20 results about "Data regression" patented technology

The invention belongs to the technical field of compressor performance prediction, and particularly provides a method and system for determining a surge judgment critical value of a compressor based on multi-source data fusion. Comprising the steps that a compressor surge test system is built and operated, and a first surge critical value is calculated; obtaining multi-source historical data related to surge of the compressor, selecting characteristic variables to construct historical input vectors, and fitting the multiple regression model by using the historical input vectors to obtain a discrimination model; training the machine learning model to obtain a trained machine learning model; a real-time input vector is constructed based on data, collected in real time, of the to-be-monitored compressor, and then a second surge critical value and a third surge critical value are obtained; and fusing the first surge critical value, the second surge critical value and the third surge critical value to obtain a predicted surge critical value of the to-be-monitored compressor. According to the method, high-precision dynamic testing and historical data regression analysis are combined, and the actual value of the surge critical value is finally and quantitatively determined through multi-parameter coupling measurement and data mining.

A blood pressure prediction method includes performing data fragment division on the pulse wave; generating an input data tensor according to a total fragment number and the pulse wave data; performing multilayer convolution and pooling calculation on the input data tensor by using a blood pressure CNN to generate a feature data tensor; generating an input data matrix according to the feature data tensor; performing feature data regression calculation on the input data matrix by using a blood pressure ANN to generate a blood pressure regression data matrix; when a prediction mode identifier is a mean prediction identifier, performing mean diastolic pressure data calculation and mean systolic pressure data calculation to generate mean diastolic pressure data and mean systolic pressure data; or, when the prediction mode identifier is a dynamic prediction identifier, extracting diastolic and systolic pressure data to generate a predicted blood pressure data sequence.

The invention provides a mixed data regression model-based pm2.5 influence factor analysis method and system, and the method comprises the steps: building a mixed data regression model of which covariables are component data and numerical data and dependent variables are functional data: the mixed data regression model is a pm2.5 concentration curve of a city, is the functional data, is the proportion of first yield, second yield and third yield of the city, and is called the proportion of third yield for short; the data is component data, logarithm of per capita GDP, average temperature and numerical data, is a to-be-estimated component type coefficient changing along with time, is distributed to a component type covariable at any moment, is a to-be-estimated function type coefficient and is a function type residual error; obtaining robust M-estimation based on equidistant logarithmic ratio transformation, functional basis expansion and an iterative reweighted least square method; and according to the estimated values, analyzing the influence of the three-yield ratio, the per capita GDP and the average temperature of each city on the pm 2.5. The method can be used for analyzing the pm 2.5 influence factors.

The present application belongs to the technical field of compressor performance prediction, and specifically proposes a compressor surge discrimination critical value determination method and system based on multi-source data fusion. It includes building and running a compressor surge test system, calculating the first surge critical value; obtaining multi-source historical data related to compressor surge, selecting characteristic variables to construct a historical input vector, fitting a multiple regression model using the historical input vector to obtain a discrimination model; training a machine learning model to obtain a trained machine learning model; constructing a real-time input vector based on real-time data collected from the compressor to be monitored, and then obtaining the second and third surge critical values; and fusing the first, second and third surge critical values to obtain the predicted surge critical value of the compressor to be monitored. The present application combines high-precision dynamic testing and historical data regression analysis, and ultimately quantitatively determines the actual value of the surge critical value through multi-parameter coupling measurement and data mining.

The invention relates to the technical field of control engineering, and discloses a control system and method for high-temperature gas heating, and the system comprises a high-temperature gas heating physical entity, a sensing detection group, and an advanced control unit. The advanced control unit comprises the following modules which work cooperatively: a data acquisition and preprocessing module which is used for acquiring system output (a gas outlet temperature measurement value), control input and measurable disturbance data acquired by the sensing detection group, and constructing a data regression vector reflecting dynamic historical information of the system; through a dynamic characteristic matrix self-correction module, a dynamic characteristic matrix representing a system input and output mapping relation is constructed and updated in real time by using a recursive algorithm with a forgetting factor; the method effectively solves the problems of strong nonlinearity and time-varying characteristics caused by dramatic changes of gas physical properties and heating element characteristics along with temperature in a high-temperature gas heating process, and remarkably improves the adaptive capacity of the model in a full working condition range.

The invention discloses a small sample table type data regression prediction method and system based on a graph neural network and a medium, and the method comprises the steps: carrying out the preprocessing of obtained table type data, and obtaining a to-be-predicted data sample; performing feature extraction on a to-be-predicted data sample by using a trunk feature extraction module to obtain an initial embedding containing the feature information of the sample; respectively inputting the initial embedding into a residual branch and a gating branch in a feature refining module, and respectively obtaining a direction correction vector and correction intensity corresponding to the to-be-predicted data sample; weighting the direction correction vector according to the correction intensity, then performing residual connection with the initial embedding, and further performing normalization to obtain refined embedding; constructing a sample relation graph according to refined embedding, and further performing information spreading and aggregation by using a graph attention network to obtain enhanced embedding; and splicing the enhanced embedding and the initial embedding, inputting a prediction head, and outputting a regression prediction value. According to the method, the accuracy of small sample table type data regression prediction can be effectively improved.

The invention belongs to the technical field of automobile sheet hot galvanizing, and particularly relates to a high-end automobile sheet hot galvanizing process surface vibration mark defect forecasting method, which comprises the following steps of S1, collecting main equipment and process parameters of an air knife of a hot galvanizing unit; s2, flow field characteristic parameters of air knife distribution on the surface of the strip steel are calculated; s3, collecting historical production data; s4, calculating a vibration coefficient of the historical production data; s5, statistically calculating a critical vibration coefficient of the historical production data; s6, formulating a vibration ripple generation discrimination model; and S7, the vibration discrimination model is used for predicting the vibration ripple defect of the to-be-produced coil. According to the overall structure provided by the embodiment of the invention, a fitting model of the surface of the strip steel affected by the air knife flow field is developed through a large amount of field data regression and theoretical analysis, and on the basis, the occurrence probability of the vibration marks is represented by the vibration coefficient; and a limit vibration coefficient and a corresponding vibration ripple prediction and judgment model are given in combination with regression of historical data.

The application discloses a kind of based on dimensionless array combination's heavy oil steam flooding main control factor analysis and recovery factor prediction method.The method includes: obtaining fluid property, interface property and core physical property parameter;Carry out heavy oil steam flooding experiment under different working conditions, obtain recovery factor data;Capillary number, bond number and gravity number are calculated, the relative action of viscous force, capillary force and gravity is characterized;Dimensionless array combination parameter including bond number, capillary number and viscosity ratio before and after oil displacement is constructed;Quantitative prediction model of recovery factor and dimensionless array combination parameter is established by determining correction coefficient through experimental data regression;The model is applied to the recovery factor prediction and process parameter optimization of steam drive under different preheating temperature, steam dryness, injection speed and injection dip angle and other working conditions.The application realizes the unified quantitative characterization of heavy oil steam flooding multi-acting force, establishes recovery factor quantitative prediction method, and provides theoretical basis for steam drive development plan optimization.

The present invention is a plating assistance system, a plating assistance device, and a storage medium, which easily determine an implementation condition for improving in-plane uniformity obtained by a plating process. The plating assistance system includes: a simulator (362) that predicts an in-plane uniformity value of a plated film formed on a substrate in accordance with a hypothetical condition related to an electrolytic plating process of the substrate; a numerical analysis data storage section (370) that stores, with respect to a plurality of hypothetical conditions, numerical analysis data that corresponds each hypothetical condition to the in-plane uniformity value; a regression analysis section (250) that calculates, by regression analysis based on the numerical analysis data, a model that takes the in-plane uniformity value as a target variable and takes variables of the hypothetical conditions as explanatory variables; and an implementation condition exploration section (252) that explores, using the calculated model, an implementation condition that is a recommended value of the hypothetical condition related to the in-plane uniformity of the plated film formed in the electrolytic plating process of a plating target substrate.

The invention belongs to the field of power grid abnormity identification, and relates to an abnormal data identification method and system for time-day-and-frequency-band characteristic engineering, and the method comprises the steps: carrying out the processing of initial multi-source data, and obtaining multi-dimensional time series data; decomposing and reconstructing the multi-dimensional time sequence data to obtain frequency band multi-dimensional time sequence data of a plurality of frequency bands; for each piece of frequency band multi-dimensional time sequence data, establishing a frequency band data regression model and screening a core feature set; based on a core feature set of a to-be-evaluated time period, outputting a monitoring data predicted value of the to-be-evaluated time period through the frequency band data regression model, and based on the monitoring data predicted value and target evaluation data, determining abnormal monitoring data; therefore, whether the power grid monitoring data is abnormal or not can be accurately identified, and technical support is provided for fine technical management and control of power grid operation, equipment troubleshooting and scheduling strategy optimization.

The invention relates to the technical field of electric power measurement, in particular to a data center PUE real-time monitoring method and system, and the method comprises the following steps: obtaining current, voltage, power, environment and business load data, setting a window to calculate total energy consumption and IT equipment energy consumption, dividing the total energy consumption and IT equipment energy consumption to obtain a PUE value, storing the total energy consumption according to a time sequence, constructing a historical sequence by the PUE and a business load, and calculating the PUE value according to the historical sequence. And inputting the LSTM network to predict a future energy consumption curve. In the invention, current and voltage values, input and output power of a power supply module and operation power of a core component are acquired in real time, and calculation mapping of total energy consumption of a data center and energy consumption of IT equipment is established, so that the problem of one-sided PUE results caused by limitation of traditional measurement points is overcome, and the power supply use efficiency is obtained; energy consumption values and service load data are associated to construct a historical energy consumption sequence, and a future energy consumption expected curve is output by using time series data regression prediction capability, so that the defect that only post statistics can be performed and the trend cannot be pre-judged in the prior art is overcome, and decision support is provided for active energy efficiency optimization.

The specification provides a fracturing wellhead pressure prediction method and device based on multivariate data regression. Multivariate fracturing construction data of a target well in a target area is acquired; a crack initiation point and a pump stop point are determined according to a first-order derivative of a displacement and a first-order derivative of a tubing pressure in the multivariate fracturing construction data, and effective operation data from the crack initiation to the pump stop are determined by using the crack initiation point and the pump stop point; the effective operation data is mapped to a preset sampling step length by using a preset linear interpolation rule to determine a fixed-length feature sequence meeting a preset dimension; a final multivariate feature tensor is determined according to the fixed-length feature sequence by using a preset feature derivation calculation rule; and a full-sequence wellhead pressure prediction value of the target well is determined by using a preset pressure prediction model and the final multivariate feature tensor. Thus, full-sequence wellhead pressure prediction of the target well is realized, effectively solving the problems of insufficient prediction accuracy and poor sequence integrity in the prior art.

The application relates to the technical field of intelligent diagnosis, in particular to a power equipment installation and debugging fault intelligent diagnosis and process optimization method. The application realizes rapid positioning and accurate diagnosis of faults by comprehensively analyzing damage conditions of power equipment parts caused by mechanical, electrical and environmental factors in the installation and debugging process, quantifies mechanical deformation, current loss, temperature and humidity, magnetic field and other multi-physical field abnormalities, and constructs a cross-domain comprehensive damage evaluation model. Through dynamic threshold adjustment and historical data regression, the adaptability and early warning capability of diagnosis are improved. By adopting descending order sorting to preferentially check high-risk positions and combining average value comparison to publish targeted process optimization suggestions, the fault positioning efficiency is improved, and closed-loop management from fault repair to preventive process improvement is realized.

This invention provides a vehicle speed estimation method and system based on dual magnetometers, belonging to the field of navigation and positioning technology. The method includes: collecting three-axis magnetic field data from the front and rear of the vehicle using dual magnetometers, and simultaneously recording the true speed value output by the odometer; constructing a dataset based on the three-axis magnetic field data and the true speed value; training a speed regression network model based on a neural network using the dataset; and when the odometer output value is missing or malfunctioning, collecting real-time three-axis magnetic field data from the front and rear of the vehicle using dual magnetometers and inputting it into the speed regression network model to obtain the speed estimate output by the speed regression network model. This invention combines six-dimensional time-series data collected by dual magnetometers with a lightweight neural network structure to train an end-to-end speed regression network model, enabling direct regression of vehicle speed based on dual magnetometer data, reducing error sources, and improving the overall robustness of speed estimation.

The invention relates to an early warning method and system applied to an abnormal state of a vehicle chassis detection area, and the method comprises the steps: U1, obtaining the image data information of the detection area and the image data information of the vehicle chassis in real time based on a camera above the detection area in a process of conveying the vehicle chassis to the detection area, acquiring data information of position coordinates of the vehicle in real time based on a vehicle-mounted positioning sensor; and U2, on the basis of the image data information of the detection area and the image data information of the vehicle chassis, predicting a stop position point of the vehicle chassis in the detection area by adopting a data regression prediction algorithm based on a saltcat swarm optimization-convolution-bidirectional long-short-term memory network-attention mechanism. And the predicted data information of the stop position point of the vehicle chassis in the detection area is obtained. Whether the vehicle arrives at the detection area or not can be accurately monitored in real time, the detection area of the vehicle chassis is monitored in real time in the detection process, and the safety of vehicle chassis detection is improved.

The application discloses a gasifier operation load distribution control method, electronic equipment and storage medium. The method comprises the following steps: obtaining historical load data of historical operation of a gasifier; obtaining a load function of effective gas production rate and load operation value of the gasifier through data regression fitting according to the historical load data; obtaining a prediction function of effective gas production according to the load function; establishing an optimization function according to the prediction function to obtain a load target value of the gasifier; and adjusting an oxygen-coal ratio of the gasifier according to the load target value. The application establishes a function relationship between the effective gas production rate and the load through regression fitting of historical operation data and data modeling, and establishes an optimization model on the basis, so that the optimal load target value is automatically found under the target effective gas demand with the minimum raw material consumption as the optimization target, thereby reducing the raw material cost and the labor cost.

The application relates to an ABTest system shunting verification method and device and computer equipment. The method comprises the following steps: stratifying an experimental area, obtaining preset parameter default values of a control group and an experimental group of each experimental layer, and the number of the experimental layers is at least one; sending access traffic to the control group or the experimental group of the experimental layer, and returning a first parameter default value; based on the preset parameter default values, judging whether the first parameter default values inconsistent with the preset parameter default values exist, so as to verify whether the ABTest system shunting state is abnormal. By adopting the method, the data regression test workload can be reduced, the test result stability can be improved, and the verification process is simpler and more efficient.

The application discloses a method for improving the uniformity of insoluble anode VCP copper plating based on 6sigma tools, relates to a PCB production process, and aims at the problem of great copper plating difference in the prior art. The uniformity of copper plating of an insoluble anode vertical continuous electroplating line is taken as a research object. Through process analysis and early data regression analysis, key influencing factors are found. According to the characteristics of the line, a DOE factor experiment of the key factors is designed. In model analysis, a stepwise method is ingeniously used, a first-order term of each factor is reserved, and a part of second-order or multiple terms with low contribution degrees is reasonably deleted, so that a more effective model can be obtained, and more accurate prediction variables can be achieved. After the final optimization parameters are input into the equipment, test boards and production boards are followed, and actual copper plating is carried out, the difference can meet the expected target.

The present application relates to the technical field of measuring electric power, in particular to a data center PUE real-time monitoring method and system, comprising the following steps: obtaining current voltage, power, environment and service load data, setting a window to calculate total energy consumption and IT equipment energy consumption, dividing the two to obtain PUE value, storing total energy consumption, PUE and service load in time sequence to build historical sequence, and inputting LSTM network to predict future energy consumption curve.In the present application, current voltage values are obtained in real time, power module input and output power and core component running power are obtained, total energy consumption of the data center and IT equipment energy consumption calculation mapping are established, the problem of one-sided PUE result caused by the limitation of traditional measurement point is overcome, power utilization efficiency is obtained, energy consumption values are associated with service load data to build historical energy consumption sequence, time series data regression prediction ability is used to output future energy consumption expected curve, the defect that the prior art can only statistically analyze after the event but cannot predict the trend is solved, and decision support is provided for active energy efficiency optimization.

The application belongs to the field of power grid anomaly identification, and relates to an abnormal data identification method and system based on time-of-day and frequency-band feature engineering, comprising: processing initial multi-source data to obtain multi-dimensional time series data; decomposing and reconstructing the multi-dimensional time series data to obtain frequency-band multi-dimensional time series data of multiple frequency bands; for each frequency-band multi-dimensional time series data, establishing a frequency-band data regression model and screening a core feature set; based on the core feature set of an evaluation period, outputting a monitoring data prediction value of the evaluation period through the frequency-band data regression model, and determining abnormal monitoring data based on the monitoring data prediction value and target evaluation data; thereby achieving accurate identification of power grid monitoring data anomalies, and providing technical support for fine technical management and control of power grid operation, equipment fault troubleshooting and dispatching strategy optimization.