3060 results about "Random forest" patented technology

The present invention relates to the technical field of monitoring, in particular to a remote monitoring method and system for an aviation obstruction light. The method comprises the following steps: on the basis of an external sensor, acquiring electromagnetic signals sent by an aviation obstruction light; and by means of using a signal processing algorithm, processing the obtained original signals to eliminate noise interference and standardize the signal format, so as to generate signal-purified data. Using a support vector machine and a random forest algorithm in the present invention enhances the fault mode identification capability and the accuracy of predicting device performance degradation trends, and substantially improves the reliability of fault prediction; the combination of a Kalman filter and a multi-level decision tree provides powerful support for the integration and analysis of multi-source data, thereby ensuring the comprehensiveness and effectiveness of decision-making support information; and using a self-organizing map network and U matrix visualization technology not only shows advantages in the aspects of data mode identification and anomaly detection, but also improves the interpretability of data analysis by means of visual image displaying.

The invention relates to a part surface defect detection and process optimization method and system, and solves the problems that defect detection has defects, missing detection and erroneous judgment are easy to occur, and subsequent process improvement faces huge challenges even if defects are detected, and the method comprises the following steps: inputting a feature set into a double-branch fusion deep learning model, the first branch identifies defect types and quantization parameters by fusing three-dimensional features and two-dimensional features, and the second branch calculates the correlation degree between the defect features and each process through association rule mining and a random forest algorithm; when the three-dimensional features and the two-dimensional features both meet a preset defect threshold value and the association degree of a certain process exceeds a preset value, determining that the process is a root process; and analyzing a deviation value between the key parameter of the source process and the defect quantization parameter, and correcting the parameter through a dynamic adjustment mechanism according to the deviation degree. The method has the advantages that the defects of the part are accurately detected, the procedure is traced, parameters are dynamically adjusted, closed-loop optimization is formed, and the quality of the part is improved.

The invention discloses a multi-path recall mixed retrieval method and system based on intelligent dynamic weight distribution, and aims to solve the problems that semantic comprehension and keyword matching are difficult to balance and the adaptability is poor due to the adoption of a fixed weight in the existing retrieval technology. The invention provides a multi-path recall mechanism fusing vector semantic retrieval, BM25 keyword retrieval and entity retrieval. A query feature vector containing 13-dimensional features such as semantic complexity, keyword density and entity coverage rate is constructed, a query type is recognized in combination with an SVM and a random forest integration model, a dynamic weight distribution algorithm is designed, and the final weight of each retrieval path is calculated in real time. And an adaptive multi-source enhanced reciprocal ranking fusion (AMSE-RRF) algorithm is further adopted to carry out optimization fusion on multiple paths of results, and a depth reordering model can be selected to improve the precision. According to the method, the accuracy and robustness of retrieval can be remarkably improved in multiple scenes of medical treatment, finance, government affairs and the like according to a millisecond-level self-adaptive adjustment strategy of query features.

The present invention provides a tool wear state monitoring method and system based on multiple types of signals, and relates to the technical field of data processing. The method includes: obtaining data of a cutting force, an acoustic emission signal and a vibration signal, and extracting a plurality of statistical features from data of the cutting force and the acoustic emission signal; extracting a singularity feature from the vibration signal by combining a singularity analysis with a wavelet transform; building a tool wear state monitoring model based on a random forest, using an obtained feature to perform preliminary training, and outputting a wear prediction result; and based on the real-time data of the cutting force, the acoustic emission signal and the vibration signal, monitoring the wear state of the tool through the refined model.

The invention discloses a radioactive measurement data processing method for improving uranium exploration efficiency. The method comprises the following steps: standardized data acquisition: integrating a multi-parameter module, carrying out time-space synchronous acquisition of geological geophysical environment parameters, standardizing protocol alignment data, and supporting three-dimensional modeling; a three-dimensional coupling model is used for processing interference in a sub-module mode, LiDAR-DEM is used for correcting gamma attenuation in the terrain, an optical fiber thermopermeability instrument is used for correcting daughter errors in the hydrology, signal attenuation of a borehole is compensated through a drilling fluid chart, and a space-time continuous interference field is formed; performing dynamic equilibrium coefficient inversion: constructing equilibrium coefficient isoparametric mapping, and inputting rock core, logging and geochemical data into a random forest model; the transfer learning is trained by using historical data, and dynamic inversion and updating of a new area are carried out; intelligent data processing three-dimensional visualization: self-adaptive denoising and spectral shape matching reinforcement abnormity are carried out; constructing a three-dimensional model, and carrying out transfer learning to optimize the precision; the WebGL platform integrates multi-parameter display, virtual drilling functions and high-dimensional data intuitive interpretation.

The invention discloses a multi-stage scheduling architecture control method based on a TSN network. Efficient management of network resources is realized through modular cooperation. The time synchronization calibration module periodically calibrates the difference of multiple clock sources by using an improved network time protocol and jitter monitoring to ensure the time unification of the whole network; the priority analysis module divides data streams according to a reference value, and determines time slot distribution of key and non-key streams; and the traffic peak period scheduling module dynamically adjusts resources by using a time triggering algorithm to ensure key data transmission. Meanwhile, the real-time path optimization module deals with delay exceeding based on a Dijkstra algorithm, the intelligent load balancing module is combined with a random forest algorithm to deal with an overload problem, and the rapid fault switching and isolation module realizes rapid fault response through redundant paths and topology analysis. And iteratively adjusting the parameters according to the key indexes. According to the method, full-process closed-loop control from time reference unification to dynamic resource scheduling and fault processing is realized, and the transmission stability and the resource utilization rate of the TSN network are effectively improved.

The invention discloses a QoS guarantee method and system for a communication network, and relates to the technical field of communication networks, and the method comprises the steps: collecting and preprocessing network state data, and forming a standardized data set; dynamically classifying service types based on an improved random forest algorithm, and predicting a future QoS demand trend of each priority service in combination with an LSTM neural network; establishing a mapping model of QoS demands and resource parameters, converting predicted demands into allocable resource indexes, monitoring the resource utilization rate in real time, and setting an elastic reservation mechanism and conflict early warning; when early warning is triggered, selecting an optimal transmission link by adopting a multi-path collaborative algorithm, and implementing differentiated resource allocation according to service priorities; and a closed-loop feedback mechanism is triggered to dynamically adjust resource allocation by monitoring the deviation between the actual QoS and a predicted value in real time. The method has the advantages that through multi-dimensional perception, LSTM prediction, dynamic resource management and multi-path scheduling, QoS requirements of services with different priorities are accurately matched, and dynamic changes of the network are efficiently coped with.

The invention discloses an energy storage power station fire early warning method and system based on multi-parameter fusion, and the method comprises the following steps: collecting the temperature, characteristic gas concentration, cell expansion force, voltage fluctuation and environment temperature and humidity data of a lithium battery of an energy storage power station in real time through a distributed sensor, and carrying out the cleaning, denoising and standardization processing of the collected parameters, temperature and gas concentration monitoring values are corrected through an environment temperature and humidity compensation algorithm, abnormal data caused by environment interference are eliminated, the preprocessed data are input into a preset intelligent early warning module, and the model is based on a random forest algorithm. Through multi-parameter fusion and intelligent algorithm deep analysis, in combination with environment compensation, interference elimination, early warning accuracy improvement, graded early warning and linkage response, full-stage accurate disposal is achieved, timeliness is enhanced, sensor redundancy, multi-cabin cooperation and other mechanisms guarantee reliability, a closed loop from monitoring to disposal is formed, and the fire risk and loss are effectively reduced.

The invention provides a shield tunnel dynamic settlement compensation construction method based on an adaptive optimization algorithm, and the method comprises the steps: collecting the ground surface settlement, soil stress and underground water level data in real time through an Internet of Things sensor, achieving the data preprocessing and feature extraction in combination with an edge calculation node, constructing a three-dimensional geologic model, integrating the historical engineering data through transfer learning, and achieving the dynamic settlement compensation of a shield tunnel. The method comprises the following steps: identifying a high-risk area by using a clustering algorithm, designing a hybrid adaptive optimization framework with fusion of random forest and incremental learning, dynamically adjusting shield tunneling speed and soil bin pressure construction parameters, introducing an adaptive step length mechanism to cope with geological complexity change, and identifying a settlement abnormal mode through Fourier transform. Precise compensation is achieved in combination with a layered grouting strategy, the pressure of a soil bin is dynamically adjusted based on a hydraulic system, a closed-loop feedback mechanism is established, the predicted deviation rate is compared with an actual monitoring value, model parameters and the compensation strategy are continuously optimized, the settlement control precision is improved, and the construction risk is reduced.

The invention relates to a building module data interface intelligent monitoring system based on the Internet of Things and a deployment method, and belongs to the technical field of building information monitoring. The system is composed of a distributed sensing unit, an edge computing gateway, a cloud platform and a visual terminal, a self-adaptive filtering algorithm and an abnormal mode recognition model are built in the edge computing gateway, the cloud platform adopts a time sequence database to construct a multi-dimensional data warehouse, and structural health degree evaluation is carried out by fusing an LSTM neural network and a random forest algorithm. The deployment method comprises the steps of optimizing a sensor distribution strategy based on a BIM model, establishing a wireless Mesh ad hoc network communication architecture, and configuring a grading early warning mechanism and a fault tracing function. The innovation point is that a dynamic threshold adjustment algorithm and an interface performance degradation prediction model are provided, and real-time monitoring of the connection state of the building module and life prediction are realized. The system has the advantages of flexible deployment, high detection precision and low maintenance cost, and the intelligent level of building structure safety monitoring is effectively improved.

The invention relates to the technical field of photovoltaic systems, and particularly discloses a fault identification method and system for a photovoltaic system, and the method comprises the steps: collecting data in real time through environment, electrical parameters and an equipment state monitoring sensor, carrying out the cleaning and standardization, extracting time domain, frequency domain and time frequency features, and screening a feature subset through a correlation analysis and feature importance sorting algorithm; detecting and classifying faults by using a hybrid model composed of an isolated forest algorithm and a random forest classifier; positioning a fault subsystem and analyzing a root cause by means of a hierarchical diagnosis strategy, a graph neural network and a Bayesian reasoning algorithm; and periodically updating the model based on the new data. The method can quickly and accurately identify and position faults, adapts to a complex environment, reduces the operation and maintenance cost, and improves the operation reliability and operation and maintenance efficiency of a photovoltaic system.

The invention provides a Transform and CNN fused crack detection and structure evaluation system and method. The system comprises an image preprocessing module, a crack feature extraction module, a crack positioning and classification module, a crack boundary refinement module and a structure integrity evaluation module. Through combination of the CNN and the Vision Transform, local and global features in the image can be extracted at the same time, and the precision of crack detection is enhanced. The dynamic attention mechanism is used for refining fracture boundaries and improving fracture positioning and recognition effects. And the structure health assessment module combines crack information and structure stress analysis, performs structure risk assessment by using a support vector machine or a random forest, and outputs a structure health state and a repair suggestion. The invention further provides an evaluation scheme of the system. The method improves the precision and robustness of crack detection, has higher multi-scale detection capability, noise robustness and real-time performance, and is suitable for automatic monitoring and health management of civil infrastructures.

The invention discloses an employment information matching method and system based on data analysis, and particularly relates to the field of employment matching, and the method comprises the steps: collecting structured and unstructured data, and carrying out the semantic feature extraction through employing a BERT model and BiLSTM-CRF; in the preprocessing stage, entity standardization is realized through a knowledge graph, and a job seeker portrait and post model including a skill matrix and an occupational development trajectory is constructed; in the feature engineering stage, extracting four core features of skill matching degree, salary expectation integrating degree, commuting tolerance and occupational development goodness of fit; the salary integrating degree is quantitatively evaluated through a bidirectional tolerance model, a random forest model with time decay is used for dynamic weight, feature weight is generated based on historical successful cases, and a real-time feedback mechanism is introduced to adjust a weight coefficient; finally, the matching degree function fuses the weighted features and the industry trend factors, and the model is continuously optimized through a three-level updating mechanism.

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 provides a sea-crossing bridge deformation prediction method based on an STL-ARIMA-meteorological coupling model, and relates to the technical field of bridge monitoring, and the method comprises the steps: collecting deformation data and meteorological data of a sea-crossing bridge, and enabling the deformation data and the meteorological data to be completely aligned at a time scale; respectively constructing deformation characteristics and meteorological characteristics based on the deformation data and the meteorological data of the sea-crossing bridge; optimizing a seasonal period and a trend window of the STL algorithm based on the deformation data, and decomposing the cumulative settlement time sequence into a trend term, a seasonal term and a residual term; carrying out random forest regression modeling on a residual term obtained by STL decomposition and a meteorological factor to generate a meteorological influence component, and further calculating a meteorological correction residual; a trend term obtained through STL decomposition and a meteorological correction residual error are superposed to generate a trend-correction residual error, the trend-correction residual error serves as an ARIMAX modeling target, a search space is expanded through an autoarchia algorithm, and ARIMAX modeling is driven through meteorological characteristics; and fusing ARIMAX model output, historical season item copying and meteorological feature prediction results to realize multi-scale deformation reconstruction.

The invention relates to the technical field of protection device testing, in particular to a verification method of an intelligent comprehensive protection device, which comprises the following steps of: extracting starting time and response time in a tripping state transition sequence of the protection device, calculating execution delay and action segment length, and comparing and identifying an abnormal link based on a state number sequence; the method comprises the following steps: realizing analysis and standard reconstruction of a state logic structure, forming an ordered tripping path, introducing a long short-term memory neural network to carry out time alignment on a state change sequence and a current signal segment, identifying a potential mismatch or abnormal change trend in a state chain through linkage analysis of a state-current abrupt change position, and reconstructing a state path. According to the method, path continuity and dynamic adaptability are enhanced, a random forest is adopted to take device numbers, the number of jump segments and response residual errors as multiple inputs for training modeling, device scores are output through an integrated tree voting mechanism, and recognition sorting of abnormal frequency significant devices is achieved.

The invention discloses a remote sensing-based canopy scale urban green land vegetation classification method. The method comprises the following steps of: obtaining and fusing multi-source remote sensing data; constructing a canopy scale multi-dimensional feature set fused with multi-source remote sensing data; constructing an urban green land vegetation enhancement sample set; constructing a low-dimensional feature set after optimization; the invention further discloses an urban green land automatic classification method based on the canopy scale of the improved random forest. According to the method, a multi-dimensional feature set is constructed through multi-source high-resolution remote sensing data; an automatic dynamic feature optimization and weight distribution mechanism based on mRMR is introduced, redundancy is effectively reduced, and discriminative features are highlighted; constructing a heterogeneity-driven adaptive sample set in combination with multi-source prior knowledge; and based on an improved random forest algorithm which introduces dynamic weighted node splitting, neighborhood constraint and adaptive category balance, high-precision, canopy-scale and adaptive classification of urban green land vegetation is realized.

The invention relates to the technical field of power equipment state detection, in particular to an ultrahigh-frequency partial discharge online detection system, method and device and a medium, and the system comprises the steps: collecting an initial discharge signal in real time through an ultrahigh-frequency sensor array, and carrying out the preprocessing of the initial discharge signal, so as to obtain an ultrahigh-frequency discharge signal; carrying out peak detection on the ultrahigh-frequency discharge signal, and triggering a high-speed analog-to-digital converter to collect an original waveform when the amplitude exceeds a preset threshold value; performing multi-dimensional feature extraction on the original waveform by using a digital signal processor to obtain multiple groups of dimensional features; identifying and classifying the multiple groups of dimension features based on a random forest algorithm, generating discharge type labels and confidence coefficients, and storing the discharge type labels and the confidence coefficients in a dynamic database; carrying out spatial position calculation on the ultrahigh-frequency discharge signal by adopting a time difference method, and determining a three-dimensional coordinate of a discharge source; the dynamic database and the three-dimensional coordinates of the discharge source are subjected to space-time correlation and multi-dimensional analysis, a defect analysis result is generated, and high-precision online detection of the partial discharge defect of the high-voltage equipment is achieved.

A wellbore temperature optimization and predication method integrating numerical models and machine learning includes the following steps: establishing a wellbore-formation transient heat transfer model, obtaining an initial data set composed of relevant parameters, normalizing the initial data set, training a wellbore temperature prediction model by using a random forest algorithm, then optimizing the hyperparameters of the random forest algorithm by using a genetic algorithm, performing global optimization by using an annealing algorithm to obtain the optimized wellbore temperature and related parameters, calculating the wellbore temperature by substituting the optimized parameters into the wellbore-formation transient heat transfer model, and performing comparative verification on the optimized wellbore temperature and the calculated wellbore temperature.

The invention discloses a loess slope stability intelligent monitoring method and system, and the method comprises the steps: carrying out the regional differentiation modeling of multi-source data of a target region, and outputting slope change data; according to the period-by-period displacement time sequence data, a slope deformation trend is obtained through prediction based on an LSTM algorithm; and inputting the slope change data, the slope deformation trend and the meteorological rainfall data into a slope stability evaluation model to predict a safety coefficient, and if the safety coefficient is lower than a preset threshold value, issuing early warning information. According to the loess slope stability intelligent monitoring method and system provided by the invention, data such as gradient change and deformation trend are accurately extracted, multi-source data and meteorological rainfall data are fused by means of cross-modal attention, a random forest model is used for learning a multi-factor coupling rule, and finally multi-factor data prediction is integrated to obtain a safety coefficient for early warning. The problem of low early warning reliability caused by difficulty in accurately evaluating the slope stability by fusing multiple factors such as gradient, deformation and meteorological rainfall can be solved.

The invention relates to the technical field of electric power fault diagnosis, and discloses a transformer area line loss abnormity diagnosis method and system based on a large model, and the method comprises the steps: collecting multi-dimensional data used for supporting transformer area line loss abnormity diagnosis, carrying out the cleaning, correlation fusion and standardization processing of the multi-dimensional data, and obtaining a comprehensive data set; a multi-layer diagnosis system based on a rule model, a random forest model and a large model is constructed, and the rule model identifies the simple and conventional anomalies of the transformer area according to a preset anomaly diagnosis rule based on the basic attribute data and the power operation state data in the comprehensive data set; the random forest model locates complex anomalies and novel anomalies by mining a coupling relationship among energy access condition data, external environment influence data and line loss fluctuation in the comprehensive data set; the big model carries out fusion verification on diagnosis results of the rule model and the random forest model, and outputs a final transformer area abnormity diagnosis result; and based on the diagnosis result, a differential loss reduction strategy adaptive to the actual data characteristics of the transformer area is recommended. The working efficiency is improved.

The invention discloses a method for evaluating evolution characteristics and evolution driving factors of a space-time pattern of a park green land, and particularly relates to the technical field of crossing of smart cities and landscape ecology, and the method comprises the steps: obtaining multi-period remote sensing image data and planning maps, and constructing a multi-source database; interpreting the image based on a random forest supervised classification algorithm to generate a park green space distribution map, and optimizing the precision; adopting a landscape ecology theory to select a landscape pattern index to quantify space-time evolution characteristics; constructing a driving evaluation index system, and analyzing a driving mechanism by means of a multiple regression model and a space measurement model; and generating a three-level spatial pattern optimization strategy based on the result and outputting a visual decision map. According to the method, the spatial-temporal pattern evolution characteristics and driving factors of the urban park green land can be comprehensively analyzed, and a scientific basis and decision support are provided for planning, management and protection of the urban park green land.

The invention discloses an engineering safety early warning method and system based on artificial intelligence real-time risk identification, and relates to the technical field of engineering safety, and the method comprises the steps: collecting scattered engineering safety data from each engineering platform in batches, and carrying out the preprocessing of the data, and constructing a dynamic database; according to the engineering safety data collected in batches, an engineering safety knowledge graph is constructed, and different risk levels are preset according to engineering safety standards. According to the method, multi-source engineering safety data are integrated, dynamically changing risk characteristics are analyzed in real time by using an AI risk identification model, the hysteresis of traditional manual inspection and static analysis is overcome, a nonlinear relationship among the risk characteristics is captured by using a random forest model through integrated learning of multiple decision trees, and the risk characteristics are analyzed in real time. And the probability values of high, medium and low risk levels are output in combination with Softmax probability normalization, so that the evaluation precision is remarkably improved, the risk features are positioned, the scientificity of risk traceability is ensured, and data-driven decision support is provided for engineering safety management.

The invention relates to the technical field of power grid load prediction, in particular to a power grid load prediction and dynamic scheduling optimization method based on big data. Comprising the following steps: preprocessing power grid multi-source data; constructing an attention enhanced load prediction model, extracting time sequence load features through an LSTM network, and screening key influence factor features through a random forest; generating a layered constraint improved whale optimal scheduling scheme; real-time closed-loop adjustment is carried out; and data archiving and tracing. According to the method, the attention enhanced load prediction model is constructed, the time sequence load features are extracted by using the LSTM network, the key influence factor features are screened by using the random forest, the attention mechanism is introduced to highlight the power consumption peak period feature weight, and the Adam optimizer training and precision verification are combined, so that the load change rules of different periods can be more accurately captured, and the load prediction accuracy is improved. And the adaptability of the load prediction result and the actual power grid operation condition is improved.

The invention discloses a method and a system for monitoring the temperature of an energy storage battery in a high-altitude area. Through environment adaptive feature reconstruction, thermodynamic model correction and multi-frequency EIS fusion feature extraction are utilized to generate a feature vector adaptive to the plateau environment. The thermodynamic model correction adjusts the convective heat transfer coefficient by introducing an air pressure correction coefficient; the multi-frequency EIS fusion feature selects a specific frequency point impedance value to construct a vector, and the weight is dynamically adjusted through a random forest algorithm. The lightweight hybrid machine learning model integrates the advantages of LightGBM, 1D-CNN and a physical constraint particle filter model, and realizes accurate prediction for different working conditions. The plateau exclusive training mechanism covers data enhancement, transfer learning and online calibration, and the generalization ability and adaptability of the model are improved. According to the method, the problems of low temperature monitoring precision and poor model adaptability of the energy storage battery in the high-altitude area are solved, the monitoring precision and the system reliability are remarkably improved, and a guarantee is provided for safe and stable operation of a high-altitude energy storage system.

The invention relates to the technical field of data processing, and discloses an intelligent monitoring method and system for cyanobacterial bloom outbreak. The method comprises the following steps: collecting a water surface spectrum and underwater particle size data, carrying out atmospheric correction, calculating a normalized algae index and a blue-green wave band ratio, inputting the normalized algae index and the blue-green wave band ratio into a U-Net network to obtain a water bloom coverage area, carrying out integral interpolation on the particle size data to obtain a vertical section distribution curve, and calculating a surface layer enrichment degree and a floating trend index, and establishing a water surface-underwater association relationship through random forest regression training, and inputting the multi-dimensional features into a CNN-LSTM model to predict a water bloom outbreak probability and determine an early warning level. According to the method, the problem that the cyanobacterial bloom three-dimensional structure cannot be comprehensively described due to the lack of effective fusion of the water surface spectral data and the underwater vertical section data is solved, the problem that the early warning timeliness of cyanobacterial bloom outbreak is insufficient due to the lack of a multi-source data time sequence analysis model is solved, and the spatial integrity and early warning advance of cyanobacterial bloom monitoring are improved.

The invention discloses a distribution network fault disaster damage analysis and intelligent disposal decision-making system based on big data and artificial intelligence, and relates to the technical field of power system distribution network fault processing. According to the system, multi-source heterogeneous data is integrated through the data intelligent acquisition module, main and distribution network topology connection is realized, and accurate fault identification and positioning, influence range evaluation and economic loss quantification are realized by using the disaster damage analysis module in combination with algorithms such as random forest, CNN and graph convolutional neural network. And constructing a closed-loop management system and generating an optimal disposal strategy and preventive maintenance suggestions based on the intelligent decision-making module. According to the method, the problems of lagging disaster damage assessment, insufficient positioning precision, dependence on artificial experience on decision making and the like in traditional distribution network fault processing are solved, rapid and accurate fault positioning, disaster damage dynamic assessment and intelligent decision making support are realized, the fault response efficiency and the power supply reliability are remarkably improved, and distribution network operation and maintenance are promoted to be transformed to an active defense and intelligent decision making mode.

The invention relates to the technical field of medical health and artificial intelligence, in particular to an old people common disease occurrence and development risk prediction method based on integrated machine learning, which comprises the steps of constructing a standardized data set, screening key variables, training a base learner, combining prediction results, dynamically evaluating risks and the like. According to the method, multi-dimensional data features are integrated, a prediction model is constructed by using algorithms such as a random forest and a support vector machine, model parameters are optimized in combination with a verification set, and a high-precision co-disease risk prediction result is finally output. According to the invention, accurate assessment of the co-illness risk of the old people can be realized, and a scientific basis is provided for personalized health management.

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 peanut disease intelligent monitoring scheme design, in particular to a peanut disease intelligent monitoring method and system and electronic equipment. RGB and near-infrared images are synchronously acquired through a dual-channel acquisition device, after illumination compensation, defogging and geometric correction, multi-scale features are extracted by using a transfer learning optimized Eff cientNet-B4 network, and spectral information is fused by using a dual-path CBAM attention mechanism to generate a disease sensitive feature vector. The cascade classifier realizes disease type identification and severity grading based on ResNet-34 and a random forest model, and predicts a disease development trend in combination with an LSTM time sequence model. And integrating a U-Net segmentation network to generate a visual report, and associating an expert knowledge base to output a prevention and treatment scheme. According to the method, the problems of low detection precision, poor model generalization ability and insufficient decision support of a traditional method are solved, and an efficient and accurate disease management tool is provided for peanut planting.