1487 results about "Ensemble learning" patented technology

The invention relates to the technical field of enterprise carbon emission analysis, and discloses an enterprise carbon emission analysis method and system based on an ESG comprehensive evaluation model, and the system comprises a data collection layer, an analysis processing layer and a decision output layer. The data acquisition layer acquires carbon emission related data in the whole production and operation process of an enterprise in real time through multiple devices; the analysis processing layer constructs a credible data processing environment, and realizes data cleaning verification, secure transmission, classified storage and dynamic synchronization of data and a model; and the decision output layer establishes a multi-dimensional associated ESG comprehensive evaluation model by using a mode recognition technology and an integrated learning algorithm, and realizes carbon emission intensity calculation, supply chain carbon footprint tracing and emission reduction path optimization decision. The method and the system can comprehensively and accurately analyze the carbon emission condition of the enterprise, provide scientific decision support for low-carbon management of the enterprise, and assist in realizing a carbon neutralization target.

The invention discloses a load flow calculation and simulation control method and system for a digital twin power grid, and relates to the technical field of digital twin simulation control, and the method comprises the following steps: constructing a digital twin power grid model, and carrying out the dynamic topology optimization processing of the digital twin power grid model based on remote signaling credibility weighting; according to the optimized digital twin power grid model, identifying the power grid operation risk based on an integrated learning model; according to the risk identification result, generating a transfer path control strategy based on an analytic hierarchy process and a fuzzy comprehensive evaluation method; mapping the transfer path control strategy into a control action instruction set, and simulating execution and establishing a feedback correction mechanism on the digital twin power grid model; by generating the optimal path control strategy and performing control strategy analog simulation and self-adaptive feedback correction based on the digital twin power grid model, the problem of lack of intelligent path control strategy selection and simulation verification based on state dynamic identification in the prior art is solved.

The invention relates to a concrete working performance measurement method and system based on a multi-modal visual large model, and solves the problem that rapid detection of concrete working performance parameters is troublesome, and the method comprises the steps: based on the spatial semantic understanding capability of the multi-modal visual large model, combining a multi-view stereoscopic vision and structured light scanning technology, and calculating the working performance of concrete; reconstructing a three-dimensional geometric structure of the concrete slurry, extracting morphological characteristic parameters, and forming characteristic vectors; inputting the feature vectors into a pre-trained multi-task neural network, fusing the spatial-temporal features and combining a rheological algorithm to identify various working performance parameters; integrating identification results for at least three times by adopting integrated learning, and verifying parameters based on a fluid dynamics basic equation through a fluid simulation platform; and based on the verification result, generating a mix proportion optimization suggestion containing the material components. The method has the advantages that non-contact rapid measurement of concrete working performance parameters is achieved, precision and efficiency are improved, and mix proportion optimization suggestions are provided.

The invention relates to the technical field of enterprise finance and taxation risk management and control, and discloses an enterprise finance and taxation integrated risk management and control platform based on multi-source data fusion. The platform collects multi-source heterogeneous finance and taxation data in real time through a finance and taxation data collection module, and a data fusion preprocessing module generates a fusion data cube by using federal learning and a cross-domain data alignment algorithm. The risk feature modeling module constructs a multi-dimensional risk feature map based on a graph convolutional network and a dynamic Bayesian network, and the risk dynamic assessment module assesses risks in real time through an adaptive weighted ensemble learning algorithm and a risk conduction model. And the risk management and control decision module generates a management and control scheme by adopting a multi-objective optimization algorithm and a game theory strategy. In addition, the finance and tax data security storage module ensures data security. According to the platform, multi-source data fusion and efficient risk management and control are realized, risks can be accurately evaluated, scientific decisions are provided, enterprise finance and taxation data security is guaranteed, and enterprise finance and taxation management level is improved.

The invention discloses a metal cutting process parameter optimization analysis method based on machine learning, and particularly relates to the field of machine learning. Comprising multi-dimensional process parameter feature extraction and preprocessing, cutting state intelligent identification based on integrated learning, dynamic process parameter sensitivity analysis and weight calculation, process parameter intelligent optimization under a multi-target constraint condition, and adaptive parameter adjustment and real-time control strategy. According to the method, the interaction relationship between complex nonlinear features and process parameters in the cutting process is comprehensively captured, and accurate and intelligent recognition of different cutting states such as normal cutting, tool abrasion and abnormal flutter is achieved through a three-layer integrated learning architecture; the technical bottlenecks that an existing system lacks real-time self-adaptive adjustment capacity and is low in process optimization efficiency are overcome, pertinence and effectiveness of parameter adjustment are ensured, and the technical current situation that machining quality fluctuates and repeatability is poor due to traditional fixed parameters is changed.

The invention discloses a life prediction method based on health index construction and neural network fusion, and belongs to the technical field of equipment state monitoring and predictive maintenance. According to the method, through multi-source degradation feature extraction, common dynamic principal component analysis (CDPCA) dimensionality reduction, health index construction and normalization, deep learning multi-model modeling, integrated learning fusion and Bayesian optimization hyper-parameter optimization, online health assessment and residual life prediction of the equipment part degradation process are realized. Specifically, the method comprises the following steps: firstly, extracting time domain, frequency domain and time-frequency domain features from a sensor acquisition signal, and performing dimension reduction through CDPCA to obtain effective degradation characterization; then, weighting the main features to construct a health index (HI) curve, optimizing the weight through a genetic algorithm, and then performing normalization; a plurality of neural network models such as CNN, Bi-GRU, Bi-RNN, Bi-LSTM and SRNN are constructed based on the normalized HI sequence, and degradation trend modeling is realized; inputting the output results of the neural networks into an integrated learning module for fusion optimization; and finally, carrying out automatic optimization on the key hyper-parameters of the model by utilizing Bayesian optimization. In the equipment operation process, a normalized HI curve can be calculated in real time and input into the fusion model, and the residual life estimation value of the part is dynamically output. According to the method, high-precision, high-robustness and online life prediction can be provided under complex working conditions, the safety and reliability of equipment operation and maintenance are improved, and the method has wide engineering application value.

The invention discloses a power load prediction method based on heterogeneous ensemble learning and an attention mechanism. The method comprises the following steps: acquiring historical data of a power load, the historical data at least comprising time sequence data; preprocessing the historical data to obtain processed feature data; based on the feature data, an integrated learning model is constructed, and the integrated learning model at least comprises a combination of multiple base learners; predicting the feature data through the integrated learning model to obtain a preliminary prediction result; and introducing an attention mechanism to carry out weighted adjustment on the preliminary prediction result, and generating a final power load prediction value. According to the method, the nonlinear and complex modes of the power load are effectively captured, the prediction performance is remarkably improved, and accurate decision support is provided for intelligent operation of a power system.

The invention discloses an urban road traffic entrance and exit influence evaluation method based on big data, and relates to the technical field of urban traffic management. Constructing an urban road traffic network topological graph based on the traffic feature vectors, proposing a dynamic weight graph embedding algorithm, and establishing a road network association mapping model; applying a graph neural network algorithm based on an attention mechanism to the road network association mapping model, performing road node influence factor evaluation, and quantitatively analyzing the road entrance and exit influence degree; fusing influence factor evaluation results, and constructing a multi-dimensional traffic influence evaluation model by adopting a cross-domain ensemble learning method; and according to a performance evaluation result of the multi-dimensional traffic influence evaluation model, generating urban road traffic entrance and exit optimization decision suggestions through an intelligent recommendation algorithm, and completing accurate scheduling of traffic network nodes. The intelligent recommendation algorithm is developed based on reinforcement learning, and reliable optimization suggestions are provided for traffic management decisions.

The invention discloses a cardiovascular disease risk prediction method and system based on dietary multi-modal data and ensemble learning, and the method comprises the steps: constructing a multi-modal set through integrating multi-source heterogeneous data such as demographic statistics, dietary nutrition, clinical physiological and biochemical indexes and lifestyles; data cleaning is completed based on a box plot method and missing value processing, and key features are screened through Pearson's correlation coefficients, variance expansion factors and feature importance evaluation; a plurality of heterogeneous base models are fused by adopting a Stacking integration framework, a meta-feature matrix is generated through five-fold layered cross validation, and multi-level decision fusion is realized through a logistic regression meta-model; and quantifying the contribution weight of the dietary characteristics to the risk in combination with an SHAP method, and generating a visual interpretation chart and personalized intervention suggestions. According to the method, the accuracy and the stability of a prediction result are remarkably improved, the contribution degree and the action mechanism of dietary factors and other characteristics to the prediction result can be deeply analyzed, powerful support is provided for accurate prevention and personalized treatment of cardiovascular diseases, and the method has good practical value.

The embodiment of the invention provides a method and device for identifying fire protection hidden dangers through an AI visual analysis technology, and the method and device achieve the integration of image video streaming, three-dimensional space, thermal imaging, environment perception and other multi-modal data through the innovative construction of a multi-source data collection and fusion mechanism. And designing a feature extraction model based on transfer learning, and realizing high-precision hidden danger feature recognition through integrated learning in combination with a hierarchical classification structure and an attention mechanism. Time sequence analysis and space positioning technologies are introduced, a hidden danger feature association network and an evolution model are constructed, and hidden danger development trend prediction and common hidden danger discovery are achieved. According to the method, the defects of the traditional technology in the aspects of multi-modal data processing, feature recognition, trend prediction and the like are effectively overcome, and the intelligent level and the early warning capability of fire protection hidden danger recognition are remarkably improved.

The invention relates to the technical field of equipment anomaly detection, and discloses an anomaly detection method and system for an intelligent motor, and the method comprises the steps: collecting multi-source heterogeneous data when the intelligent motor works, carrying out the feature-level fusion, and carrying out the potential local learning, and obtaining optimized data features; performing data fragment segmentation on the optimized data features to obtain segmentation features, and determining a time-frequency feature spectrum of the segmentation features under multiple resolutions; mining a feature association relationship of the multi-dimensional operation features of the intelligent motor to construct a reinforcement learning strategy, and performing target feature screening on the multi-dimensional operation features of the intelligent motor to obtain a screened feature subset; and carrying out confrontation generation processing on the screening feature subset to obtain a simulation sample, carrying out feature ensemble learning processing on the simulation sample and the screening feature subset to obtain a judgment feature, and carrying out anomaly analysis on the intelligent motor to obtain an anomaly detection report. According to the invention, the detection precision of non-obvious or weak-feature anomalies in the intelligent motor can be improved.

The invention relates to the technical field of knowledge processing, and particularly discloses a knowledge proposition error correction method and system based on knowledge graph optimization upgrading, and the method comprises the steps: extracting effective information from multi-source knowledge data through employing entity linking, relation extraction and attribute alignment methods, and constructing an initial knowledge graph. Through semantic analysis based on deep learning, grammar, semantic and logic analysis is carried out on knowledge propositions, and a proposition element triple is generated. A graph neural network is utilized to encode triples, proposition vectors and neighborhood feature vectors are obtained, structural similarity is calculated accordingly, and proposition errors are detected in combination with rules and a statistical method. An integrated learning algorithm is adopted to classify error traceability, and an error correction decision is made in combination with multi-source evidence, so that an accurate error correction result is obtained, and the accuracy and reliability of knowledge propositions are improved; the error correction process is more logical and reliable, blind error correction is avoided, and the quality and efficiency of knowledge proposition error correction are improved.

The invention discloses a government affair data automatic classification and grading method, and relates to the technical field of data management and information processing. According to the method, data of different sources and formats are converted into structured formats by adopting a regular expression and a pattern matching technology, and the consistency and accuracy of the data are improved by means of a machine learning algorithm, data quality detection, anomaly correction and the like; by constructing a government domain ontology and a knowledge graph and combining a natural language processing technology, semantic classification is effectively carried out on government affair data, overlapping and uncertainty between categories are solved, robustness and accuracy of classification decision are further improved through an integrated learning algorithm, high efficiency and reliability of classification are ensured, and the method is suitable for large-scale popularization and application. By establishing a data grading dynamic adjustment and update mechanism and combining real-time data monitoring, sensitivity evaluation and automatic adjustment of access control rules, the flexibility and security of data management are ensured.

The invention discloses a big data-based internet equipment fault prediction method, which comprises the following steps of: acquiring operation data, environment data and historical fault records of internet equipment, and performing data cleaning, normalization processing and feature fusion processing on the operation data, the environment data and the historical fault records to generate a training data set; substituting the training data set into an Internet equipment fault prediction model constructed based on an integrated learning algorithm for learning training; dynamic feature extraction is carried out on real-time data flow of the Internet equipment through a sliding window mechanism, fault prediction model parameters are updated in an incremental learning mode, and a fault risk prediction result about the Internet equipment is output online; and generating a graded early warning signal according to a prediction result, and linking the automatic operation and maintenance system to execute a predefined maintenance strategy. According to the method, through multi-source data fusion and efficient model training, sliding window incremental learning and an automatic operation and maintenance strategy are combined, and the dynamism, accuracy and reliability of Internet equipment fault prediction are achieved.

The invention belongs to the technical field of landslide susceptibility analysis, and relates to a landslide susceptibility ensemble learning evaluation method considering spatial heterogeneity partitioning and factor feature screening, which comprises the following steps: generating a landslide sample based on historical landslide catalog data, and selecting a non-landslide sample through environmental factor frequency ratio analysis; the method comprises the following steps of: extracting static and dynamic environment factor data sets, realizing factor space interpretation force transformation by utilizing a t-SNE-ISO clustering algorithm and a feature screening strategy, eliminating high-correlation factors through a Pearson correlation coefficient method, quantifying interpretation force of each factor on landslide space differentiation by combining a geographic detector, screening optimal feature combinations under global and partition frameworks respectively, and performing landslide space differentiation on the landslide space. According to the method, a Stacking integrated learning framework is combined with CNN, DNN, MLP-based learners and LR element learners, a landslide susceptibility probability prediction model is formed, the generalization ability and prediction accuracy of the model are improved, and the method is especially suitable for landslide high-incidence areas with severe topographic relief and complex geological conditions.

The invention relates to a machine learning-based scouring damage bridge buckling load prediction method and system, and belongs to the technical field of machine learning and structural engineering. The prediction method comprises the steps of obtaining original data, preprocessing the original data, constructing a LightGBM machine learning model, and evaluating and verifying the constructed model by adopting multiple performance evaluation indexes. According to the method, key variables such as bridge structure parameters and foundation soil physical property indexes are comprehensively considered, a large sample database covering a large number of scouring combinations is constructed, and the critical buckling load of the structure is accurately calculated as an output target by using a pier-bearing platform-pile structure system buckling analysis method after scouring damage based on an energy method. A multivariable nonlinear mapping relation is established in combination with an ensemble learning algorithm, traditional complex calculation is replaced, and the method has good engineering adaptability and universality and can be suitable for rapid prediction of the buckling bearing capacity under different parameter bridges, different foundation forms and complex scouring conditions.

The invention discloses a dam safety monitoring method and system based on a knowledge association structure, and the method comprises the steps: collecting the multi-source heterogeneous data of a dam, and obtaining a dam safety knowledge graph; time sequence data in the dam safety knowledge graph are preprocessed, a deep learning model is adopted for modeling and analysis, anomaly detection is carried out, and potential safety hazards of the dam are recognized; the method comprises the following steps: constructing an integrated learning model in combination with multiple machine learning algorithms, performing fault diagnosis on potential safety hazards of a dam, generating dam early warning information, feeding back the dam early warning information to a dam safety knowledge graph, dynamically updating the dam safety knowledge graph in combination with historical data, and performing dam safety monitoring. Based on comprehensive integration of dam safety monitoring data, through modeling and analysis of the time series data by the deep learning model and data-based fault diagnosis of the model algorithm, the accuracy and robustness of dam fault diagnosis are improved, and safety monitoring and early warning of the dam are optimized.

The invention discloses an intelligent wake-up system and method for a mobile hard disk in a low-power-consumption mode, particularly relates to the technical field of wake-up control of the mobile hard disk, and is used for solving the problem that the mobile hard disk cannot accurately recognize a wake-up request in the low-power-consumption state. According to the method, the wakeup request in the low-power-consumption mode of the mobile hard disk is classified and identified, the operation type, the system running state and related behavior information are extracted, the multi-modal feature vector is constructed, and the request validity is intelligently predicted based on the integrated learning model, so that the wakeup judgment accuracy is improved, and the wakeup efficiency is improved. For the request with the prediction result in the uncertain interval, a short-time semi-activation window period is set, and secondary confirmation is performed by monitoring the host access behavior in real time, so that energy consumption waste and equipment loss caused by false wake-up are effectively avoided, self-adaptive response to the state change of the mobile hard disk is realized, and the service life of the mobile hard disk is prolonged. And the low-power-consumption intelligent control level and the energy-saving management capability are improved.

The invention relates to the technical field of meteorological prediction, in particular to a meteorological prediction precision improvement method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: constructing a plurality of initial weather prediction models based on a Transform structure in combination with a PIDL (Precision Independent Design Language) algorithm; taking the power curve of the wind turbine generator and the performance characteristic curve of the photovoltaic module as constraint conditions, and integrating the constraint conditions into each initial meteorological prediction model to obtain a plurality of basic meteorological prediction models; based on a preset automatic hyper-parameter tuning tool, performing hyper-parameter optimization on each basic meteorological prediction model in combination with a cross validation algorithm; fusing the plurality of basic meteorological prediction models after hyper-parameter optimization by adopting an integrated learning algorithm to obtain a target meteorological prediction model; and according to the meteorological characteristic data and the target meteorological prediction model, the target meteorological prediction result is determined, and the meteorological prediction precision is improved.

The invention provides an electric energy metering system operation state comprehensive evaluation method and system, and the method comprises the steps: receiving a real-time energy consumption data stream, carrying out the processing through an adaptive threshold anomaly detection algorithm and a Bayesian inference method, and generating a real error range estimation corresponding to each data point; generating a target prediction result by using the time sequence prediction model in combination with historical same-period data, seasonal factors and a reinforcement learning mechanism; comparing the target prediction result with the real-time energy consumption data flow to obtain a comparison result, and generating a comprehensive performance fluctuation evaluation result based on the comparison result and the multi-dimensional state space model; constructing a comprehensive evaluation index system by using an integrated learning algorithm and a fuzzy logic algorithm, and obtaining a comprehensive score corresponding to each ammeter by using the comprehensive evaluation index system; according to the method, the prediction accuracy of the electric energy metering system, the comprehensiveness of health condition evaluation and the scientificity of maintenance decision are improved, and support is provided for efficient management and optimal scheduling of an intelligent power grid.

The invention relates to the technical field of grain detection, and discloses a grain broken rice rate online detection method and system based on image analysis. A grain broken rice rate online detection method based on image analysis comprises the steps of intelligent illumination control and high-speed synchronous imaging, self-adaptive image enhancement based on reinforcement learning, multi-stage parallel particle detection and segmentation, multi-modal feature deep fusion, robust classification based on ensemble learning, intelligent quality control and process optimization. And continuously learning and updating knowledge. A clear image is obtained through a multi-angle linear array camera and a stroboscopic light source, a reinforcement learning agent is adopted to carry out adaptive image enhancement, a deep learning network is used to carry out particle detection segmentation, multi-dimensional features are extracted, accurate identification is realized through an integrated classifier, and a digital twin model is established to carry out process parameter optimization. According to the invention, accurate online detection of the broken rice rate of grains can be realized in a high-speed flowing state, and the detection efficiency and accuracy are improved.

The invention discloses a prostate cancer three-classification risk layering method based on an integrated learning model, and relates to the technical field of data processing and analysis. The method comprises the following steps: collecting clinical information and pathological data of a patient with increased PSA, and dividing the data into a training set and a test set; the training set is preprocessed, and prediction features are screened through LASSO regression; constructing a plurality of machine learning base models based on the features, and training and optimizing through cross validation; soft voting is constructed through an integration strategy, and an integration model is stacked; setting double thresholds according to the integrated model prediction probability, and establishing a layering rule; combining an integrated model and rules to form a three-classification model, and judging low, high and medium risks according to probabilities; and finally verifying the model diagnosis performance in the test set. According to the method, through cross-modal feature integration and ensemble learning, the method is PSAlt; accurate risk stratification is provided for 30 ng / mL people, biopsy decision-making efficiency is optimized, and excessive puncture and missed diagnosis risks are reduced.

The invention relates to the technical field of exploration, and discloses a water depth inversion method and system based on a multispectral remote sensing image, which utilizes an independent verification sample set to carry out hierarchical precision evaluation, and analyzes model performance differences according to dimensions such as a water depth range and a substrate type. Error propagation of links such as atmospheric correction and water level correction is quantified through full-chain uncertainty analysis, an uncertainty quantification model of ensemble learning is constructed, and a pixel-level precision distribution diagram is generated. Therefore, according to the technical scheme, a closed-loop dynamic adaptation framework is formed by systematically integrating multi-temporal data dynamic modeling and an uncertainty quantification mechanism, a spatio-temporal variation compensation mechanism is embedded in the whole process from data acquisition to result verification, the interference of spatial-temporal heterogeneity of water optical characteristics on water depth inversion is effectively dealt with, and the accuracy of water depth inversion is improved. The problems of parameter mismatch and precision reduction of the water depth inversion model caused by dynamic change of optical characteristics of a coastal water body in seasonal and tidal scales are solved.

The invention discloses a multi-source data fusion foam concrete construction process monitoring method and system, and relates to the technical field of concrete construction, the method comprises the following steps: obtaining a standard response data set of foam concrete, including raw material ratio parameters and performance index parameters which are stored in an associated manner; constructing and training a hybrid hierarchical performance prediction model, wherein the model comprises a shared prediction layer based on integrated learning and a plurality of independent output layers connected with the shared prediction layer; in combination with a multi-objective optimization algorithm, by taking compressive strength and cost optimization as an objective, constructing an evaluation function for global optimization, and obtaining a target mix proportion scheme; acquiring real-time process data associated with the target mix proportion scheme, and inputting the real-time process data into the mixing layering performance prediction model to obtain a prediction result; and comparing with a preset design target, and generating an adjustment instruction for adjusting the matching parameters of the subsequent stirring batches. The problem that in the prior art, the performance fluctuation of foam concrete of different batches is large due to the fact that the construction condition cannot be dynamically adjusted in real time is solved.

The invention provides a method for predicting the quality of a sparse small sample industrial process based on a stacked network model. The method comprises the following steps: collecting end point quality report data of an industrial production process; performing hierarchical processing on the acquired data according to the missing rate, and removing abnormal data in combination with a quartile method and production experience; generating a synthetic data expansion small sample data set by adopting a conditional generative adversarial network; obtaining a first-layer basic model based on an accumulated contribution rate screening method of an SHAP value; constructing a first layer of a stacked integrated learning model and adjusting hyper-parameters by using Bayesian optimization; constructing a Ridge meta learning device to integrate the output of the basic model and constructing a second-layer network; a six-fold cross validation training model is adopted; predicting performance through a multi-index quantitative model based on the test set; and verifying the prediction precision of the end-point phosphorus content and the temperature by using real converter production data. The method can realize high-precision prediction of the end point quality index of the complex industrial generation process, and is beneficial to ensuring the product quality and improving the production efficiency.

The invention relates to an electric charge recovery dynamic risk assessment method and system based on dual-channel ensemble learning and dynamic PID regulation and control. The prediction method comprises the following steps: selecting power data of a certain number of enterprises for analysis, and screening Top-15 high contribution features; the XGBoost and the LightGBM are fused by adopting a Stacking integration method, and the arrearage probability of the user is predicted; proposing a tail risk capture mechanism of quantile random forest regression, and predicting the arrearage amount of the arrearage user based on the arrearage probability output by the classification model; and a two-channel joint framework of a classification model and a regression model is constructed, and two-dimensional early warning of arrearage probability prediction and arrearage amount quantitative analysis is realized. A dynamic risk scoring mechanism is constructed according to the arrearage probability of classification prediction and the large-amount arrearage risk of regression quantification, upgrading from a single index to a comprehensive score is completed, a dynamic risk regulation and control mechanism based on PID is provided, and intelligent grading and dynamic early warning of arrearage users are achieved.

The invention relates to the field of power system planning, in particular to a micro-grid boundary quantitative evaluation method and system for multi-dimensional analysis and dynamic verification. The method comprises the steps that power distribution network and micro-grid scheme data are acquired, and scene recognition and decoupling modeling are carried out after standardization processing; the method comprises the following steps: extracting end supply-preserving scene data, analyzing cost elements, and generating a critical cost threshold table through normalization processing and a threshold approximation algorithm; performing multi-dimensional parameter correlation analysis and integrated learning training based on the table, and constructing a multi-dimensional boundary index model; performing benefit matching calculation and green value accounting according to the result to generate an economic benefit decomposition structure; real-time streaming data processing and stability verification are combined to generate an economical efficiency boundary index set; and finally, generating a standardized evaluation file through matrix mapping and weight dynamic adjustment. According to the method, dynamic quantitative evaluation of the economy boundary of the micro-grid is realized, the capacity substitution benefit and the green value are effectively integrated, and an accurate basis is provided for planning decision.

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

The invention discloses a multi-modal data fusion and intelligent analysis platform and method for an unmanned aerial vehicle inspection system for a wind power plant. The platform comprises a multi-modal data acquisition module, a feature extraction and standardization module, a multi-modal information fusion module, a joint learning and optimization module, a domain knowledge injection module and an intelligent decision and application module. The system processes multi-source heterogeneous data through an integrated learning and deep learning fusion strategy, projects features to a shared semantic space by using joint training and comparative learning to enhance the anomaly discrimination ability, and performs verification and semantic enhancement on a supervised retrieval result in combination with a knowledge base in the wind power field. And finally, outputting a high-reliability diagnosis report and a maintenance suggestion. According to the invention, accurate identification and positioning of the fan fault are realized, and the inspection efficiency and the system decision reliability are significantly improved.

The invention discloses a coronary artery calcification early warning system for type 2 diabetes patients, and relates to the technical field of medical detection. A data acquisition module is used for acquiring continuous physiological parameter data of a user; the risk modeling module is combined with coronary artery calcification evolution characteristics in historical clinical samples to construct a multi-parameter dynamic association model; an index weight calculation unit generates a risk influence factor vector based on a sensitivity analysis result of the physiological indexes on risk prediction; the machine learning analysis module performs iterative training on the prediction model by adopting an integrated learning algorithm, and performs prediction updating by utilizing a risk influence factor vector; the early warning trigger module dynamically generates a graded early warning signal according to the grading trend and a set threshold value; the weak item positioning module carries out contribution degree analysis and anomaly recognition on the key risk indexes and automatically generates personalized intervention suggestions; according to the invention, early recognition and dynamic early warning of coronary artery calcification progress can be realized, and the method is suitable for intelligent early warning management scenes of chronic disease cardiovascular risks.