1606 results about "Random forest" patented technology

The invention provides an urban drainage pipe network monitoring data cleaning and intelligent prediction method, and the method comprises the steps: firstly obtaining pipe network monitoring data, and carrying out the classification tracking and repairing of missing values; adopting a dynamic IQR algorithm based on a sliding window to adaptively identify abnormal candidate points; secondly, introducing a pipe network topological relation, comparing upstream and downstream data change trends, eliminating non-physical anomalies caused by equipment faults, and reserving real hydraulic events; calculating the physical delay time between the nodes by using the cross correlation coefficient; and finally, constructing a random forest model, taking upstream historical data after delay alignment as feature input, and realizing accurate prediction of a future water level and quantification of a feature contribution degree. According to the method, a physical mechanism and machine learning are fused, the problems that data cleaning lacks adaptivity and a deep learning model lacks interpretability are effectively solved, and the accuracy of waterlogging early warning is improved.

The invention belongs to the technical field of flight simulator maintenance, particularly relates to a flight simulator predictive maintenance method based on machine learning, and solves the problems that existing maintenance depends on regular inspection and passive maintenance, fault early warning lags behind, and the false and missing report rate is high. The method comprises the following steps: acquiring historical operation data, sensor time sequence data, fault records and environmental parameters of a flight simulator, and carrying out cleaning, labeling and feature fusion preprocessing on the historical operation data, the sensor time sequence data, the fault records and the environmental parameters; constructing a composite health feature set containing statistical features, dynamic health state values and aerial material reliability parameters; a mixed prediction model (random forest feature screening + LSTM time sequence prediction + adaptive correction reliability evaluation) is adopted to train a model, prediction result fusion analysis and multistage decision rule post-processing are combined, and a maintenance work order and a spare part demand plan are output. According to the method, the accuracy and timeliness of fault prediction are improved, the maintenance conversion from passive response to active pre-judgment is realized, and the maintenance cost and the non-planned shutdown risk are greatly reduced.

The invention relates to the technical field of fault diagnosis, in particular to a multi-modal bearing fault diagnosis method based on cross-domain transfer learning, which comprises the following steps: constructing a source domain and a target domain; calculating fault frequency characteristics and revolution frequency of the bearing; calculating a first feature vector; performing correlation coefficient and strategy feature standardization pipeline operation, collaborative feature screening strategy and dimension reduction on the first feature vector, and obtaining a second feature vector by using inherent importance and arrangement importance of a random forest classifier; training a plurality of benchmark test models by using the second feature vector of the source domain; evaluating the effectiveness of the second feature vector and determining a performance baseline; and training the target domain by using the 1D-CNN network, carrying out end-to-end cross-domain migration training on the 1D-CNN network by using the comprehensive loss function of the source domain and the target domain, and outputting a predicted fault type. The problem that the accuracy of transfer learning is affected due to lack of multi-modal data screening in an existing method is solved.

The invention discloses an emergency pre-examination grading system and method based on collaborative decision of a large language model and a tree model. The method comprises the following steps: constructing a data set according to patient information and screening data; training an initial random forest model based on the data set, generating a basic decision rule, mining and extracting high-frequency features based on association rules, and combining to obtain a candidate rule pool; constructing a cue word structure adaptive to the field, driving LLM to complete rule correction, and forming a correction rule set; performing multiple rounds of rule random division and rule combination generation based on the correction rule set, and then screening out an optimal rule; and on the basis of a specific scene, the expert rule and the corrected optimal rule are fused, matching is carried out on patients, and emergency pre-examination grading is realized. According to the method, the interpretability is improved while the grading accuracy is improved, and the problem of poor cross-courtyard generalization of a machine learning model is effectively solved. The generalization ability of the rule is remarkably improved, and the method is adaptive to a multi-center combined diagnosis and treatment scene.

The invention discloses an ecological shoreline diagnosis method and system based on hydrological-biological communication, and is applied to the technical field of water environment ecological restoration and shoreline health assessment. Comprising the following steps: dividing intertidal zones of a shoreline research area, and generating uniformly distributed sampling points in each tidal zone; extracting a plurality of indexes related to hydrological and biological connectivity, and constructing a comprehensive hydrological-biological connectivity state index through principal component analysis; dam feature data of a shoreline area are obtained, correlation analysis and collinearity screening are carried out, and key shoreline features are screened through a random forest algorithm; constructing a mechanism connectivity index based on a circuit theory; constructing and training a Bayesian network prediction model; and performing connectivity state diagnosis, attribution analysis and restoration scheme effect simulation on the target shore section by using a Bayesian network prediction model. By coupling hydrological and biological connectivity, static element evaluation is changed into dynamic ecological process diagnosis, and dominant factors are accurately positioned.

The invention provides a hydropower station fusion computing power intelligent server system, and relates to the technical field of carbon emission. The method comprises the following steps: an acquisition correction module acquires hydropower station generation power data and server cluster instantaneous energy consumption data in real time, establishes a time sequence corresponding relation between a power generation side and a power utilization side, and obtains clean energy supply quantity data; the prediction scheduling module generates a non-tampering carbon footprint tracking chain number, and predicts a clean energy supply fluctuation trend sequence by adopting a long short-term memory network algorithm; training a random forest model to predict a computing power demand trend sequence; optimizing task scheduling according to a carbon emission minimization target, and generating dynamic balance configuration data of a computing power task and clean energy supply; and the monitoring evaluation module outputs computing power task level carbon emission and a clean energy use ratio in real time through an application programming interface according to the dynamic balance configuration data, and generates quantitative evaluation data of a computing power service carbon neutralization target by adopting a data visualization technology monitoring technology.

The invention discloses a multi-energy load prediction method based on feature screening and multi-model fusion, and belongs to the field of electric power and comprehensive energy load prediction. The invention provides a three-stage hybrid learning prediction framework. In the first stage, dynamic feature screening is achieved through a recursive feature elimination cross validation method based on expert knowledge constraints, key meteorological elements and multi-energy load time sequence features are reserved, and redundant feature interference is reduced. In the second stage, a multi-task long-short-term memory network is constructed, and coupling relation modeling and collaborative prediction of cold, heat and electricity multi-energy loads are achieved through sharing time sequence characteristic representation and a task exclusive output structure. And in the third stage, a random forest is adopted to carry out nonlinear correction on the residual error of the sub-model, so that the precision and robustness of prediction in sudden disturbance and local non-stationary scenes are improved, the prediction error is effectively reduced, and the stability of multi-energy load prediction is improved. And reliable support is provided for optimized operation, scheduling decision and renewable energy consumption of the park integrated energy system.

The invention discloses a method for constructing a machine learning model based on cerebellar subregion multi-modal radiomics. According to the method, [18F] FDG PET metabolic features and 3DT1 W MRI structural features of a cerebellar subregion are extracted, a random forest classification model is constructed after feature selection, and high-precision identification of the Alzheimer's disease (AD) and a cognitive normal (CN) is realized in combination with SHAP analysis. According to the method, the multi-modal radiomics characteristics of the cerebellar subregion are integrated for the first time, the accuracy and interpretability of AD early diagnosis are improved, and a non-invasive and efficient diagnosis tool is provided for clinic.

The invention provides a grounding wire state monitoring and early warning system and method based on a random forest algorithm, relates to the technical field of electric power safety monitoring, and solves the limitation problems of low efficiency, poor data continuity, inaccurate early warning judgment and the like in the existing scheme. In the system, a multi-source sensor network acquires multi-dimensional operation data corresponding to a grounding wire and transmits the data to a data acquisition terminal; the data acquisition terminal performs feature extraction processing to generate a feature vector set and uploads the feature vector set to the cloud data processing platform; the cloud data processing platform uses a random forest classification model, based on the feature vector set, realizes dynamic feature weight adjustment through feature importance analysis, constructs a hierarchical decision tree structure, identifies and outputs a grounding wire fault mode, and issues the grounding wire fault mode to the local early warning terminal; and the local early warning terminal provides early warning information for the ground wire site by adopting a graded and differentiated early warning mode. According to the invention, high-precision early warning and predictive maintenance of the state of the grounding wire are effectively realized.

The invention discloses a sewage treatment dosing method and system based on machine learning, electronic equipment, a storage medium and a product. The method comprises the following steps: constructing a multi-parameter time sequence data set between historical operation data about a sewage treatment system and historical dosing amount data of time lag duration t corresponding to each group of historical operation data; a dosing prediction model is constructed based on a random forest network, and a multi-parameter time sequence data set is used for training; dynamically updating the lagging duration t; operating parameters of the sewage treatment system are collected in real time, and the trained dosing prediction model is used for predicting the dosing amount of the optimal dosing lagging duration. According to the method, the hysteresis of an existing dosing control mode is effectively compensated, meanwhile, the delay time is dynamically regulated and controlled, accurate prediction of the dosing amount is achieved, the fluctuation risk of the effluent quality is reduced, and the working condition generalization ability of a dosing system is improved.

The invention relates to a rolling bearing embedded lubrication state evaluation method and a computer device. The rolling bearing embedded lubrication state evaluation method comprises the steps that a temperature signal of a rolling bearing and a frequency domain spectrum amplitude sequence are spliced to generate a multi-source spectrum fusion feature vector; a random forest model is adopted to screen mean value features extracted from the temperature signals and time domain features and frequency domain features extracted from the vibration signals, the sound signals and the sound emission signals to obtain a sensitive feature set; inputting the sensitive feature set into a support vector machine model to output a first lubrication state evaluation result; inputting the multi-source spectrum fusion feature vector into a MobileNet V2 model to output a second lubrication state evaluation result; and fusing the first lubrication state evaluation result and the second lubrication state evaluation result through a D-S evidence theory to obtain a rolling bearing lubrication state evaluation result. The multi-source fusion evaluation method considering accuracy, robustness and engineering practicability is constructed, so that the problems of high limitation, insufficient fusion layers, high model complexity and the like of an existing single signal are solved.

The invention relates to the technical field of geological informatization, in particular to an artificial intelligence modeling analysis method for a hydrate pilot production data set, which comprises the following steps of: acquiring logging data, lithology data, stratum physical property parameters and natural gas hydrate production dynamic data; screening, cleaning, complementing, de-noising and standardizing are carried out in sequence to obtain an artificial intelligence modeling data set; and establishing a stratum lithology machine learning recognition model, a stratum physical property machine learning recognition model and a natural gas hydrate artificial intelligence historical fitting model through a support vector machine SVM, a random forest RF and a neural network DNN. According to the method, a serial modeling architecture of lithology identification, physical property prediction and production history fitting is created, and the prediction output of the upstream model is used as the optimization input of the downstream model, so that the downstream production prediction model can learn physical property parameters which are recalculated based on machine learning and have higher precision; and the accuracy of final production prediction is improved from the data source.

The invention relates to the field of city analysis, and discloses a method for analyzing the nonlinear influence of a city environment on city vitality, and the method comprises the steps: carrying out the preprocessing of multi-source geographic big data and multi-source remote sensing data; according to a Deeplab V3 + deep learning semantic segmentation algorithm, predicting the pixel ratio of each category in each streetscape image, and constructing streetscape features based on the pixel ratio; constructing urban vitality evaluation indexes, and aggregating the urban vitality evaluation indexes by using a TOPSIS algorithm to obtain an evaluation result of the urban vitality; constructing an urban environment index according to the preprocessed multi-source remote sensing data; analyzing the spatial distribution difference of the non-linear influence of the urban environment on the urban vitality by using a geographically weighted random forest algorithm; and analyzing the threshold effect of the non-linear influence of the urban environment on the urban vitality by using a Gaussian fitting line algorithm. According to the method, street view features are extracted through a semantic segmentation technology, and comprehensive, scientific and refined quantitative evaluation of urban vitality is realized.

The invention provides a part lightweight design method based on a random forest model, and the method comprises the steps: defining a design variable and a response target, and generating a sample point through a test design sampling method; geometric modeling and updating, finite element preprocessing, solving calculation and result extraction are automatically completed through parameterized scripts, and a sample data set is formed; independently training a random forest regression model for each performance response by using the sample data set, and constructing a quality rapid calculation model; verifying the random forest agent model until all agent models meet preset precision requirements; on the basis of the proxy model, constructing an optimization problem, carrying out optimization solution by adopting a multi-objective optimization algorithm, and outputting a Pareto optimal solution set; and selecting a candidate design scheme from the Pareto optimal solution set, executing high-precision finite element simulation, comparing a prediction result and a simulation result of the proxy model, and if an error is within an acceptable range and all performances meet design requirements, determining a final lightweight design scheme.

The invention provides a random forest shallow sea sediment classification method based on multi-temporal remote sensing image fusion, and relates to the technical field of sediment information extraction. Comprising the following steps: 1, collecting and preprocessing multi-temporal image data to obtain remote sensing reflectivity; 2, the water depth of each single-time-phase image is inverted, and the optimal water depth is obtained; 3, calculating bottom reflectivity characteristics of blue and green wave bands based on the optimal remote sensing image; 4, respectively calculating topographic features and spectral features based on the optimal water depth and the optimal remote sensing image; and 5, in combination with the bottom reflectivity features, the topographic features and the spectral features, carrying out random forest feature optimization and classification model training, and generating a substrate classification result. On the basis, the method solves the problems that an existing remote sensing image substrate classification method is insufficient in feature consideration, noise in a single-time-phase image can cause low classification precision, and therefore negative effects can be generated on accurate acquisition of substrate information.

The invention relates to the technical field of industrial vision and artificial intelligence, and discloses an aluminum coating formula prediction method and system based on industrial vision and double-model fusion. The method comprises the following steps: acquiring visible light and near-infrared band images of the surface of an aluminum material coating through multispectral image acquisition, identifying a defect area to generate a binary mask, extracting three types of features of color, texture and spectral reflection, and respectively inputting feature vectors into a random forest regression model and a convolutional neural network model to obtain a target image; and dynamically calculating a fusion weight according to the verification set error, and carrying out weighted fusion on the prediction results of the two models to generate a formula component content prediction value. According to the method, the technical problems of low precision, low efficiency and insufficient generalization ability of a traditional aluminum coating formula determination method are solved, and rapid and accurate prediction of the formula is realized.

The invention belongs to the technical field of remote sensing image data processing, and relates to a multi-feature fusion coastal zone culture pond extraction method, which comprises the following steps: obtaining spectral features and polarization features based on an obtained Sentinel-1 image and an obtained Sentinel-2 image; calculating and evaluating an NDWI time sequence based on the NDWI to generate a time sequence synthesis NDWI image; a water body main body is obtained through the hierarchical feature fusion decision tree; obtaining morphological characteristics based on the water body object; obtaining the chlorophyll a concentration and the dynamic characteristic factor of the chlorophyll a concentration based on the Sentinel-2 image; and extracting a culture pond through a random forest classifier, and generating a culture pond spatial distribution diagram. According to the method, the spectral features, the polarization features, the morphological features, the chlorophyll a concentration and the chlorophyll a concentration dynamic feature factors are fused, and the decision tree and the random forest classifier are fused through the hierarchical features, so that the problems of low accuracy and poor stability of existing culture pond extraction are solved.

The invention relates to the technical field of equipment life prediction, and provides an equipment life prediction method based on a mixed attention enhancement time sequence convolutional network, which comprises the following steps of: preprocessing original test data, extracting 10 types of time domain statistical characteristics from the preprocessed original test data, screening high-importance feature data as model input through a random forest algorithm; a life prediction model is constructed, an encoder adopts a stacked expansion causal convolutional layer and a self-attention layer, and a decoder fuses historical features and exogenous variables through cross attention; training a life prediction model by using a mixed attention enhancement time sequence convolutional network, wherein a composite loss function synchronously optimizes point prediction and multi-quantile regression loss; and inputting sensor data collected in real time into the trained model, outputting a prediction result, and generating a 95% confidence interval based on nonparametric probability prediction. The overall reliability and accuracy of equipment life prediction can be effectively improved.

The invention discloses a layered cold storage monitoring system and method for a workshop intelligent water cold storage device. The system comprises a cold storage tank, a layered sensing module, a flow field adjusting module, a cooling capacity metering module, an edge control module and a cloud collaboration module. The layered sensing module collects temperature, density and turbidity parameters in a distributed mode along the inner wall of the cold storage tank, and the cooling capacity metering module collects flow and temperature difference parameters. The edge control module recognizes cold and hot water areas and thermocline features through gradient calculation and a threshold value judgment algorithm, and regulates and controls the opening degree of a water distributor and the position of a spoiler of the flow field regulation module; and the cloud collaboration module stores the data and optimizes the recognition model through a random forest algorithm. According to the method, closed-loop monitoring is realized through initialization, multi-parameter acquisition, layered identification, flow field adjustment and data uploading. Layered monitoring precision is improved, cold and hot water mixing is inhibited, workshop air conditioner requirements are met, and energy consumption and fault risks are reduced.

The invention belongs to the technical field of wind power prediction, and particularly relates to a single-fan ultra-short-term output prediction method based on mRMR and TabNet. The method comprises the steps of firstly obtaining historical operation data of a single-machine wind turbine generator; carrying out abnormal value identification and elimination on the original data through a random forest algorithm, and filling missing data by adopting linear interpolation; derived features are constructed on the basis; inputting all the candidate features into a maximum correlation and minimum redundancy feature screening module, and obtaining an optimal feature subset for ultra-short-term prediction according to the correlation between the features and output and the redundancy among the features; constructing a TabNet deep neural network prediction model by using the feature subset, and performing training by inputting single-machine operation features and taking historical ultra-short-term output as a supervision signal to obtain a single-fan ultra-short-term output prediction model; according to the method, the output prediction precision of the single wind turbine generator in a high-volatility scene can be effectively improved, and the interpretability and the feature utilization efficiency of the prediction model are improved.

The invention discloses a speech emotion recognition method and system based on multi-modal feature fusion, and relates to the technical field of speech emotion recognition. The speech emotion recognition method and system based on multi-modal feature fusion comprises the following steps: S1, collecting a speech emotion data set, and carrying out label unified coding and normalization processing; s2, frame-level acoustics and construction of frequency spectrum, rhythm and sound quality emotion features are carried out; s3, a speech emotion representation generation method fusing multi-sub-mode depth coding and a gating cooperative attention mechanism; s4, performing random forest weight initialization and two-order variation grey wolf mapping evaluation; and S5, voice emotion recognition and operation feedback adaptive updating are carried out. According to the method, the feature selection efficiency and the emotion classification accuracy in voice emotion recognition are effectively improved, and the problems that existing voice emotion feature selection is single in stage and single in index, emotion retention and real-time performance are difficult to consider while dimension reduction is performed, and the overall performance is limited are solved.

The invention discloses a satellite hyperspectral remote sensing image-based adaptive random forest land utilization classification and identification method, which comprises the following steps of: data preprocessing: loading a vector boundary of any predetermined administrative region, the method comprises the following steps: acquiring surface reflectance data of a certain satellite hyperspectral image in any preset time period from a natural resource satellite remote sensing cloud service platform, and preprocessing; making a sample set; parameter definition and remote sensing index selection: calculating a group of representative remote sensing indexes by using a specific wave band combination of the hyperspectral image; a remote sensing index and an original wave band are used as input feature vectors together to form a complete feature space, and in the training process of the random forest, part of features are randomly extracted from the feature space during construction of each decision tree for node division; performing cross validation and parameter tuning; the visualization of the optimal parameter result is selected; according to the invention, adaptive parameter tuning based on random forest algorithm classification is realized.

The invention relates to the technical field of unmanned aerial vehicle detection, in particular to a civil small unmanned aerial vehicle cat eye detection and countering system which comprises the steps that a cat eye detection module collects cat eye reflection characteristic signals, environment sensing data and unmanned aerial vehicle flight state associated information in an airspace; the threat level evaluation center identifies the type of the unmanned aerial vehicle and the intrusion risk level through an improved random forest model, and measures the real-time airspace position; the dynamic trajectory modeling module constructs a digital twin flight model of the civil small unmanned aerial vehicle, and simulates a flight trajectory and a potential intrusion path by using an intention pre-judgment algorithm; the grading countering unit generates a non-destructive countering strategy according to the intrusion risk grade, the flight path and the potential intrusion path; the man-machine interaction unit constructs a visual airspace monitoring scene, renders a detection result, an intrusion risk level and a countering effect in real time, and carries out man-machine interaction operation. Therefore, the problems of insufficient perception, poor decision collaboration and the like in the prior art are solved.

The invention belongs to the field of pollution traceability, and particularly relates to a karst landform soil and groundwater collaborative heavy metal pollution traceability method, which comprises the steps of end member site selection, groundwater monitoring point arrangement, collection and measurement of feature identification of each end member and multi-end member hybrid modeling. According to the scheme, the number of karst funnels is counted through neural network segmentation, high, medium and low density areas are divided, intelligent partition extraction of end members is achieved through a random forest model, monitoring points are arranged in the flow direction in a layered mode in combination with funnel distribution and an aquifer structure, and the problems that the pollution diffusion rule is difficult to capture and pollution source positioning is fuzzy under heterogeneity are solved; a three-dimensional monitoring network of soil, water quality and geology is constructed, a Bayesian mixture model is combined with an isotope fractionation effect to quantify an end member contribution proportion, the defect that traditional monitoring and modeling are not adaptive to karst heterogeneity is compensated, and pollution spatial and temporal distribution and migration paths are captured; unification of accurate simulation of the pollution diffusion rule and quantification of the end member contribution proportion is realized.

The invention discloses a design and maintenance system and method for an anti-icing and anti-icing scheme of a power transmission line in a low-temperature icy region, and aims to solve the problems of extensive design and passive operation and maintenance in the prior art. The system comprises a data acquisition module, an icing risk division module, an icing load engineering determination module, a differential design module, an online monitoring module and a maintenance decision module. According to the method, through multi-source data collection and preprocessing, a refined icing grade distribution diagram is generated in combination with GIS spatial analysis and a random forest model, the ice thickness is deduced and designed based on Gunn Bell distribution, the icing load is calculated, differential design schemes are generated for a pole tower and a foundation, a wire and a ground wire, an insulator and a fitting and power distribution equipment, and the method is applied to the field of power distribution. And closed-loop management is formed in cooperation with online monitoring and dynamic maintenance. The method realizes accurate identification of the icing risk, gives consideration to safety and economy, improves the ice-resistant toughness of a power grid, reduces the operation and maintenance cost, and is suitable for design and maintenance of power transmission lines in low-temperature icy areas such as northern and southwest plateau areas.