468 results about "Gradient boosting decision tree" patented technology

The invention discloses an electric energy metering box fault prediction method and system based on big data analysis, relates to the technical field of smart power grids, and solves the problems of progressive aging missing detection and instantaneous interference misjudgment caused by dependence on single parameter threshold alarm and fault positioning misalignment caused by multi-source data isolated analysis in the prior art. According to the scheme, electrical, environment and equipment state parameters are collected in real time through a multi-dimensional sensing network; the error drift of the mutual inductor is dynamically predicted based on an LSTM-Kalman filtering model, and core breakdown early warning is realized in combination with wavelet transform; outputting a corrected resistance value and a fault mark by using a BP neural network; predicting the life of the piezoresistor by adopting a gradient boosting decision tree and fusing lightning overvoltage characteristics; the transient interference is suppressed through the combination of a Transform self-attention mechanism and dynamic time warping; according to the method, the aging detection precision and the complex environment adaptability are remarkably improved, the misjudgment rate is reduced, and the multi-fault associated positioning and active defense capability is realized.

The invention relates to the field of power equipment control data processing, in particular to a boiler combustion optimization control method based on data driving, which comprises the following steps of: acquiring multi-source data such as temperature field distribution, air and smoke pressure, smoke components and coal quality characteristics, and eliminating noise interference by adopting sliding window mean filtering; generating a standardized feature matrix in combination with principal component analysis and a dynamic time warping algorithm; constructing a dynamic coupling model fusing a gradient boosting decision tree and a long short-term memory network, analyzing a nonlinear relationship between pulverized coal particle size distribution and a wind-coal ratio, and predicting combustion efficiency, pollutant concentration and temperature field uniformity; and model parameter self-correction and weight dynamic adjustment are triggered through actual combustion data feedback, and a closed-loop control link is formed. According to the method, accurate modeling of the multi-physical field coupling characteristic of the combustion system is achieved, the time sequence generalization ability under the dynamic working condition is improved, and the purposes of heat efficiency improvement and pollutant emission reduction are effectively balanced.

The invention relates to the technical field of intelligent control of transformer substations, in particular to a standardized intelligent control system of a transformer substation, which realizes real-time response to the running state of equipment by acquiring the current, voltage, power factor, temperature and humidity, operation state, insulation state and environment temperature and humidity of a power transformation unit in real time. A gradient boosting decision tree is combined with a variational auto-encoder and a self-attention mechanism, state vector features are extracted, complex abnormal states are accurately recognized, potential faults are detected in an early stage, and the monitoring efficiency and the early warning precision are greatly improved. The future power demand is accurately predicted by combining the long-short-term memory network model with historical and real-time load data, and the limitation of traditional static load management is overcome. Based on the predicted power demand, the reinforcement learning model is adopted to dynamically adjust the operation number of the power transformation units, optimal configuration of resources is achieved while the demand is met, and the overall operation efficiency of the transformer substation is remarkably improved.

The invention relates to the technical field of tea leaf detection, and discloses a tea leaf quality grade detection method and system.The method comprises the steps that a tea leaf sample is collected and pretreated; extracting chemical component indexes and physical properties of the pretreated tea leaves, and performing fusion and analysis; and establishing a tea quality grade classification model, inputting characteristics of a tea sample to be detected, outputting a detection result, and generating a tea quality grade report. Through combination of the support vector machine, the gradient boosting decision tree and the improved logistic regression algorithm, the precision of the tea quality classification model is optimized. The dynamic threshold function and the segmented excitation function are introduced to further solve the boundary fuzzy problem of the high-grade tea leaves and the low-grade tea leaves, and accurate judgment of tea leaf grade classification is achieved. And data restoration is carried out through weighted compensation correction and a recheck process, so that the output tea grade report is ensured to have high accuracy and reliability.

The invention discloses a supply chain intelligent tracking system oriented to process monitoring, relates to the technical field of supply chain intelligent management, and is used for solving the problems of flexibility and anti-risk capability of supply chain breakpoint management. Comprising a supply chain data processing module, an energy transmission analysis module, a breakpoint prediction module and a supply chain adjustment module. According to the method, original data are collected from supply chain nodes, effective features are generated in combination with a time sequence, classification features and a high-dimensional data processing technology, a breakpoint prediction model is constructed through a gradient lifting decision tree, breakpoint risks are identified and evaluated, a supply chain network topology structure is constructed, and propagation paths and influence ranges of the breakpoint risks are calculated recursively. Determining an affected node set, formulating an adjustment and recovery strategy in combination with a prediction result, optimizing resource allocation and logistics routes, quickly recovering key nodes, ensuring continuity of a supply chain, and upgrading an early warning mechanism to dynamically monitor risks, thereby improving management flexibility and anti-risk capability of the supply chain.

The invention provides a casting system temperature intelligent control method which comprises the following steps: acquiring a casting mold characteristic area, and setting a thermocouple in the mold characteristic area based on casting structure characteristics; a random forest model is established according to thermocouple temperature measurement results and casting quality detection results, recursive feature elimination is carried out, correlation between thermocouple temperature measurement data and casting quality is discriminated, the number of thermocouples is optimized, and the thermocouples are screened; and analyzing according to the temperature data of the thermocouple temperature measuring points, the cooling process parameters and the corresponding casting quality, constructing the relationship among the cooling process parameters, the initial temperature of each mold thermocouple and the casting quality through a gradient lifting decision tree model, and controlling the temperature of the casting system based on the relationship. By arranging the thermocouple and based on the random forest model and the gradient boosting decision tree model, the temperature control accuracy is improved, the temperature is adjusted in time, and the purposes of optimizing the casting process, reducing defects and ensuring high-quality manufacturing of the aluminum alloy hub are achieved.

The invention discloses an industrial personal computer and multi-graphics card collaborative parallel computation acceleration system, which relates to the technical field of industrial resource allocation and parallel computation, and comprises a resource monitoring and predicting module, a resource management module and a prediction type resource preparation module, the task splitting and collaborative execution module comprises a task splitting module, a cross-node collaborative module and a collaborative operation engine; the intelligent scheduling and dynamic resource allocation module comprises an intelligent scheduler, a dynamic resource allocation module and a conflict avoidance module. According to the method, the GPU video memory utilization rate, the core utilization rate, the temperature, the video memory fragment rate, the available video memory total amount, the CPU core total utilization rate, the load condition and the idle core number index are collected in real time through a resource monitoring module, and the video memory capacity, the GPU core occupancy rate and the CPU load requirement are predicted in advance before a task is submitted in combination with a gradient boosting decision tree and a neural network prediction model; and resources are reserved, so that the scheduling delay is remarkably reduced, and the scheduling hit rate is improved.

The invention relates to the technical field of artificial intelligence fault diagnosis, and discloses an observation and aiming system fault prediction method based on IARO-GBDT, and the method comprises the steps: carrying out the preprocessing of collected data through an LPP algorithm, and screening the high-evaluation pin signal data as a model input data set; the artificial rabbit optimization algorithm ARO is improved, elite reverse learning is introduced in the ARO population initialization stage, and the fitness is calculated; an adaptive weight factor is introduced in an ARO circuitous foraging stage; t-distribution disturbance is introduced in the ARO random hiding stage to enhance the global search capability; and carrying out parameter optimization on the gradient boosting decision tree GBDT by adopting an improved artificial rabbit optimization algorithm IARO, constructing an IARO-GBDT fault diagnosis model, carrying out fault diagnosis on the observation and aiming system, and outputting a prediction result. According to the method, main parameters of the gradient boosting tree GBDT are optimized through the improved artificial rabbit optimization algorithm (IARO), the defect of blindness of parameter selection in the training process is overcome, and the prediction precision of the classification prediction model is improved.

The invention discloses a hybrid encryption method based on an industrial bus, and the method comprises the following steps: S1, generating an initial session key through a multiple elliptic curve algorithm, and generating an initial key pair through a pseudo-random number generator; s2, encrypting the control data by using a first-level key; s3, generating a secondary key to encrypt common transmission data; s4, dynamically generating a third-level temporary session key by using a pseudo-random number generator, and encrypting real-time transmission data of a large data volume; s5, realizing collaborative key generation and synchronous updating of a plurality of communication nodes through a distributed key management system; s6, periodically utilizing a probability density regression forest algorithm to dynamically update the key, and triggering key re-negotiation when communication abnormity is detected; and S7, integrating a classifier based on the combination of a deep forest and a gradient boosting decision tree, automatically classifying the data and dynamically adjusting an encryption strategy. According to the method, a multiple elliptic curve algorithm, dynamic key management and the like are combined, and efficient encryption in industrial bus communication is achieved.

The invention relates to a fractured coal and rock mass strength intelligent prediction method based on image recognition, and belongs to the technical field of rock mass mechanics prediction. The method comprises the following steps: (1) carrying out an indoor test according to different fracture parameters and lithology, and establishing an initial training data set; (2) introducing numerical simulation data, supplementing laboratory data through the simulation data, integrating the data into a data set, and constructing a sample database of coal and rock mass fractures and strength; (3) training a machine learning model by using the sample database, wherein the machine learning model is trained by adopting a gradient lifting decision tree model; (4) testing the precision of the machine learning model by using test set data; (5) acquiring a working face image; and (6) predicting the strength of the to-be-measured coal rock mass. According to the method, a traditional rock strength measurement method is improved, an accurate prediction model can be constructed by combining the nonlinear relationship between the geometrical characteristics and the strength of the coal-rock mass fractures with different lithology, and efficient prediction is realized.

The invention provides an intelligent health risk assessment method and system, and relates to the technical field of health risk assessment, health big data is constructed, feature selection is performed through the health big data to construct a gradient boosting decision tree model, risk prediction of individual diseases is performed, and time sequence analysis of health indexes is performed according to a first health risk score. Adjusting the first health risk score, carrying out health risk interpretation based on the second health risk score, carrying out health risk hierarchical evaluation according to the personalized health optimization suggestion and the second health risk score, making a hierarchical intervention strategy, carrying out health detection on the target individual, and triggering health early warning according to an abnormal detection result. The technical problems that in the prior art, health risk assessment data sources are single, so that health assessment is not comprehensive and inaccurate, and targeted intervention strategies are lacked are solved. The technical effects of comprehensively and accurately evaluating the individual health risk and providing a targeted intervention strategy are achieved.

The invention relates to the technical field of intelligent gardens and precise irrigation, and particularly discloses an intelligent irrigation monitoring method and system. According to the method, a multi-source sensor array is deployed, Kalman filtering is adopted to fuse environmental data, and a three-dimensional state vector input reinforcement learning model is constructed to generate an irrigation decision; predicting a vegetation water demand by combining a gradient lifting decision tree, forming a graded irrigation strategy and converting the graded irrigation strategy into a water pump control instruction; soil humidity feedback data are collected in real time, decision model parameters and filtering rules are dynamically adjusted, and closed-loop optimization is achieved. Through multi-source data fusion and a self-adaptive decision-making mechanism, the irrigation precision and the water resource utilization rate are remarkably improved, meanwhile, the response capacity of the system to the vegetation growth dynamic state and the environment change is enhanced, and the beneficial effects of optimization process closed loop, decision dynamic adaptation and controllable resource consumption are achieved.

The invention discloses a regional power grid wind power generation power prediction optimization method and system, and particularly relates to the technical field of power grid wind power generation. Multi-source environment data is collected, wind speed change characteristics are extracted, a time series data set is constructed, a hybrid prediction model is constructed in combination with a long-short term memory network and a gradient boosting decision tree, a time series trend and a nonlinear wind speed-power mapping relation are captured respectively, and prediction weights of the time series trend and the nonlinear wind speed-power mapping relation are adaptively adjusted through an attention mechanism. According to the method, the model hyper-parameters are adaptively adjusted by further combining Bayesian optimization, short-term prediction errors are corrected by using Kalman filtering, the real-time adaptability and stability of prediction are enhanced, the problems of prediction misalignment and the like caused by high nonlinearity and data scarcity of a wind speed mode in a complex terrain environment are effectively solved, and the prediction accuracy is improved. The wind power prediction precision is improved, a more stable and reliable scheduling reference is provided for a regional power grid, and the standby capacity demand and the operation cost are reduced.

The invention discloses a method for predicting the long-term delivery probability of a dual-drive reservoir, and belongs to the technical field of intelligent scheduling of water resources. Comprising the steps of constructing and training a causal consistency probability inflow prediction model, eradicating quantile crossover mathematically by adopting a double-anchor symmetric chain output layer, and performing training by utilizing a gradient heterogeneous modulation normal form based on internal attention deconstruction to efficiently learn a hydrological process; a mechanism constraint outflow prediction model is constructed and applied, physical mechanisms such as a water balance equation and an ex-warehouse boundary are deeply transformed and embedded into a loss function of a gradient boosting decision tree, and the model is forced to self-learn and comply with physical rules in the training process. According to the invention, a reservoir outbound prediction scheme with probability self-consistency and physical reliability can be generated.

A dynamic real estate valuation system based on multiparametric market indicators, which includes the following: a valuation engine configured to generate real-time results for property valuation; a multitude of distributed data ingestion and processing units configured to capture heterogeneous data sources, including historical property transaction data, real-time property listings, zoning and land use records, macroeconomic indicators, geospatial data, environmental sensor outputs, and sentiment-derived metrics; a model orchestration control unit comprising a stack of machine learning models, wherein the models include at least a gradient boosting decision tree model, a long-short-term memory (LSTM) time series forecaster, and an enhancement learning module that iteratively optimizes the model parameters based on the observed evaluation accuracy; a data contextualization controller configured to apply dynamic weighting to each input parameter based on the geographic, temporal, and market context by executing decay functions and location-specific rule matrices; a physical property valuation terminal (PVT) that includes an edge processing unit (EPU), geolocation circuitry, secure communication interfaces and a touch-based user interface; a valuation book subsystem configured to hash the valuation output, timestamp, and signatures of the input record into a blockchain-based distributed ledger; wherein the system is designed to continuously recalibrate its valuation results by comparing the predicted valuations with the actual sales or rental prices, and wherein the physical terminal is designed to produce a legally certifiable valuation document with embedded provenance data.

The invention discloses a surrounding rock deformation prediction method for tunnel construction in a soft rock area, belongs to the technical field of surrounding rock deformation prediction, and can solve the problem of low accuracy of surrounding rock deformation prediction in the soft rock area in the prior art. The method comprises the following steps: S1, according to a surrounding rock monitoring network of a tunnel, respectively adding adaptive weight items to a plurality of loss functions in a target function of a gradient boosting decision tree algorithm to obtain an improved gradient boosting decision tree algorithm; the plurality of loss functions are in one-to-one correspondence with a plurality of monitoring points in the monitoring network; s2, using surrounding rock monitoring data collected by a surrounding rock monitoring network to train an improved gradient boosting decision tree algorithm to obtain a tunnel surrounding rock deformation prediction model; and S3, predicting the surrounding rock deformation condition of the tunnel by using the surrounding rock deformation prediction model. The method is used for predicting surrounding rock deformation.

The invention discloses a power transmission line icing intelligent early warning method, system and device and a medium, and the method comprises the steps: obtaining icing state data and meteorological data, and dynamically adjusting the collection frequency and a dormancy strategy; performing data preprocessing and cleaning on the acquired data; extracting time-frequency features through wavelet packet transformation and a self-attention mechanism, and fusing the spatial dependency relationship and cross-modal interaction information of multiple monitoring points by using a graph neural network to obtain enhanced icing state characterization; performing icing risk prediction by adopting a gradient boosting decision tree model to obtain an icing risk prediction result; and analyzing an icing risk prediction result by using an interpretable tool, identifying a key factor which has the greatest influence on icing risk prediction, dynamically adjusting an early warning level according to the key factor, and generating an early warning and maintenance suggestion. Therefore, the monitoring real-time performance and the early warning timeliness are improved.

The invention relates to the technical field of oil and gas well management, in particular to an intelligent remote transmission control oil and gas well system based on the Internet of Things and multi-dimensional physical sensing feedback. Comprising a multi-dimensional sensor module; a 4G unvarnished transmission communication module and an intelligent remote decision module; the intelligent remote decision-making module is used for analyzing the collected data and generating a control strategy based on a self-adaptive gradient lifting decision-making tree algorithm; and the wide-temperature adaptive power supply module is used for providing continuous power supply for the system based on a multi-energy collaborative energy management algorithm and realizing energy optimization in an off-grid environment. By integrating multi-dimensional physical quantities such as pressure, flow, temperature and vibration, a multi-parameter coupled state space model is constructed, high-confidence real-time data input is provided for control strategy generation, limitation of traditional data processing on dimension coverage and feature extraction is broken through, and comprehensiveness and accuracy of gas well operation state evaluation are improved.

The invention provides a unified authentication and data authority management and control method and system for a big data component. The method comprises the steps of performing validity verification on an identity certificate and generating a unified identity token; obtaining an identity token bound with the data access request through the unified identity token; based on the identity token bound with the data access request, performing permission decision through a preset strategy rule to obtain a permission decision result; obtaining an adjusted permission decision result based on the user attribute, the current operation environment and the access mode; embedding the access traceability representation into the adjusted permission decision result to obtain processed structured data; and obtaining an audit record and a compliance report based on a data access request of the user, the adjusted permission decision result and the processed structured data. Based on the user attributes and the real-time operation environment, the authority decision result is dynamically adjusted by using the gradient boosting decision tree and the attention mechanism, so that cross-component unified management is realized, and the problem of authority strategy stiffness is solved.

The invention discloses an automobile lengthened longitudinal beam stamping springback prediction and compensation method, which comprises the following steps: constructing an initial data set containing an initial prediction value according to the influence of preset process parameters on stamping springback; constructing a springback prediction model by adopting an incremental plasticity theory, training the springback prediction model by adopting a gradient boosting decision tree, and adjusting process parameters by adopting a Bayesian optimization method; according to the optimal process parameters, finite element simulation is adopted to manufacture a longitudinal beam, simulation stamping springback data is generated, and a genetic algorithm and a variational auto-encoder are adopted to optimize a die compensation shape; and calculating compensation data of auxiliary stamping according to the die compensation shape, generating longitudinal beam processing data, and taking the longitudinal beam processing data as real stamping process parameters of the longitudinal beam. According to the stamping springback prediction and compensation method for the lengthened longitudinal beam of the automobile, the springback prediction accuracy can be improved, and the forming quality of the longitudinal beam is improved based on the optimized mold compensation shape and the auxiliary stamping compensation data.

The invention relates to the technical field of power equipment state monitoring, and discloses a cable state fault prediction method based on multi-source data fusion. According to the method, a digital twinborn model is constructed by acquiring multi-source data, and a theoretical health baseline changing along with working conditions is simulated and calculated in real time. And comparing the base line with the measured data to generate a thermoelectric coupling matrix with quantitative deviation, and diagnosing the degradation state according to the thermoelectric coupling matrix. And when the deviation exceeds a threshold value, performing attribution analysis by using a gradient boosting decision tree model, and generating a visual spectrogram associated with the partial discharge and the root cause. And finally, the defect type is judged by intelligently identifying a high-risk visual mode on the spectrogram, and the high-risk state is predicted. According to the method, working condition interference is filtered through dynamic reference, causal diagnosis is performed, the problem of high false alarm rate in the prior art is solved, and the prediction accuracy is remarkably improved.

The invention provides an enterprise intelligent finance and taxation system fusing supervised learning and a block chain, and solves the core technical problems of insufficient data credibility, transparent contradiction between privacy protection and supervision, AI model training data island and the like of a traditional finance and taxation system. According to the system, an innovative technical fusion scheme is adopted, bank API, OCR recognition, voice input and other multi-source financial data are integrated through a multi-modal data fusion unit, and high-quality data fusion is achieved through confidence evaluation and an intelligent conflict resolution algorithm; an intelligent classification unit based on BERT deep learning integrates a pre-training model with a business rule engine and a gradient boosting decision tree, and accurate and automatic classification of financial transactions is achieved. A complete technical solution is provided for enterprise finance and taxation digital transformation, intelligent, automatic and credible processing of finance and taxation businesses is achieved on the premise that data safety and privacy protection are guaranteed, and the method has wide market application prospects.

The invention discloses an industrial process product quality estimation method based on a stacked ensemble learning model, and the method comprises the following steps: S1, carrying out the preprocessing and feature extraction of industrial evaporation operation data through a convolutional neural network model; s2, selecting and training a base learning device; s3, selecting a gradient boosting decision tree as a meta-learner of the stacked ensemble learning model, and estimating the concentration of the outlet sodium aluminate solution by taking a prediction result of the meta-learner and features extracted by the convolutional neural network model as input; and S4, calculating an error between the outlet solution concentration estimated value and an actual measurement value, and re-training the random forest model for error compensation to obtain a final outlet product quality estimated value. According to the industrial process product quality estimation method based on the stacked ensemble learning model, the estimation result has high accuracy, and the problem that the concentration of an outlet sodium aluminate solution in a multi-stage evaporation process in the aluminum oxide production process is difficult to estimate under the current industrial background can be solved.

The invention relates to the technical field of power system power distribution network fault detection, in particular to an arc fault diagnosis and analysis method based on an artificial intelligence algorithm, and the method comprises the four steps: synchronous data collection and topological excitation, deployment of terminals at transformer area nodes, injection of characteristic current, and synchronous collection of response waveforms; signal preprocessing: separating a key frequency band through double-digital band-pass filtering, and calculating energy ratio and other enhanced fault features; performing multi-source feature fusion and intelligent diagnosis, constructing a vector containing statistical features, time domain distortion features and topology identification, and inputting a gradient boosting decision tree and deep neural network hybrid model to obtain fault confidence and type; and based on fault positioning and verification of topology, scheduling multi-node cooperative monitoring, and determining a fault point in combination with a topological relation. The method improves the data reliability and diagnosis precision, achieves the precise positioning of a fault, and guarantees the safe operation of a power distribution network.

The invention discloses a power distribution network fault early warning method, system and device and a storage medium, and the method comprises the steps: carrying out the processing of the operation state data of a power distribution network according to a minimum redundancy and maximum correlation algorithm, and obtaining a corresponding dimension feature tag; performing classification processing on the data according to the dimension feature tags to obtain a balanced data set; constructing a target data model based on a weak classifier through a multi-dimensional integration strategy; and optimizing and integrating the target data model based on a gradient lifting decision tree algorithm to obtain a fault prediction model for fault prediction. According to the method, the integrated prediction model is constructed by using the multi-dimensional weak classifier, so that the state in the operation process of the power distribution network can be accurately predicted, the fault risk of the power distribution network can be early warned in advance, and a more effective fault maintenance strategy can be formulated, thereby reducing the occurrence of power distribution network accidents and improving the economy and safety of the power distribution network.

The invention discloses a method and system for predicting juvenile depression based on intestinal flora, and relates to the technical field of bioinformatics and artificial intelligence, and the method comprises the steps: firstly, obtaining an original sequence of a microbiome, carrying out the preprocessing of the original sequence of the microbiome, and obtaining a feature matrix; and screening core flora characteristics with stable trans-folding by adopting characteristic importance evaluation and interpretability analysis based on a gradient boosting decision tree. A mixed weighted graph is constructed based on Spearman correlation and a proximity relationship, and an absolute value of a correlation coefficient is taken as an edge weight and an edge density is adjusted through a threshold adaptive strategy. And finally, through an improved graph attention neural network, based on edge weight attention, layer normalization and random inactivation, enhancing robustness, and adopting adaptive optimization to complete parameter learning. And determining a dynamic classification threshold according to the AUC of the target patient, and outputting a sample discrimination result and confidence. According to the method, the accuracy, stability and biological interpretability of juvenile depression recognition are remarkably improved.

The invention belongs to the technical field of wind power, and discloses a feature refined capture-oriented ultra-short-term wind power prediction method, which specifically comprises the following steps: S1, data preprocessing: preprocessing missing values and abnormal values of a data set to ensure data quality; the method comprises the steps that firstly, meteorological factors with the strongest correlation with wind power are recognized through a gradient boosting decision tree, meteorological feature screening is achieved, secondly, variational mode decomposition is optimized by combining a northern eagle optimization algorithm improved by a subtraction optimizer, time sequence data are subjected to fine processing and decomposed into components with different frequencies, and the components with different frequencies are obtained; the method comprises the following steps: firstly, using an Informer prediction model to capture dynamic change characteristics under different scales, and finally, using the Informer prediction model to capture a long-term dependency relationship of a time sequence, performing refined training on the model through an RMSprop optimizer to further improve the prediction accuracy, and performing a simulation experiment to verify the accuracy and timeliness of the method in data processing and wind power prediction.

The invention discloses a power distribution network transient characteristic prediction method based on supervised learning, and relates to the technical field of power distribution network state prediction, and the method comprises the steps: collecting historical operation data through a power distribution network monitoring system, carrying out the data preprocessing, and obtaining standardized multi-dimensional time series data; carrying out transient feature extraction, constructing a high-dimensional feature set, and carrying out feature dimension reduction according to a transient event tag to generate a feature subset; inputting the feature subset into a mixed supervised learning model of a gradient boosting decision tree GBDT and a long short-term memory network LSTM for joint training to obtain a transient feature prediction result; and calculating a root-mean-square error according to the transient characteristic prediction result and the real-time monitoring observation value of the power distribution network, and dynamically adjusting hyper-parameters of the supervised learning model based on a Bayesian optimization algorithm. According to the method, the detection accuracy can be improved, the calculation complexity can be reduced, and the discrimination capability and the time sequence prediction capability of the model are considered.

The invention belongs to the field of cultural heritage digital protection, and discloses an ancient building three-dimensional modeling method based on three-dimensional laser scanning, which comprises the steps of extracting feature points of a point cloud data set and texture image data, and performing registration in combination with a flight log of an unmanned aerial vehicle; performing image restoration and feature extraction on the texture image data and the point cloud-image registration result through a convolutional neural network-probability Markov random field (CNN-PMRF) model; performing geometric feature extraction on the point cloud data set, fusing the texture image feature vector to obtain a geometric-texture joint feature vector, and optimizing a rough mesh model generated based on the point cloud data set; mapping the repaired texture image data to the optimized grid model to obtain a preliminary three-dimensional grid model; and processing the preliminary model parameters through a gradient boosting decision tree (GBDT) model to obtain a correction value, and adjusting the preliminary three-dimensional grid model based on the correction value to obtain an optimal three-dimensional model. The precision of three-dimensional modeling of the ancient building can be improved.

The invention discloses a wind power plant wind speed prediction method, system and device and a storage medium, and the method comprises the steps: optimizing the white noise amplitude weight and noise addition times in adaptive noise ensemble empirical mode decomposition CEEMDAN through employing an improved energy valley optimization algorithm IEVO, and carrying out the CEEMDAN decomposition of the historical data of a wind power plant; the decomposed data is preprocessed by adopting sample entropy SE, so that the calculation complexity of the data is reduced; for the preprocessed data, introducing a random forest algorithm RF feature selection of a heuristic strategy for optimization, and eliminating redundant features; converting the optimized data from one-dimensional time sequence data into a two-dimensional image by using a GASF (Grubby Amplitude Shift Function), and extracting image features; the extracted image features are sent to a time sequence block converter Patch TST model for wind speed prediction, and a training algorithm GPTuner based on a gradient boosting decision tree is introduced into the Patch TST model to adjust model parameters; compared with a traditional method, the accuracy of wind speed prediction can be effectively improved, and stable operation of a wind power plant is guaranteed.