302 results about "Nonlinear correlation" patented technology

The invention relates to the technical field of computer programming languages, and particularly discloses a coal-fired power plant safety monitoring system and method. The system comprises a multi-modal data fusion platform, a federal learning agent network and a digital twinborn simulation engine. The edge computing node carries out unified acquisition and feature extraction on multi-source heterogeneous data through multi-protocol conversion, quantum noise suppression and a preprocessing chipset; the multi-modal data fusion platform realizes semantic mapping based on a knowledge graph, and fuses time-space correlation characteristics of data such as an infrared image and gas concentration by adopting a time-space encoder and a cross-modal attention mechanism. According to the method, the problems of early warning delay and high false alarm rate caused by low multi-source data decentralized processing efficiency and insufficient nonlinear correlation analysis in a traditional scheme are solved, efficient data fusion, complex risk accurate prediction and automatic safety response are realized, and the real-time performance and reliability of a coal-fired power plant monitoring system are remarkably improved.

The invention provides a carbon database construction method and system based on regional energy carbon emission prediction, and the method comprises the steps: eliminating the space-time reference deviation of an original data set, and generating an energy activity hologram spectrum with a unified space-time label; inputting the energy activity holographic spectrum into a non-linear correlation program, extracting a carbon release intensity characteristic curve of each energy carrier through a self-adaptive weight distribution mechanism pre-stored in the non-linear correlation program, and constructing a spatial-temporal characteristic network in combination with industrial operation cycle parameters and climate response factors in a target area; the spatial-temporal characteristic network establishes a dynamic coupling relationship between an energy flow mode and a carbon emission hot area to form a carbon emission associated topological structure with a self-organizing characteristic; a three-dimensional carbon database is formed, and the three-dimensional representation and deduction capability of space-time evolution of carbon emission is realized. The system comprises an atlas generation module, a structure construction module and a content fusion module. According to the invention, normal form transformation from static carbon accounting to dynamic carbon metabolism simulation is realized.

The invention provides an intelligent inspection operation robot, which comprises a self-adaptive interaction assembly for collecting unstructured features in an inspection environment of the inspection operation robot in real time, converting continuous signals of the unstructured features into discrete interaction decision nodes, and generating an interaction parameter set capable of being iteratively optimized through a cross validation mechanism; the autonomous intention analysis component constructs a probabilistic decision-making program based on implicit features of an operator behavior track of the inspection operation robot, extracts a potential mode of an operation intention through nonlinear correlation analysis in combination with an interaction parameter set, and recognizes an interaction demand which is not clearly expressed; and the collaborative response component constructs a response decision mechanism with self-organizing characteristics, and jointly inputs an interaction parameter set and an interaction demand into the response decision mechanism by establishing a dynamic association rule between different interaction channels of the inspection operation robot and an operator to generate a composite interaction scheme. According to the method, the composite interaction scheme comprehensively considering multiple factors can be generated.

The invention relates to the technical field of error correction, in particular to a water conservancy facility environment interference error correction method and device based on multi-dimensional data. The method comprises the following steps: collecting historical monitoring logs of water conservancy facilities, carrying out environmental disturbance event identification and multi-scale disturbance relevance mining, and constructing an environmental disturbance space-time relevance map; monitoring surrounding environment water monitoring parameters of the water conservancy facility, performing sequential response trend change analysis and dynamic response characteristic modeling, and constructing a facility environment personalized change response portrait; identifying a plurality of water body position monitoring data according to the surrounding environment water body monitoring parameters, and constructing a hydrodynamic full-field sensing graph; and performing nonlinear correlation analysis based on the hydrodynamic full-field perception graph and the personalized change response portrait of the facility environment, and constructing a digital twinborn model of the water conservancy facility under the water flowing condition. According to the invention, through dynamic error trend analysis and correction, the monitoring precision and monitoring stability of the water conservancy facility are improved.

The invention relates to the technical field of filling automation control, in particular to a full-automatic essential balm filling control system based on PLC online monitoring, which comprises a filling parameter real-time acquisition module, a dynamic coupling analysis module, a multi-axis cooperative execution module, a foreign matter real-time detection module, an interlocking control module and a filling quality feedback module. Wherein the filling parameter real-time acquisition module is used for acquiring temperature data of a filling head, a liquid viscosity value and the flow velocity of a filling pipeline in real time; and the dynamic coupling analysis module is used for establishing a correlation model of the temperature gradient and the viscosity change rate and generating a dynamic compensation parameter set. According to the method, the nonlinear correlation model of the temperature and the viscosity change rate is constructed, and the dynamic compensation parameter set is generated, so that self-adaptive precise control in the filling process and automatic response linkage of abnormal working conditions are realized, and the stability and the intelligent level of a filling system are remarkably improved.

The invention relates to the technical field of automobile brake control, in particular to a self-adaptive brake kinetic energy recovery control method and system based on multi-sensor fusion. Tire wear and road surface feature data are collected through a multi-source sensing fusion unit, and a tire wear coefficient and a road surface friction coefficient are generated through an intelligent decision calculation unit; the method comprises the following steps of: integrating the nonlinear correlation of the multi-source sensing fusion unit and the self-adaptive control execution unit through a rule and data fusion algorithm, outputting a comprehensive friction coefficient, and dynamically adjusting the braking kinetic energy recovery force and response time by the self-adaptive control execution unit according to the comprehensive friction coefficient. The system comprises a multi-source sensing fusion unit, an intelligent decision calculation unit, the self-adaptive control execution unit and a closed-loop feedback calibration unit. The closed-loop unit corrects data deviation through cross validation of the laser radar and the motor torque inversion model, the problems of poor working condition adaptation and unreliable data are solved, and the kinetic energy recovery efficiency, the braking safety and the driving smoothness are improved.

The invention relates to the technical field of high temperature sensing and data fusion, in particular to a method for array thermocouple multi-mode compensation and DIC stress field space-time coupling fusion, which comprises the following steps: step 1, in a thermotechnical signal intelligent processing and compensation unit, completing hardware and algorithm collaborative design of an electronic cold junction compensation module; 2, constructing a multi-algorithm fusion compensation module, integrating nonlinear correction, drift compensation and interference suppression functions, and accurately coping with various interference signals through a dynamic weighting strategy; step 3, adopting a sub-pixel-level matching algorithm and a homography matrix calibration technology to realize high-precision space alignment of the temperature and stress measurement units; and establishing a nonlinear incidence relation between the temperature and the stress based on an improved Gaussian process regression model. According to the invention, based on collaborative design of the thermotechnical signal intelligent processing and compensation unit and the DIC vision and temperature data conjoint analysis module, the core precision problem of temperature and stress detection in a high-temperature environment is solved through hardware optimization and algorithm innovation.

The invention discloses a short-term power load prediction method, system and device based on multi-intelligent-model fusion and a medium, and belongs to the technical field of short-term power load prediction, and the method comprises the steps: obtaining regional historical load data and meteorological data; performing data cleaning on the obtained load data and meteorological data; measuring linear and nonlinear correlation between the power load and the meteorological factors, and screening meteorological data with high load correlation; decomposing the load data into a time sequence by using an empirical mode decomposition method based on combination of multi-scale permutation entropy to obtain a multi-scale sub-data sequence; respectively predicting the multi-scale sub-data sequences to obtain prediction results; carrying out weighted fusion on the prediction result through a long short-term memory network model to obtain a load prediction result, and optimizing model parameters to obtain a trained multi-model prediction model; and predicting the test set data by using the trained model to obtain a final load prediction result. According to the invention, the precision and adaptability of load prediction are effectively improved.

The invention discloses an electric power resource scheduling method and device suitable for extreme weather, terminal equipment and a storage medium, and belongs to the technical field of electric power scheduling, and the method adopts nonparametric kernel density estimation to construct a wind and light output joint probability distribution function representing output characteristics of wind power output and photovoltaic output in extreme weather. When flexibility demand analysis is carried out, the advantages of nonlinear correlation between variables and tail risks can be analyzed by means of a joint distribution probability function, wind power prediction errors and photovoltaic prediction errors are accurately analyzed, and then adjustment demand risks of net load flexibility demands are quantified in combination with convolution operation. The problems that the joint probability distribution characteristics of wind and light output in extreme weather cannot be accurately described and the regulation demand risk cannot be ignored in the current flexibility demand evaluation method depending on the normal distribution independent assumption are solved. And the power supply reliability and the operation stability of the power system in extreme weather can be ensured by the formulated power dispatching plan.

The invention relates to the field of lamp holder module data analysis, in particular to an information analysis method and device capable of achieving communication storage of a lamp holder module. The method comprises the following steps: acquiring a historical operation monitoring log of a lamp holder module, and performing data cleaning and multi-window division to obtain monitoring logs of a plurality of time windows; performing transient abrupt change identification and multi-scale abnormal state deconstruction according to the monitoring logs of the plurality of time windows to obtain transient abrupt change state features of a plurality of scales; carrying out multi-time-point environment temperature fluctuation analysis on the monitoring logs of the plurality of time windows, and carrying out multi-scale quantitative correlation analysis according to the transient abrupt change state characteristics so as to obtain a quantitative correlation relationship between transient abrupt change and environment change; and performing multi-factor nonlinear correlation evolution according to the quantitative correlation, and constructing a multi-factor causal chain. According to the invention, real-time evaluation is carried out according to the working state of the current lamp holder module, and accurate fault risk and service life state evaluation is provided.

The invention discloses a multi-source heterogeneous data processing method and system based on a test chip, and the method comprises the steps: employing a distributed message queue to access a multi-protocol data stream in real time, collecting the multi-source heterogeneous data related to a chip test, and carrying out the normalization preprocessing; performing cross-domain spatio-temporal feature alignment and mapping on the preprocessed multi-source heterogeneous data, constructing a hybrid incidence matrix based on linear correlation degree and nonlinear correlation degree, extracting three-dimensional joint features, and generating a dynamic metadata tag; generating nodes and edges by using the incidence matrix and the extracted three-dimensional joint features, constructing a parameter association graph structure, performing graph embedding training and generating an index structure, positioning Top-K candidates based on the index structure by using query features and labels, and sorting and returning a result according to a comprehensive distance; the problem that cross-domain association analysis is difficult to carry out due to the fact that the format difference of multi-source heterogeneous data generated by existing chip testing is large is solved, and the precision and efficiency of multi-source heterogeneous data processing are improved.

The invention provides a multi-modal pulse fusion network prediction framework for aero-engine gas path performance parameters, and belongs to the technical field of aero-engine gas path performance parameter prediction and fault diagnosis. The method comprises the following steps: firstly, acquiring flight data of an aero-engine, and preprocessing data of each sensor; secondly, constructing a multi-modal pulse fusion neural network model comprising a time sequence data pulse processing module, an image data pulse processing module, a multi-modal feature fusion module and a prediction output module; and finally, training the multi-modal pulse fusion neural network model, and predicting gas path performance parameters by using the trained model. According to the method, the leakage integration distribution spiking neuron and the attention mechanism are introduced, the depth feature extraction and fusion of the time sequence data set and the image data set are realized, the complex nonlinear correlation between the data is effectively captured, the accuracy of the prediction result is remarkably improved, and the real-time prediction requirement in the operation process of the aero-engine can be met.

The invention discloses a thermal power generating unit decommissioning path planning method and device based on meteorological and hydrological risks, equipment, a medium and a product, and relates to the field of power system decision optimization. The method comprises the steps of obtaining meteorological and hydrological risk data and a decommissioning index of a thermal power generating unit; according to the decommissioning index and the meteorological and hydrological risk data under the climate change, a progressive hierarchical screening logic method is adopted for screening and constraint progressive correction processing; performing mapping and nonlinear correlation interaction analysis by adopting Monte Carlo simulation and a gradient boosting decision tree and combining multiple groups of parameter combination data, such as water temperature and runoff volume in meteorological and hydrological variables; and performing collaborative optimization processing based on the adaptive strategy model and a set analysis function to obtain an optimal strategy, and then performing mapping and analysis processing in combination with an analysis result to obtain targeted strategy list information for planning the decommissioning path of the thermal power generating unit. The invention aims to realize the planning of the decommissioning path of the thermal power generating unit.

The invention discloses a high-dimensional feature selection method of an evolutionary multitask optimization algorithm based on proxy assistance, and mainly relates to the field of feature selection and intelligent optimization. The invention designs a novel evolutionary multi-task optimization technical scheme for solving three defects of an existing evolutionary multi-task optimization algorithm for solving high-dimensional feature selection. In the task generation stage, a task generation strategy considering linear and nonlinear correlation at the same time is provided, and diversity is improved; in the knowledge migration stage, an agent-assisted knowledge migration strategy is provided for the farmer particles, and forward knowledge is migrated by evaluating aggregated knowledge and non-aggregated knowledge through agent assistance; in addition, a bidirectional asymmetric flipping strategy is provided for winner particles to improve the classification accuracy. By carrying out feature selection on a small-sample high-dimension biological data set, experiments show that compared with other feature selection methods based on an evolutionary multi-task optimization algorithm, the method can obtain a better feature subset.

The invention discloses a circulating water culture pond fish school biomass evaluation method, which comprises the following steps of: firstly, acquiring a sonar image corresponding to each circle of rotary scanning of a circulating water culture pond by sonar through steps S1 and S2, and extracting a spatial three-dimensional coordinate of each identified cultured fish in each sonar image based on image identification; filtering noise points generated due to repeated identification in the identified cultured fish coordinate points through the step S3, and counting the number of fish schools corresponding to each sonar image according to the actual cultured fish coordinate points obtained through point cloud merging, and then obtaining a linear correlation curve Si (hi) of the effective area Si of the pool wall and the sampling depth hi through the step S4, and finally, performing integration on the two curves according to intervals through the step S5, performing layered calculation on the number of the fish schools in each interval, and performing accumulation to obtain an estimated value P of the fish school biomass in the circulating water culture pond, so that the measurement accuracy of the fish school biomass in the circulating water culture pond can be improved.

The invention discloses a photovoltaic power prediction method and system based on artificial intelligence. The method comprises the steps of photovoltaic data acquisition, preliminary data processing, power prediction model construction, model hyper-parameter optimization and photovoltaic power prediction. The invention relates to the technical field of photovoltaic data processing, in particular to a photovoltaic power prediction method and system based on artificial intelligence, and the method comprises the steps: obtaining original data through photovoltaic data; a primary processing method of missing value processing, spatio-temporal data alignment, data standardization and data set segmentation is adopted; an improved graph convolutional network model is adopted as a power prediction model, nonlinear correlation is effectively captured through adaptive feature extraction and an uncertainty perception mechanism, and meanwhile, the interpretability and the anti-interference capability of the model are enhanced by combining physical rule constraint output; a resonance evolutionary optimization algorithm is adopted to carry out model hyper-parameter optimization, and a dynamic adjustment strategy is combined with frequency domain analysis and hierarchical optimization, so that the stability of the model in a variable environment is improved.

The invention discloses a new energy missing data completion method and system based on multi-model fusion. The method comprises the following steps: performing linear and nonlinear correlation two-stage feature screening on a collected new energy data set; normalizing the data set, constructing a missing data mask according to a missing proportion parameter, and dividing the data set; the designed AFMFormer model is constructed and trained; a self-adaptive frequency domain feature extraction module constructed in the model utilizes a data-driven dominant frequency extraction and frequency spectrum noise suppression mechanism to enhance the modeling capability of the model for a long-sequence dominant mode structure; the multi-granularity time sequence coding module realizes semantic consistent feature fusion through short-term features and long-term trends of a multi-scale modeling time sequence and through a learnable weight and an adaptive alignment mechanism, and generalization ability training of the model under a complex time sequence structure is further improved. Compared with a traditional method, the method shows higher precision, robustness and generalization ability in a high-fluctuation and high-missing-rate data scene.

The invention provides an ecological system service flow simulation and collaborative tradeoff relation quantification method based on a graph neural network, and relates to the technical field of ecological system service simulation and quantification. According to the method, the Huaihe River basin serves as a research area, firstly, a landscape is abstracted into a graph structure, ecological patches serve as nodes and are endowed with multi-dimensional features, ecological process flow paths serve as edges, and flow attributes are quantified; then, constructing a graph neural network model, and learning inherent rules of generation, flow and consumption of an ecological system service through training; and finally, realizing ecological system service, particularly dynamic simulation of supply service and adjustment service, and quantifying the collaborative tradeoff relation by adopting a multi-index fusion method. According to the method, the nonlinear correlation of the ecological process can be accurately captured, the flow simulation and relation quantification precision is improved, scientific support is provided for ecological planning and management of the Huaihe River basin, and compared with an InVEST model, the simulation error is reduced by 20%-30%, and the collaborative tradeoff recognition accuracy is improved by 15% or above.

The invention provides a data quality evaluation method and system. The method comprises the following steps: firstly, collecting three types of data sets of three-phase unbalance degree, free charge density distribution and line impedance decomposition; and then, carrying out space topology mapping on the charge density gradient extreme point of the cable joint area and the voltage phase angle, considering charge migration effect compensation, and calculating a corrected three-phase voltage unbalance parameter. Secondly, establishing a coupling factor matrix based on the separated line inherent impedance component and the load fluctuation impedance component, and performing time domain convolution operation on the coupling factor matrix and a voltage unbalance parameter to generate a distortion feature vector; and finally, calculating data credibility weight distribution of the global monitoring nodes according to the nonlinear correlation degree between the three-phase unbalance degree data and the distortion feature vector, and using the data credibility weight distribution to evaluate the quality of the low-voltage treatment data. According to the technical scheme provided by the invention, the accuracy and reliability of data quality evaluation can be improved.

The invention discloses a tailing pond water level intelligent early warning system based on multi-modal data sensing, and the system comprises a data acquisition module which collects multi-dimensional data, and obtains water level seepage fusion features through principal component analysis; the behavior acquisition module is used for acquiring external rainfall characteristics and acquiring water level conduction behavior data by excavating a space-time nonlinear correlation mode between the water level seepage fusion characteristics and the external rainfall characteristics; the spatial topology module is used for acquiring a safety threshold through historical risk data and constructing an improved tailing pond spatial topology network in combination with a graph convolutional network; according to the improved tailing pond spatial topology network, on the basis of a graph convolution network infrastructure, the construction of the improved spatial topology network is completed by designing a topology perception attention mechanism and stacking multiple layers of graph convolution; and water level early warning data: inputting the water level conduction behavior data and the water level seepage fusion features obtained in real time into the improved tailing pond spatial topology network to obtain an early warning result.

The invention relates to the technical field of air tightness detection, in particular to a diaphragm type energy accumulator air tightness detection method which is used for solving the problems that in the prior art, equipment operation characteristics cannot be accurately described, typical defect modes cannot be recognized in combination with spectral clustering, and a quantitative basis cannot be provided for equipment fault trend analysis and intelligent maintenance. The method comprises the following steps: constructing a low-dimensional state map to describe equipment operation characteristics, identifying typical defect modes in combination with spectral clustering, establishing a nonlinear correlation model to reveal a defect evolution relationship, optimizing classification model parameters by adopting an evolutionary algorithm, improving the identification accuracy, mining a most probable defect evolution path based on a transition probability matrix, and improving the identification efficiency. And a quantitative basis is provided for equipment fault trend analysis and intelligent maintenance.

The invention relates to the technical field of optical communication equipment testing, in particular to an integrated optical module multi-parameter testing system and method.The method comprises the steps that a multi-channel parallel testing unit synchronously collects light spot morphology, divergence angles, insertion loss and return loss space and transmission characteristic parameters through a transmission light path and a reflection light path; the parameter collaborative analysis module performs nonlinear correlation analysis and dynamic performance threshold evaluation based on a cross-dimension correlation model; the intelligent calibration engine dynamically adjusts the test benchmark and compensation parameters according to the environment temperature and the equipment aging attenuation model; the fault diagnosis and prediction unit identifies potential defect types of the collimator, constructs a performance degradation prediction model in combination with a working state and environmental stress, and predicts a performance evolution trend according to test data; and the test report generation module automatically generates a standardized and customized dual-mode test report, a quality grade label and a performance optimization suggestion according to the prediction result and the diagnosis conclusion. Therefore, the problems of poor test precision, low test efficiency and the like in the prior art are solved.

The invention discloses an AI intelligent coal blending method based on deep learning, relates to the technical field of intelligent coal blending, and solves the technical problems of low efficiency in feature association recognition and screening and poor model architecture scene adaptability. The Pearson coefficient is used for linear features, the mutual information method is used for hierarchical redundancy elimination for nonlinear features, the limitation of traditional single statistic feature screening is avoided, hidden association is generated and mined through composite features, feature dimensions are more simplified, association with coal blending targets is more direct, the model training efficiency and prediction precision are effectively improved, and the method is suitable for large-scale popularization and application. Models are customized for different scenes, compared with an existing method that a single model adapts to a full scene, precise coverage of lightweight-dynamic-complex scenes is achieved, and the applicability and optimization effect of coal blending schemes under different scenes are greatly improved.

The invention discloses a traditional Chinese medicine analysis and identification method and system based on clustering analysis, and relates to the technical field of traditional Chinese medicine analysis, and the method comprises the following steps: S1, collecting a chemical fingerprint spectrum, a microscopic image, metabonomics data and geographical indication information of a traditional Chinese medicine sample; according to the traditional Chinese medicine analysis and identification method and system based on clustering analysis, through a dynamic weight distribution mechanism and a high-order nonlinear collaborative characterization technology, the core problem of multi-modal data fusion failure in traditional Chinese medicine identification is effectively solved; the dynamic weight distribution network adaptively adjusts the contribution degree of each modal based on real-time entropy fluctuation and sample density, so that the identification precision under complicated scenes such as related species and processed product difference is remarkably improved; the high-order tensor fusion space is combined with adversarial generation training, the characterization limitation of a linear kernel function on component-form-effect nonlinear correlation is broken through, and the clustering boundary better fits the overall characteristics of traditional Chinese medicine.

The invention relates to the technical field of crane safety monitoring, and discloses a crane hoisting overload state monitoring method and system based on digital twinning, and the system comprises a twinning modeling module, a data acquisition module, a state simulation module, an overload decision module, and a model optimization and visualization module. Deep interaction between a physical world and a virtual space is realized through digital twin bodies, limitation of a traditional monitoring method is broken through, multi-source parameters such as wind speed, load and structural stress are fused for the first time, a dynamic coupling model is constructed, nonlinear correlation of wind load, inertia force and structural deformation is captured, the problem of missed judgment of hidden risks in the traditional method is solved, and the method has a wide application prospect. An accident early warning coverage range is expanded, a deflection calculation model is established based on an improved mechanical theory, high-sensitivity detection of fine deformation of the main beam is realized, compared with a static model, the magnitude order is improved, structural damage accumulation is effectively prevented, deep correlation mining is performed on historical and real-time data, and long-time-history advanced early warning of overload risks is realized.

The invention belongs to the technical field of electric power measurement, and discloses a calibration method and system of an intelligent measurement switch and a multi-switch synchronous measurement method, and the method comprises the steps: obtaining voltage and current waveform data of the intelligent measurement switch under a nonlinear load condition, and extracting a frequency spectrum feature matrix of the voltage and current waveform data; constructing an amplitude-phase coupling error model representing a nonlinear incidence relation between amplitude fluctuation and phase deviation, inputting the spectrum characteristic matrix into the amplitude-phase coupling error model, and calculating an amplitude-phase coupling error vector; performing vector decomposition on the amplitude-phase coupling error vector, and separating out an in-phase component error and an orthogonal component error; and generating a calibration compensation coefficient according to the in-phase component error and the orthogonal component error, and performing vector calibration on the intelligent measurement switch, thereby improving the calibration precision of the intelligent measurement switch.

The invention belongs to the technical field of landslide prediction, and discloses a landslide prediction method and system based on multi-source data fusion, and the method comprises the steps: collecting multi-source heterogeneous landslide related data, carrying out the preprocessing of the landslide related data through a data fusion technology, and obtaining a landslide data set; performing noise reduction on the landslide data set; mining nonlinear correlation characteristics in the landslide data set after noise reduction, and constructing high-order statistics capable of depicting a landslide evolution process; constructing a plurality of prediction sub-models according to the high-order statistics, and fusing the prediction sub-models to form a landslide prediction model; and inputting the to-be-measured high-order statistical magnitude into the landslide prediction model, and if the to-be-measured high-order statistical magnitude exceeds a preset danger threshold, determining that a landslide risk exists. The method has high universality and practicability, and can be widely applied to the field of monitoring and early warning of geological disasters such as landslide.

The invention provides a nonlinear correlation pair screening method for photovoltaic power generation output power influence factors. The nonlinear correlation pair screening method comprises the following steps: acquiring photovoltaic power generation output power data and corresponding meteorological factor data in a preset time period; constructing a plurality of time window pairs according to the photovoltaic power generation output power data and the meteorological factor data; calculating a nonlinear correlation coefficient of the time window pair, and obtaining a candidate set of a nonlinear correlation pair according to a calculation result; and performing reduction processing on the candidate set of the nonlinear correlation pair to obtain an optimal nonlinear correlation pair. According to the method, influences of different regularity, different periodicity and volatility of the influence factors of the output power, namely meteorological factors on the time scale are fully considered, and a more reliable analysis result can be obtained.

The invention discloses a personalized physical examination period dynamic estimation system and method based on big data, and relates to the technical field of medical health data processing and intelligent evaluation. The estimation method comprises the step of constructing an integrated implementation framework including a multi-source data integration link, a multi-party cooperative processing link, a three-step modeling evaluation link and a quantitative feedback adjustment link. According to the personalized physical examination cycle dynamic estimation system and method based on big data, a medical institution, wearable equipment, family genetic disease history and government affair medical treatment are integrated through a cross-domain data fusion platform, and a multi-dimensional evaluation basis covering physiology, behavior, heredity and resource collaboration is constructed; and then through a three-step modeling process of a hybrid model training engine, the problem of insufficient data is solved through similar group data adaptation, nonlinear correlation features are mined through multiple decision trees, a risk level is finally output, and a period is dynamically adjusted in combination with medical resource assessment.

The invention discloses a foundation high-excavation low-filling operation state monitoring and early warning method based on cloud edge collaboration, and relates to the technical field of building construction.The method comprises the steps that firstly, various sensors are deployed according to topography and geology to collect multi-source data, abnormal value detection and correction processing are conducted on the collected multi-source data, and the abnormal value of the collected multi-source data is obtained; integrating the processed multi-source data to obtain multi-class feature data; step 2, constructing an improved LSTM model by taking the multi-class feature data as input and adopting historical operation state data as a training sample; the improved LSTM model focuses key time steps and key sensor features in the multi-class feature data by introducing an attention mechanism, and performs feature enhancement to optimize the state characterization capability; in the improved LSTM model, a mean square error is used as a loss function, an Adam optimizer is used for initial training, and an operation state evaluation model is obtained and used for learning nonlinear correlation between various types of feature data and operation states.