18 results about "Predictive regression" patented technology

The invention discloses a solid waste water content prediction system and method based on an infrared thermal imager. The temperature field of the solid waste on the conveyor belt in the natural convection and forced convection moisture evaporation process is monitored and compared in real time through the thermal infrared imager, and the moisture content of the solid waste can be accurately predicted in combination with data processing, environmental parameter correction and prediction of a regression equation. Compared with a traditional sampling detection method, the method has the advantages of being high in real-time performance, non-contact, simple in equipment, good in economical efficiency and the like, can adapt to a complex industrial field environment, and provides a new technical means for intelligent and refined control over waste incineration. The system is simple in structure, high in reliability, good in detection precision and capable of achieving real-time prediction of the water content of the solid waste entering the furnace.

The application provides a driver fatigue prediction method and device, electronic equipment and storage medium, which obtains a monitoring video; obtains facial state features and a fatigue score according to the monitoring video; trains a fatigue prediction regression model by taking the facial state features as an input vector and the fatigue score as a target result; extracts facial state features of a to-be-detected driver from a to-be-detected image or video, inputs the facial state features of the to-be-detected driver into the trained fatigue prediction regression model, obtains a fatigue score corresponding to each time in a future period of time, and then can infer the time required to reach different fatigue levels according to the current time and the time corresponding to different fatigue levels, so as to achieve the purpose of predicting the fatigue condition of the driver in advance, help early warning, avoid vehicle accidents, and the same model can obtain corresponding prediction time according to different fatigue level requirements, and has high expansibility.

The application discloses an agent action prediction method based on multi-scale space perception, comprising map space structure modeling, historical trajectory feature extraction, feature fusion and multi-modal action prediction, wherein: the map space structure modeling uses a multi-scale graph convolutional neural network to extract map features from two-dimensional vector map data in an application scenario map, to obtain high-dimensional map feature information; the historical trajectory feature extraction uses a convolutional neural network and a feature pyramid network to extract high-dimensional trajectory data features of all agents; the feature fusion models and fuses the correlation of high-dimensional map feature information and high-dimensional trajectory data feature information through a self-attention mechanism, to obtain agent trajectory fusion features with direction information; and the multi-modal trajectory prediction uses the agent trajectory fusion features for prediction regression and confidence scoring, to output multi-modal complete trajectory coordinates and corresponding confidence scores of action prediction, so as to provide reasonable auxiliary decision-making for agent action.

The invention discloses a non-invasive pulmonary arterial hypertension hemodynamic monitoring method based on machine learning, and belongs to the technical field of pulmonary arterial hypertension hemodynamic monitoring. The non-invasive pulmonary arterial hypertension hemodynamic monitoring method based on machine learning comprises the following steps: collecting BCG signal data through static data collection equipment; eCG signal data are acquired through dynamic data acquisition equipment; pPG signal data are collected through a photoelectric finger clip; static feature extraction is carried out on static data composed of the BCG signal data and the PPG signal data, and dynamic feature extraction is carried out on dynamic data composed of the ECG signal data and the PPG signal data; and inputting the static characteristics and the dynamic characteristics into a regression model, and training the regression model by taking the hemodynamic parameter CO measured by the right cardiac catheter at the same time as a target to obtain a cardiac displacement prediction regression model. By adopting the non-invasive pulmonary arterial hypertension hemodynamics monitoring method based on machine learning, the problems that an existing pulmonary arterial hypertension hemodynamics monitoring method is complex in operation and cannot meet daily rehabilitation training monitoring use are solved, and the prediction precision is improved.

This disclosure relates to the field of bridge engineering, specifically to a design method, system, and medium for HPC-RC combined eccentrically compressed columns. The method includes: defining a design space; selecting optimal design data combinations based on engineering specification constraints, bearing capacity constraints, and cost-effectiveness values ​​to construct a design data sample set; constructing a two-branch heterogeneous neural network model, the model including an input layer, a shared feature extraction layer, a diameter prediction classification branch, and a reinforcement area prediction regression branch; constructing a loss function composed of focal loss and mean square error loss; training the two-branch heterogeneous neural network model using the design data sample set; and using the trained two-branch heterogeneous neural network model to predict the diameter and total reinforcement area of ​​the eccentrically compressed column. This disclosure improves the stability and accuracy of the design, reduces the computational burden, increases computational speed, and reduces computational complexity.

The invention discloses a road loss prediction method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring map information and road test data of a target area; according to the map information, extracting a geographic feature vector of the map information through a geographic pre-training model based on comparative learning training; according to the road test data and the geographic feature vectors, road loss prediction is carried out through a pre-trained road loss prediction regression model, and a road loss prediction value of the target area is obtained.According to the road loss prediction method and device, the road loss prediction is carried out through the geographic pre-training model based on comparative learning training, the direction of model feature extraction can be guided, and the accuracy of road loss prediction is improved. And the pressure of model fitting is reduced, so that the migration scene prediction accuracy is improved.

The invention relates to the technical field of disease risk intelligent prediction, in particular to a chronic kidney disease progress risk prediction system based on machine learning, which comprises an accumulated impact acquisition module used for inputting disease information and physiological index data of a current patient into a disease risk transmission network to acquire an accumulated impact condition; the predicted eGFR calculation module is used for inputting the accumulated impact condition into an eGFR prediction regression network to obtain a predicted eGFR curve; the similarity analysis module is used for calculating a similarity measurement value between the eGFR curves of the current patient and each target historical patient; the risk value calculation module is used for obtaining a renal function attenuation risk value based on the predicted eGFR curve, the similarity measurement value and the eGFR difference value between the current patient and each target historical patient; and the risk judgment module is used for judging whether the current patient has a renal function collapse risk based on the renal function attenuation risk value. According to the invention, early and accurate prediction can be carried out on the CKD progress risk.

The invention discloses an unmanned aerial vehicle operation state multi-threshold evaluation method and system based on uncertainty quantization, and relates to the field of aircraft state evaluation. The problems that in an existing method, the evaluation capability of a key parameter prediction model reflecting the operation state of the unmanned aerial vehicle is insufficient, and evaluation result information given by a single-threshold abnormal state evaluation method is limited are solved. The method comprises the following steps: inserting a Dropout network layer in a prediction regression model, and obtaining a Monte Carlo Dropout prediction model; obtaining an uncertainty estimation result according to a prediction model of Monte Carlo Dropout; calculating an operation state evaluation quantitative score according to the uncertainty estimation result and the unmanned aerial vehicle operation state deviation degree; determining a multi-stage operation state evaluation threshold according to the operation state evaluation quantitative score; and evaluating the operation state of the unmanned aerial vehicle according to the multi-stage operation state evaluation threshold. The method is applied to the unmanned aerial vehicle flight field.

This invention relates to the field of building structural performance evaluation, specifically a machine learning-based method for evaluating the seismic resistance of unreinforced masonry buildings. The method includes considering the spatial distribution and cumulative overall evaluation index of damage; pre-setting different levels of damage states; using an adaptive multi-scale progressive nonlinear dynamic response analysis method to obtain the median critical peak acceleration when the index reaches the limit value of each level under different working conditions; training a seismic resistance prediction regression model using the median values ​​of different levels; training a seismic resistance classification model using the median values ​​of each level; fitting adjustment parameters for service life; using the prediction model to obtain the median value prediction results corresponding to each level during evaluation; and inputting the prediction results optimized with the adjustment parameters into the classification model to obtain the building grade. This invention solves the technical problems of high computational cost of traditional evaluation methods, insufficient sample size and poor interpretability of machine learning methods, and difficulty in practical application of existing evaluation methods.

The invention relates to the technical field of refrigerator control, in particular to a refrigerator compressor start-stop control method based on data processing, and the method comprises the steps: collecting multi-dimensional time sequence data in the operation process of a refrigerator; responding to the condition that the compressor is switched to the shutdown state, and calculating a temperature return inertia index according to the time required by unit temperature rise in the refrigerator; determining the attention weight of the historical information according to the regenerative thermal inertia index and the switching frequency; performing weighted fusion on the historical information and the short-time state in the long-short term memory network based on the attention weight to obtain a fusion feature vector at the current control moment; and inputting the fusion feature vector into a prediction regression layer to obtain a prediction temperature, and realizing start-stop control of the compressor according to the prediction temperature. According to the technical scheme, the self-adaptability of start-stop control and the stability of the temperature in the box can be improved.

The invention discloses an establishment method and an evaluation method of a post-stroke inflammation injury prediction model, and belongs to the technical field of post-stroke inflammation injury prediction, and the establishment method of the post-stroke inflammation injury prediction model comprises the following steps: 1, collecting clinical index data of an AIS group and a health control group; 2, after the AIS group and the healthy control group are subjected to tendency score matching according to gender and age, a matched AIS group with the same number of people as that of the healthy control group is obtained, and matching features of the healthy control group and the matched AIS group are obtained; step 3, difference and correlation analysis of matching characteristics between the healthy control group and the matched AIS group; 4, carrying out single factor analysis on fibrinogen level influence indexes in the AIS group; and step 5, inputting the screened confounding factors into a full-automatic machine learning model for model training and verification to obtain a post-stroke inflammation injury prediction Logistic regression model. The regression model established through the steps is good in sensitivity and high in accuracy when being used for predicting the post-stroke inflammatory injury.

The invention provides a hydraulic tunnel surrounding rock mechanical parameter inversion method based on an FGO-CB collaborative optimization algorithm, and relates to the technical field of hydraulic and hydro-power engineering, the method combines an FGO global optimization algorithm with a CB local agent model, gives full play to the super-strong exploration capability of the FGO algorithm in the aspect of global optimization, and improves the performance of the FGO-CB collaborative optimization algorithm. And meanwhile, by utilizing the excellent prediction regression analysis capability of the CB model on the search space of the hypha population, the calling times of the refined numerical model in the inversion process are remarkably reduced, the convergence condition is quickly achieved, and the inversion precision is further improved.

Systems and methods are disclosed for image processing of cone beam computed tomography (CBCT) image data, relating to radiotherapy plans and treatments. Example operations for training a predictive regression model include obtaining a reference medical image of an anatomical region (e.g., from a reference CT image); generating a change image providing changes in the representation of the anatomical region (e.g., from the deformation or geometric transformation); identifying, for each of the plurality of varying images, a projection viewpoint (e.g., a projection capture angle from the CBCT projection space); generating a CBCT projection set and a simulation aspect set of the corresponding CBCT projection at each projection viewpoint; and training an algorithm in the regression model by using the corresponding CBCT projection set and the simulation aspect set of the CBCT projection. Corresponding operations for use of the regression model, including in radiotherapy treatment, are also disclosed.

The invention belongs to the technical field of geological disaster monitoring and early warning, and particularly relates to a ground surface settlement prediction system fusing AdaGCN and multi-scale LSTM. Comprising a data quality control and preprocessing module, an adaptive graph construction and spatial modeling module, a sequential sequence construction and multi-scale feature extraction module, a prediction regression module, an uncertainty quantization and output module and a model verification and performance evaluation module. According to the method, the adaptive graph convolutional network is adopted, and the traditional fixed geographical adjacency rule is replaced by data-driven adaptive association, so that the spatial association between PS points is reflected more accurately; an original high-frequency sequence and a down-sampling / accumulation sequence are processed through a double-branch LSTM structure, short-term fluctuation and long-term trend are captured, and the expression ability of the model to a complex settlement mode is enhanced; a physical constraint mechanism and an uncertainty quantification method are introduced, the prediction accuracy and interpretability are improved, and a more reliable basis is provided for engineering risk assessment.

This invention discloses a regression method for predicting urban road material inventory based on the GBDT algorithm, comprising the following steps: segmenting road network information into regions using ArcMap on GIS road network vector maps at various time points, determining parameters such as road width, pavement thickness, and density and admixture of road construction materials; calculating the material inventory of different road construction materials using Python programming; summarizing and organizing the material inventory data, and obtaining area, population, and economic data within the calculation area to construct a feature variable dataset, while converting the categorical feature variables into binary vectors using One-hot encoding; dividing the sample set into a training set and a validation set; training a material inventory prediction regression model based on the GBDT algorithm; evaluating the model's adaptability and validating it on an independent test set. This invention establishes a prediction regression model for the material inventory of various road construction materials in the road system, achieving high prediction accuracy.

This invention belongs to the field of computer vision and pattern recognition technology, specifically relating to a method and system for predicting energy consumption during fitness activities based on vision and deep learning. It aims to address the problem that existing vision-based general energy consumption prediction models suffer from severe feature interference and limited prediction accuracy due to neglecting biomechanical differences between different movement patterns. The invention includes: acquiring video stream data and extracting key human body points to construct a skeleton sequence tensor; identifying movement pattern categories using a spatiotemporal graph convolutional network; extracting associated kinematic feature time series based on movement categories; inputting the features into an energy consumption prediction regression model with an independent parameter space, encoding temporal dependencies based on a self-attention mechanism, and outputting predicted values ​​of body activity intensity. This invention, through a hierarchical architecture of identification followed by prediction, effectively improves the accuracy and robustness of energy consumption prediction across movement patterns.

The invention discloses a mass concrete temperature gradient adjusting system based on fuzzy control, and the system comprises the following modules: a wireless temperature collection and standardization module which is used for generating a concrete temperature time sequence data set and carrying out the standardization processing; the ARIMA modeling prediction module is used for carrying out stability detection and differential processing, constructing an ARIMA model meeting an inspection standard and completing initial temperature trend prediction; the SVR regression prediction module is used for constructing an SVR regression model based on the radial basis kernel function; the fuzzy control module is used for identifying an overtemperature trend point and executing fuzzy control; the Smith lag compensation module is used for receiving the control trigger signal to generate a compensated expected temperature control response curve; and the execution parameter conversion module is used for converting the control decision variable into a specific execution parameter. According to the invention, an ARIMA + SVR temperature prediction model and fuzzy control are fused, and an intelligent concrete temperature gradient adjusting system is constructed.