6 results about "Principal component regression" patented technology

The application relates to the technical field of power energy consumption prediction, in particular to a power energy consumption prediction method and system based on multidimensional data and a medium, the method comprising the following steps: collecting power consumption data and various influence data of a power grid in real time; determining redundancy imbalance degrees of the various influence data in a time sequence interval; calculating mutual information dependency degrees of the various influence data in the time sequence interval; identifying each mutation point of the power consumption data in the time sequence interval; determining prediction effective contributions of the various influence data in the time sequence interval; combining the redundancy imbalance degrees and the prediction effective contributions to correct regression coefficients in a prediction process of the power consumption data of the power grid by using a PCR principal component regression algorithm. Therefore, the prediction accuracy of power energy consumption is improved.

A device and method for detecting the gas content of a two-phase flow of oil and gas using a halogen lamp light source are disclosed. The method includes the following steps: Step 1: Turn on the halogen lamp light source and inject the prepared two-phase flow of oil and gas to be tested into the pipeline through a three-way valve according to the pre-calibrated gas content. Step 2: Turn on the peristaltic pump to uniformly fill the pipeline with the two-phase flow of oil and gas to be tested, then turn off the peristaltic pump. A first collimating lens is connected to the halogen lamp light source via a first optical fiber to receive the incident light emitted by the halogen lamp light source. A spectrometer is connected to the second collimating lens via a second optical fiber to receive the outgoing light of the two-phase flow of oil and gas passing through the flow cell. The spectrometer records the spectral information in a computer. Step 3: Repeat steps 1 and 2 until the spectral information of all calibrated gas contents is obtained. Step 4: Establish a prediction model using the spectral information. Specifically, a principal component regression (PCR) model for detecting the gas content of the two-phase flow of oil and gas is established to predict the gas content of the two-phase flow.

The invention relates to a cable degradation degree detection method and device based on principal component regression. The method comprises the following steps: acquiring a plurality of harmonic data in currents of a plurality of circuits, and performing data conversion on the plurality of harmonic data to obtain a plurality of harmonic data in a target format; performing data preprocessing on the harmonic data in the target format to obtain preprocessed target harmonic data; performing principal component analysis on the target harmonic data to obtain a data matrix corresponding to the target harmonic data; inputting the data matrix corresponding to the target harmonic data into a pre-trained target principal component regression model to obtain a cable degradation degree prediction value of the circuit corresponding to the target harmonic data; determining the degradation state of the cable of the circuit according to the cable degradation degree prediction value of the circuit; and determining a circuit cable maintenance suggestion according to the degradation state of the circuit cable. Stable and accurate degradation degree detection can be performed on the cable at least through a principal component regression model and a principal component analysis method.

The invention provides a method for constructing a prediction model for early warning of acquired myasthenia related to sepsis based on an electrophysiological index, a prediction method and application, and the construction method comprises the steps: determining the electrophysiological index most related to an APACHE II score and an SOFA score based on factor analysis and principal component regression analysis; the method comprises the following steps: acquiring personal information, electrophysiological indexes, inflammatory response and score, MRC score and EMG, RNS and DMS measurement data of a sample SIRS patient; constructing different attribute sets according to data categories, and constructing a similarity fusion network model based on the attribute sets; and based on a similarity measurement matrix output by the similarity fusion network model, carrying out clustering analysis according to a spectral clustering algorithm, outputting a first classification category and a confusion matrix, and based on the confusion matrix, taking the similarity fusion network model meeting accuracy and precision requirements as an early prediction model of acquired myasthenia related to sepsis. The prediction model can predict the type of the patient, and is beneficial to early intervention and patient rehabilitation.

The invention provides a short-term power load interval prediction method based on kernel principal component regression analysis. The method comprises the following steps: acquiring historical power load data and corresponding historical power load influence data; clustering the historical power load influence data, and determining the historical power load influence data corresponding to each power consumption scene according to a clustering result; for the historical power load influence data corresponding to each power consumption scene, performing dimension reduction based on kernel principal component analysis, and extracting main power load influence characteristics corresponding to each power consumption scene; constructing a load interval prediction model based on the main power load influence characteristics corresponding to each power consumption scene and the corresponding historical power load data; and performing interval prediction on the short-term power load of the power system according to the load interval prediction model corresponding to each power consumption scene. The method can provide an interval prediction result for short-term power load prediction of the power system, overcomes the uncertainty of the prediction result, and gives consideration to the prediction efficiency.

The application discloses a kind of urban space net red phenomenon determination method, it is related to urban space technical field, including the following steps: collection short video check-in data, obtain the online traffic and offline traffic of multiple net red check-in points;Based on the ratio of offline traffic and online traffic, obtain the traffic conversion rate of multiple net red check-in points in the preset research range;The surrounding area of net red check-in point is researched, and the influence factor of urban space net red phenomenon is determined and corresponding data is obtained;Based on the traffic conversion rate of net red check-in point and each influence factor, linear regression model is established respectively, and multiple regression analysis equation is obtained;The weight of each influence factor is obtained by using principal component regression analysis method, and weight analysis result is obtained.The application can analyze the correlation between urban net red phenomenon and influence factor, help to develop urban space potential, and help to promote the development of urban space net red phenomenon by optimizing space quality targetedly.