model to obtain the final cache solution. The present invention considers that the edge server needs to process a large amount of video information, and the excellent data analysis ability of machine learning in big data processing, so that the edge server can minimize the service access delay, improve the cache efficiency of the edge server, and the The scheme is very simple and easy to implement, and has a good application prospect.

The invention discloses a face key point detection method and system based on local principal component analysis. The method includes steps: S1, collecting a large amount of face image sample data, and marking the face key points; S2, dividing the face key points Form a plurality of local key points, adopt principal component analysis to process each local key point respectively, obtain the principal component feature of each local key point; S3, calculate the combination of each key point of each piece of face image under the described principal component feature Coefficient; S4, build regression model, train model by described combination coefficient, generate combination coefficient regression model; S5, input the combination coefficient regression model of face image to be detected, predict and obtain described its combination coefficient; S6, based on prediction The obtained combination coefficients and the principal component features restore the key points of the face. The invention performs local principal component analysis on key points, predicts local principal component coefficients, reduces the complexity of directly performing principal component analysis on all key points, and improves regression modeling precision.

The invention belongs to the technical field of web application deployment and resource scheduling. In order to comprehensively consider the use status of different resources to expand and shrink to ensure application work efficiency, the invention provides a web application-oriented container cluster self-adaptive expansion and contraction system, including a resource monitoring module , the regression modeling module and the container scaling module, the resource monitoring module is used to obtain the container resource usage status; the regression modeling module is used to extract the container load data, and the CPU utilization rate, memory utilization rate, network throughput rate and file system throughput rate are The characteristic value, the number of requests processed per second is the label, and multiple linear regression modeling is performed to obtain the influence of the independent variable Ccpu, Cmemory, Cnetwork_io, and Cfs_io on the dependent variable Cqps, so that the known independent variable can be used to predict the change of the dependent variable . The present invention is mainly applied to web application occasions.

The present application discloses a method and device for displacement filling modeling, the method includes: the relative displacement s between multiple groups of other vehicles and the target vehicle obtained by identifying and obtaining based on historical point cloud data corresponding to multiple other vehicles c , relative velocity v c and the corresponding calculation time t c , the regression modeling obtains the first displacement filling model; the coefficient of determination R based on the first displacement filling model 2 and the preset coefficient of determination value to determine the target displacement filling model. It can be seen that based on the relative displacement s of multiple groups of other vehicles and the target vehicle c , relative velocity v c and the corresponding calculation time t c , the target displacement filling model obtained by regression modeling is used to achieve displacement filling. The point cloud data can more accurately predict the relative displacement of other vehicles and the target vehicle through the target displacement filling model, reduce the delay difference, and avoid inaccurate lag. question.

The invention discloses a heavy-duty tool thermal deformation prediction method taking environmental temperature into account. The method specifically comprises the following steps of predicting the tool thermal deformation caused by the internal heat source of a tool, predicting tool thermal deformation caused by the external heat source of the tool, summing the tool thermal deformation caused by the internal and external heat sources to obtain the final tool thermal deformation. Time-lag thermal deformation error caused by the environmental temperature is taken into account during the prediction of the tool thermal deformation caused by the external heat source of the tool, and multiple regression modelling based on the least square method principle is performed during the prediction of the tool thermal deformation caused by the internal heat source of the tool. The joint action effects reflecting the influence of the environment temperature nonlinear lag and the influence of the internal heat source on the heavy-duty tool are combined by the method, and the thermal deformation error under any environmental condition and processing condition can be effectively predicted in real time.