4915 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 provides an equipment supervision data real-time analysis method and system, and the method comprises the steps: calculating an energy value of a frequency band corresponding to a sub-band signal, determining the energy characteristics of the surface roughness and dust deposition distribution uniformity of a blade in each sub-band in a square sum form, and generating energy distribution vectors which are arranged according to a frequency band sequence; performing time-frequency analysis on the vibration signal by using the acquired real-time rotating speed information of the gas turbine, identifying a frequency component corresponding to the eccentric distance value of the rotor and the vibration frequency offset, and determining the amplitude of the frequency component; according to the classification result and the real-time rotating speed information, the comprehensive influence index of the material density change rate and the ash deposition layer thickness difference is calculated, the fault early warning level is determined through a weighted average method, and an early warning signal is generated; and according to the change trend of the early warning signal and the blade edge wear degree, a random forest algorithm is adopted to predict a development curve of the vibration frequency offset in the future target time, and real-time equipment supervision analysis data are obtained.

The invention relates to the technical field of data processing, in particular to an intelligent analysis system for electric energy quality and read data, which comprises a data fusion module, a modeling module, an analysis module, a detection module, an optimization module and a control module. The data fusion module aligns data of an electric meter terminal, power grid monitoring equipment and an environment sensor through a sliding time window, and the analysis module calculates a power grid node loss transfer coefficient based on a graph neural network and fuses transformer no-load loss and line contact resistance parameters to generate a dynamic line loss evaluation matrix. And the detection module identifies the power consumption characteristic deviation degree through a random forest classifier, and generates a priority management strategy in combination with a multi-dimensional abnormal scoring model. The control module adaptively selects a communication protocol to execute a regulation and control instruction according to a network state, a heartbeat detection mechanism feeds back operation data of a governance device in real time, model parameters are driven to be iteratively updated, and dynamic cooperation of power supply quality optimization and line loss governance is achieved.

A layout optimization method of water quality monitoring points based on a RF-C-SOM clustering algorithm includes: preprocessing collected water quality data to obtain preprocessed water quality data that are used as data, and using water quality categories as labels to train a random forest model to determine feature importance of water quality indicators; selecting important features based on the feature importance and model training accuracy, performing dimensionality reduction on the preprocessed water quality data to obtain dimension-reduced data; performing a fuzzy clustering on the dimension-reduced data to obtain a water quality section classification result, and based on it, determining initial weight values of a self-organizing mapping algorithm; initializing neurons and training a self-organizing mapping network model with the initial weight values; obtaining a point clustering result through the self-organizing mapping network model; and conducting a water quality index evaluation for the point clustering result before and after screening.

The invention relates to the technical field of health monitoring, in particular to a cardiovascular disease data fusion and multi-dimensional health assessment method, which comprises the following steps of: acquiring multi-source data of a patient through a sensor and wearable equipment, preprocessing the acquired data, ensuring data integrity, extracting physiological features by adopting a convolutional neural network, and evaluating the health of the patient. The method comprises the steps of extracting non-physiological data features through principal component analysis, constructing a multi-dimensional feature set, identifying cardiovascular disease related risk factors through a random forest algorithm, distributing weights through a logistic regression algorithm to generate individualized risk scores, analyzing time sequence trends of the risk factors based on a long and short-term memory network model, and generating dynamic risk scores in real time. When the score exceeds a preset threshold value, an early warning mechanism is automatically triggered, and a patient and medical staff are notified through an application program; according to the method, accurate assessment and personalized intervention of cardiovascular health are realized through multi-source data fusion and dynamic analysis, and the method has high efficiency and timeliness.

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 low-altitude unmanned aerial vehicle flight statistical system, and relates to the technical field of air traffic management. A flight statistical system of a low-altitude unmanned aerial vehicle comprises a multi-source sensing fusion module, a twin airspace modeling module, a behavior portrait modeling module, a non-cooperative target recognition module, a flight statistical analysis module and a self-adaptive scheduling module. According to the invention, a three-dimensional twin airspace model of a monitoring area is constructed based on high-precision map data, a building three-dimensional model and flight information, and space mapping and collision risk analysis of a flight path are carried out, so that real restoration of the flight path and environment collision prediction are realized; the behavior feature vector is generated and the random forest algorithm is adopted to carry out classification modeling, and the behavior portrait of the unmanned aerial vehicle is constructed, so that accurate identification and classification of the behavior pattern of the unmanned aerial vehicle are realized.

The invention relates to the technical field of computers, and discloses a cultivated land resource quality grade evaluation management method and system, and the method comprises the steps: carrying out the spectrum residual error normalization processing of a multi-temporal remote sensing image, generating a corrected image sequence, effectively inhibiting the reflectivity difference caused by the alternation of wetting and drying of a water body, and achieving the evaluation of the quality grade of cultivated land. A first boundary result and a second boundary result are respectively extracted by using depth segmentation and progressive edge detection, the adaptability and robustness of boundary extraction are enhanced, a difference graph is further generated based on a complex phase difference matrix, the second boundary is corrected, a coherent boundary result is obtained, the boundary time sequence consistency is remarkably improved, and the robustness of boundary extraction is improved. And finally, the cultivated land parcel units are constructed based on the coherent boundaries, and the random forest model is input to complete scoring and grading, so that the stability and traceability of the cultivated land quality evaluation result under multiple temporal conditions are ensured, and the technical targets of stably extracting the boundaries and accurately completing grading in a dynamic change scene are achieved.

The invention discloses a geological disaster early warning method and system based on big data analysis, and particularly relates to the technical field of geological disaster early warning. Geological deformation time series data of a target area are obtained through satellite remote sensing monitoring, feature importance is evaluated through a random forest algorithm, and regional geological risk grades are divided; meanwhile, wavelet transformation and long-short-term memory network modeling are carried out on rainfall spatio-temporal data of the meteorological monitoring station, and the potential influence of rainfall is evaluated; high-risk areas are screened, an underground water level change rate and rock-soil body strength correlation model is constructed, and a support vector machine algorithm is adopted to evaluate the landslide probability; and finally, analyzing a geological risk grading result, the potential influence degree of rainfall on the target area and the landslide occurrence probability by adopting an analytic hierarchy process, calculating a comprehensive risk index, comparing the comprehensive risk index with a comprehensive risk threshold value, and judging whether to trigger grading early warning, thereby realizing data fusion and intelligent analysis, early warning in advance, and improving the early warning efficiency. And effective help is provided for disaster prevention and reduction.

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 provides a road construction project carbon emission ratio toughness analysis method, and belongs to the technical field of carbon emission, machine learning and big data analysis and mining, and the method comprises the steps: obtaining a historical change trend of a carbon emission ratio from historical data of a road construction project, and constructing a dynamic change model of the carbon emission ratio in combination with real-time monitoring data; determining the influence degree and the action mechanism of the interference factors on the carbon emission ratio dynamic change model by analyzing the interaction relationship between the carbon emission ratio and the interference factors; key fluctuation characteristics are extracted from the corrected change trend of the carbon emission ratio, and whether fluctuation of the carbon emission ratio exceeds an acceptable range or not is judged in combination with a preset threshold value; if the fluctuation of the carbon emission ratio exceeds a preset threshold value, performing noise reduction processing on the data by adopting an adaptive filtering algorithm to generate noise-reduced carbon emission ratio data; and training the segmented analysis result by adopting a random forest model in a machine learning algorithm to generate a prediction model of the carbon emission ratio.

The invention relates to the technical field of 5G networks, in particular to a 5G network intelligent optimization method and system, and the method comprises the steps: obtaining 5G network signal data, recognizing an abnormal power spectral density region, carrying out the filtering extraction of the abnormal power spectral density region, and separating interference signals, interference types including narrowband interference, broadband interference and directional interference; extracting features from the obtained interference signals, and performing interference type identification according to a random forest algorithm; starting a corresponding anti-interference means according to the interference type, and generating operation state data in real time; a bee colony algorithm is adopted to simulate bee behaviors for iterative search, and a local optimal resource scheduling strategy is determined; and executing the resource scheduling strategy, feeding back an execution effect, and restarting the bee colony algorithm to determine a new resource scheduling strategy if the execution effect does not reach a set expectation. Therefore, the problems of lack of dynamic adaptive adjustment, single anti-interference means, lack of cross-base station collaboration and the like in the anti-interference aspect in the prior art are solved.

The invention relates to the technical field of fault diagnosis, in particular to an AI-based power system fault diagnosis and early warning system, which comprises a data acquisition module, a data processing module, a fault mode identification module, a fault diagnosis module, an early warning module and a decision support module, multi-source data related to power system fault diagnosis are collected in real time, the data processing module carries out preprocessing and feature extraction on the collected data, and the fault mode recognition module constructs a fault mode recognition model based on a long short-term memory network, a random forest and a convolutional neural network and recognizes a fault mode. The fault diagnosis module carries out fault diagnosis based on real-time data, the early warning module carries out early warning on possible faults in advance according to a diagnosis result, and the decision support module generates an emergency scheduling scheme according to fault diagnosis and early warning information. And the stability and safety of the power system are improved.

The invention provides a deep learning-based rapid ore sorting method under adaptive illumination intensity, and relates to the technical field of ore sorting, the deep learning-based rapid ore sorting method comprises the following steps: obtaining illumination intensity distribution information and ore three-dimensional point cloud data, and calculating ore space orientation parameters based on quaternion attitude estimation; inputting the obtained data into a layered illumination compensation network, and obtaining an illumination normalized enhanced image through adaptive brightness mapping and local light spot correction in combination with reflectivity recovery and non-local mean filtering; inputting the illumination normalized enhanced image and the ore three-dimensional point cloud data into a multi-branch feature extraction network, constructing feature high-order association, and adopting adaptive feature reconstruction and conditional random field optimization features; and inputting the reconstructed features into a classification model based on active learning, optimizing a feature manifold space by using a self-organizing mapping network, carrying out sample labeling in combination with active sampling of adaptive entropy threshold screening and uncertainty perception, and realizing ore classification and sorting through random forest integration.

A forest point cloud reconstruction method and system based on combination of a handheld radar and an unmanned aerial vehicle, relating to the technical field of remote sensing. The method comprises: on the basis of an unmanned aerial vehicle and a handheld radar device, using a multi-band radar sensor to perform forest terrain and vegetation information acquisition to generate original radar data. The use of the multi-band radar sensor improves the data acquisition accuracy and the coverage range, so that forest terrain and vegetation information is recorded more comprehensively and accurately; a generative adversarial network improves the data quality, and reduces the noise, thereby ensuring the accuracy of subsequent analysis; a convolutional neural network and a random forest algorithm are combined to improve the accuracy of vegetation classification and terrain feature recognition; the generation of comprehensive environmental feature data integrates multidimensional environmental parameters, providing richer information for in-depth analysis; a three-dimensional point cloud model visually displays a forest structure; and time series analysis and a long short-term memory network improve the accuracy of forest change trend prediction, and provide decision support for forest management strategies.

The invention relates to the technical field of power systems, in particular to a multi-dimensional network performance index evaluation system and method based on measured data of a new energy station. Comprising a data acquisition preprocessing unit; a multi-dimensional index construction unit; the weight adjustment unit dynamically adjusts the index weight according to the operation condition based on a fusion algorithm of an improved analytic hierarchy process and an entropy weight method; the performance evaluation model unit adopts a comprehensive evaluation model fused with a random forest algorithm to generate multi-dimensional performance scores and risk early warning; and the visual interaction terminal unit is used for displaying the evaluation result in real time and generating a trend analysis report. According to the invention, the three-dimensional evaluation system covering the static electrical parameters, the dynamic stability indexes and the harmonic pollution indexes is constructed, so that the limitation of single-dimensional evaluation in the prior art is broken through, and the extension of the network-related performance of the new energy station from local detection to full-dimensional coverage is realized; and the multi-scene evaluation requirements of the power grid on the steady-state operation, the transient response and the electric energy quality of the station are accurately matched.

The invention belongs to the technical field of intelligent equipment fault diagnosis, and particularly relates to a motor fault real-time diagnosis method and system based on LSTM and random forest. The method comprises the steps that a vibration signal, a current signal and a temperature signal of a motor are collected and preprocessed; performing feature engineering, extracting time domain, frequency domain, time-frequency domain and cross-modal correlation features, and determining an optimal static feature subset through a hybrid screening strategy; constructing a hybrid fault prediction model comprising a random forest model and an LSTM sequential network model; fusing prediction results of the two models by adopting a dynamic credibility weighted fusion mechanism; and performing real-time decision and hierarchical feedback control based on a fusion result. According to the method, advantages of multi-source information and the model are fused, the classification precision of the lag type is remarkably improved, real-time fault diagnosis and active protection are realized, the equipment maintenance cost is reduced, and the method is suitable for motor health management of intelligent agricultural equipment such as mowers.

The invention discloses a landslide susceptibility assessment method and system fusing feature interaction and dynamic weighting, and belongs to the field of geological disaster assessment, and the method comprises the steps: building an evaluation index system, and carrying out the factor classification; acquiring landslide and non-landslide point sample data, evaluating factor importance by adopting a random forest to obtain a preliminary weight, and preliminarily evaluating the susceptibility based on an I-RF model; detecting double-factor interaction by using a geographic detector, screening interaction factors for collaborative enhancement, and optimizing an expansion index system; retraining the random forest model, obtaining a new weight containing a single factor and an interaction factor, and constructing an I-RF optimization model; for spatial heterogeneity, sub-regions are divided by using spatial clustering, and the weight of each sub-region is dynamically corrected according to the sensitivity of a main control factor, so that self-adaptive adjustment is realized; and calculating susceptibility indexes of the sub-regions and the whole region, and grading to form spatialized and multi-level risk partitions. According to the method, the scientificity and accuracy of evaluation are effectively improved through feature interaction optimization and partition dynamic weighting.

The invention discloses a remote printer monitoring and fault prediction system based on the Internet of Things, and relates to the technical field of the Internet of Things and artificial intelligence, and the system comprises a data collection module which is used for collecting equipment state data of a remote printer in real time and carrying out the preprocessing of the equipment state data; the data processing module is used for integrating the preprocessed equipment state data into a data matrix and a label vector; the model construction module is used for outputting a final feature set by using a random forest model and combining a Gumbel-Sigmoid activation function; and the fault prediction module is used for constructing a fault prediction model by using the bidirectional LSTM to output the predicted fault probability of the current time point and dividing a fault interval data pool. By introducing the combination of a random forest algorithm and a Gumbel-Sigmoid activation function, a feature selection process can be automatically optimized, a feature set which is crucial to fault prediction can be accurately screened out, and a bidirectional LSTM model is adopted to capture time sequence information, so that the fault prediction precision and the real-time response capability are remarkably improved.

The invention belongs to the technical field of intelligent agriculture, and discloses a canopy-atmospheric environment information-based soil water content prediction method and system, and the method comprises the steps: setting a canopy and atmospheric environment monitoring unit in a field, and synchronously collecting the canopy temperature, wind speed, relative humidity, and six-dimensional environment parameters of atmospheric temperature, wind speed and relative humidity; the crown temperature difference is calculated after preprocessing; the method comprises the following steps: constructing a training set by utilizing measured data of soil relative water content, constructing a soil water content prediction model by adopting a random forest algorithm, screening and confirming an optimal prediction model through grid search and cross validation, and screening key features by combining an SHAP value; the water demand is automatically calculated according to a pre-established crop whole-growth-period drought stress threshold table and a dynamic irrigation decision formula, and meanwhile, the adaptation of a general model to a regional specific model is realized by constructing an environmental parameter-actually measured water content database and transfer learning, so that the soil water content state is accurately predicted, and the soil quality is improved. And a scientific basis is provided for precise irrigation.

The invention relates to the technical field of power grid energy storage systems, in particular to a battery intelligent fault diagnosis method and system suitable for a power grid energy storage system. According to the method, multi-source monitoring data including battery electrical parameters, power grid features, environmental conditions and historical operation records are obtained, unified time step alignment and feature extraction processing are carried out on the data, and statistical features, time sequence features and frequency domain features are obtained. A double-layer diagnosis model structure is adopted, a random forest model is used for diagnosing main fault modes such as over-charging and over-discharging, and a convolutional neural network model is used for diagnosing complex fault modes such as short circuit and temperature out-of-control. The system has a dynamic updating mechanism, and online optimization is realized by calculating incremental updating values of model parameters; meanwhile, a model evaluation mechanism is established, and when an evaluation index is lower than a preset standard, a retraining process is triggered. According to the invention, high-precision and real-time identification and early warning of the battery fault are realized, and the operation reliability and safety of the energy storage system are significantly improved.

The invention provides an ocean net cage unmanned ship intelligent inspection method based on multi-source data and environment perception, which comprises the following steps: collecting and preprocessing multi-source ocean environment data of an ocean net cage group area, and simultaneously obtaining net cage distribution information, net cage inspection task requirements and an initial state of each unmanned ship; predicting the number of unmanned ships by using an improved random forest model; adopting an improved genetic algorithm to generate a plurality of optimal inspection paths; intelligently distributing a plurality of optimal routing inspection paths to each unmanned ship, and performing routing inspection on each unmanned ship according to the distributed path; multi-source marine environment data collected by a fixed monitoring station deployed in a net cage group area and a sensor of the unmanned ship can be comprehensively utilized, unmanned ship inspection path pre-planning, intelligent task allocation and dynamic adjustment of the inspection process are achieved, and therefore the inspection efficiency is improved, inspection safety is guaranteed, resource allocation is optimized, and the cost is reduced. And the intelligent level of mariculture management is improved.

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 an intelligent detection method for product defects on an automatic production line and a detection system based on machine vision, and relates to the technical field of industrial product quality detection. According to the method, a multi-waveband imaging technology is combined with temperature and chemical component information to generate a multi-dimensional feature data set, and an unsupervised learning algorithm is utilized to perform clustering analysis, so that the limitation of traditional single-waveband imaging is broken through, product features are comprehensively captured, the defect detection range and accuracy are remarkably improved, and a foundation is laid for subsequent analysis and classification; further utilizing an attention mechanism and a deep learning technology to accurately position and classify defects, and triggering deep scanning through a priority index to improve the detection precision and the system adaptability; and finally, combining with a random forest algorithm to analyze defect influence, and feeding back and optimizing production parameters through a dynamic adjustment mechanism, thereby realizing production optimization closed-loop management, and improving production efficiency and product quality detection.

The invention discloses a farmland abandoned land identification method based on multi-source remote sensing data and time sequence correction. The method comprises the following steps: 1, data acquisition and preprocessing; 2, extracting a key phenological period; 3, constructing a feature library; 4, training and predicting a random forest model; and 5, post-processing and precision evaluation are carried out. The method comprises the following steps: firstly, integrating multi-source remote sensing image data, and strictly preprocessing the multi-source remote sensing image data to ensure the time-space consistency of the data; and accurately extracting key phenological period information by deeply analyzing the minimum vegetative period and the vegetative peak period of the vegetation through NDVI and NDSI time sequences. In the feature construction stage, multiple spectral indexes are fused, principal component analysis and feature importance screening are applied, feature dimensions are optimized, and model performance is improved. A random forest model is adopted for training and prediction, and a high-precision abandoned land spatial distribution diagram is finally generated by combining an oversampling technology and a category weight adjustment technology.

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 provides a power transmission corridor forest fire spreading prediction and risk assessment method based on a satellite internet and a related device, and relates to the technical field of disaster prevention and early warning of a power system. Building a fire risk factor database based on multi-modal data of a satellite-ground fusion network mode; applying a random forest algorithm to screen out key factors and determining fire risk contribution degrees corresponding to the key factors; based on the power forest fire hidden danger factors, similar features of a power transmission corridor neighborhood are extracted, and sub-regions are divided by adopting a clustering algorithm; constructing a fire risk weighted scoring model to identify high, medium and low risk areas; a mountain fire spreading model is constructed based on meteorological observation data driving, a fire risk state transition matrix is established, and a fire spreading path is predicted in real time in combination with a dynamic Bayesian network; evaluating the damage risk of the forest fire to the power transmission line, and generating a damage risk grading graph of the power transmission corridor. The technical effects of dynamic assessment of the forest fire risk and accurate prediction of the spreading trend are achieved.

The invention relates to the technical field of defect detection, in particular to a suturing scalpel packaging quality detection method based on optic nerves, which comprises the following steps: collecting suturing scalpel packaging images to perform angular point detection and gradient screening, obtaining alignment parameters under a unified coordinate system, calculating an optical flow displacement initial value and a residual error, and calculating an optical flow displacement initial value; an ROI sub-image is cut to extract a cutting edge contour; an orthogonal projection length is calculated to generate a cutter form mark; texture and shape features are extracted and input into a support vector machine for defect identification; a random forest regression model is called to calculate compensation parameters; according to the method, coordinate mapping is established through angular point detection and gradient screening, displacement is corrected through combination of optical flow residual and depth tracking, a cutting edge contour is extracted through ROI cutting, shape deviation is quantized through orthogonal projection, a classification basis is constructed through texture fusion, defects are identified through a support vector machine, parameters are compensated through random forest regression, and performance is optimized through closed-loop feedback.

The invention relates to the technical field of wetland damage identification, in particular to a coastal wetland damage dynamic identification method and system based on a remote sensing technology, and provides the following scheme: calling multispectral remote sensing data through a cloud computing platform, performing cloud masking, atmospheric correction and resolution resampling, and generating a high-quality remote sensing image data set; collecting ground feature samples, and constructing a random forest classification model based on spectrums, vegetation indexes, water body indexes and texture features; applying the classification model to a remote sensing image, generating a coastal wetland classification chart, and distinguishing a natural wetland from a damaged area; through time sequence analysis, ground feature changes of the same spatial position are identified, and dynamic evolution characteristics, including change amplitude, rate and conversion relation, of the damaged area of the wetland are extracted; and evaluating the precision of the classification model by using the confusion matrix, and optimizing the model according to a verification result. According to the invention, automation and precision of dynamic monitoring of wetland damage are improved, and scientific support is provided for wetland protection and management.