466 results about "Relevance analysis" patented technology

The invention discloses a light industry supply chain multi-modal data fusion analysis method based on deep learning, and the method comprises the following steps: carrying out the cleaning and standardization processing of text, image, audio and video data collected in a supply chain environment, and constructing a standardized multi-modal data set; then, a special feature extraction network is adopted to generate each modal feature vector, and a feature incidence matrix is constructed through cross-modal correlation analysis; feature weights are dynamically adjusted in combination with a domain knowledge rule base, multi-modal feature interaction is achieved through a cross-modal attention fusion network, and unified fusion features are generated through a self-attention mechanism; and finally, constructing a supply chain decision model, and mapping the fusion feature into a supply chain state evaluation result and an optimization parameter. According to the method, knowledge rule constraint and a deep attention mechanism are fused, supply chain situation awareness precision and decision reliability can be effectively improved, and technical support is provided for intelligent management of the light industry supply chain.

The invention relates to a water conservancy equipment service life prediction and fault monitoring method. The water conservancy equipment service life prediction and fault monitoring method comprises the steps of performing comprehensive digital modeling on water conservancy equipment, determining a fault sensitive area and a key monitoring point, generating a sensor deployment scheme, preprocessing analog and simulated sensor data, and ensuring that the data is comprehensive and targeted; performing multi-domain fusion feature extraction, deep feature learning and correlation analysis on the data, screening out important features to form a feature subset, and capturing equipment fault features in all directions; a mixed life prediction model is constructed, parameter initialization, pre-training and formal training are completed, a multivariate Gaussian mixture model and a deep belief network are constructed, and then a fault monitoring model is obtained; sensor data arranged according to a deployment scheme is obtained in real time, features are extracted after preprocessing, real-time feature vectors are input into a life prediction model and a fault monitoring model respectively, life prediction and fault monitoring of the water conservancy equipment are achieved, powerful decision support is provided for equipment maintenance, reliable operation of the water conservancy equipment is guaranteed, and fault loss is reduced.

The invention belongs to the field of data analysis, and discloses a data analysis method for engineering consultation digital intelligent management, which comprises the following steps: mapping multi-project heterogeneous data to a unified three-dimensional space-time grid through space-time grid division to form a grid mapping data set; detecting data format and attribute conflicts based on neighborhood correlation analysis, and distributing calibration data through dynamic weight; missing values and abnormal fluctuations are processed by using a time sequence autoregression model and a smoothing technology, and a complete data set with continuous time is constructed by combining linear interpolation and multi-dimensional data fusion; key indexes are extracted to construct a digital mirror image containing spatial positions, time sequences and attribute association, real-time data are synchronized, and outlier grid points are corrected; and multi-scene operation conditions are constructed based on the mirror image data, and a dynamic adjustment scheme is realized. According to the method, heterogeneous data are integrated through space-time grids, digital twinning and multi-scene simulation technologies are combined, and full-period management and risk intelligent decision-making of engineering data are achieved.

The invention relates to a cotton field carbon sink dynamic evaluation method and system based on multi-source data fusion. The method comprises the following steps: firstly, comprehensively collecting multi-source data of a cotton field area environment, and processing the multi-source data by using a time window alignment technology to generate a multi-source heterogeneous data set; based on the data set, key features are extracted through a data mining means, and a key influence factor weight table is generated; and then, inputting the key influence factor weight table and the multi-source heterogeneous data set into the trained optimization evaluation model together to obtain cotton field carbon sink space-time distribution characteristics. And finally, carrying out correlation analysis on the spatial-temporal distribution characteristics and meteorological continuity data, and generating a curve capable of visually displaying the dynamic change of the cotton field carbon sink along with time. The method overcomes the limitation of a single data source, greatly improves the accuracy and comprehensiveness of cotton field carbon sink evaluation, can accurately reflect the real state of cotton field carbon sink under different time-space conditions, and provides a key basis for agricultural managers to formulate scientific and reasonable cotton field planting management strategies.

The invention relates to the technical field of deep learning, in particular to an AI chip test parameter adaptive optimization method based on deep learning, which comprises the following steps: acquiring historical test data of an AI chip, and calculating correlation strength among different failure modes based on the historical test data; identifying a failure coupling matrix according to the edge weight, and converting a preset static detection parameter constraint boundary into a dynamic constraint space changing along with a failure detection state; a multi-level optimization framework is constructed, the upper layer executes failure type correlation analysis and generates constraint propagation information, the middle layer optimizes a parameter cluster based on the constraint propagation information, and the lower layer adjusts a single detection parameter and outputs a parameter optimization result; establishing a neural network mapping model, and obtaining a nonlinear mapping relationship between the detection parameters and the failure types; based on the physical state parameters, the nonlinear mapping relation is adjusted, the dynamic constraint space is updated, parameter optimization is executed again, a parameter optimization result is output, and an optimal test parameter combination is output.

The invention relates to the technical field of power system measurement, and discloses a current transformer error dynamic monitoring method and system.The current transformer error dynamic monitoring method comprises the steps that current transformer time sequence data and a system event log are obtained; constructing a time sequence causal graph to represent the time correlation between the event and the error change; identifying potential causal links by applying a counter causal model; designing a multi-world simulation engine to generate an anti-fact scene; quantifying a causal effect by comparing actual observation with an anti-fact simulation result; establishing a monitoring mechanism to track key trigger events in real time; generating a dynamic causal interpretation report and adjusting a compensation strategy; according to the method, the limitation of traditional correlation analysis is broken through, the causal relationship and the correlation can be accurately distinguished, the real triggering factor of the error change of the current transformer can be accurately identified, the false alarm rate and the missing report rate are reduced, and the accurate dynamic monitoring of the error of the current transformer is realized.

The invention discloses a common concrete and ultra-high performance concrete performance prediction and mix proportion multi-objective optimization design method and system, and the method comprises the steps: obtaining a comprehensive data set which covers the mix proportion, performance data and cost data of common and ultra-high performance concrete; correlation analysis is carried out, and influence factors strongly correlated with the target variable are determined; constructing a concrete performance prediction model by using a machine learning algorithm, training and testing, then carrying out interpretability analysis, and selecting an optimal concrete performance prediction model; constructing a mix proportion optimization design objective function and an ultra-high performance concrete cost function; establishing a multi-objective optimization design mathematical model in combination with constraint conditions, a cost function and an optimization objective function; and solving by adopting a multi-objective evolutionary algorithm, generating an approximate Pareto frontier solution set, and determining an optimal mix proportion optimization design scheme. According to the method, the concrete performance can be predicted, the mix proportion with the optimal comprehensive performance and cost effectiveness is obtained, and multi-objective optimization design of the concrete mix proportion is achieved.

Provided in the present invention are a method and system for real-time update of a medical knowledge base, and a medium and a device. The method comprises: acquiring medical literature data in real time; analyzing the medical literature data, so as to obtain first structured data; performing correlation analysis on the first structured data and second structured data in a medical knowledge base, so as to obtain an analysis result; on the basis of the analysis result, determining whether to update the medical knowledge base; identifying the differences of the medical knowledge base before and after the update, so as to obtain a difference identification result; assigning a unique version number to each difference in the difference identification result, so as to obtain version update records; and on the basis of the version update records, using incremental hash table technology to perform data storage for each difference. The present application has a higher update efficiency without the risk of omission; and by means of difference analysis and by using incremental hash table technology to store difference data, the present application significantly reduces data storage space requirements, and also improves data access speed.

The invention is suitable for the field of abnormal data monitoring, and provides an abnormal data monitoring method and system based on artificial intelligence, and the system comprises a real-time data flow collection module, a cross-level correlation analysis module, a dynamic baseline model building module, an abnormal behavior comparison module, and an abnormal traceability module. According to the method, through real-time data acquisition, cross-hierarchy analysis, dynamic modeling, anomaly detection and deep threat tracing, an efficient and intelligent anomaly monitoring closed loop is constructed, and remarkable beneficial effects are presented. According to the method, firstly, real-time data flow capture and cross-level correlation analysis are utilized to ensure that interaction behaviors of multiple protocol levels in a network are comprehensively covered, and a complex abnormal mode is recognized in combination with context information; according to the multi-dimensional analysis, the detection accuracy is remarkably improved, false alarm and missing alarm are effectively reduced, meanwhile, an abnormal mark data set with weights is generated, and high-quality data support is provided for follow-up processes.

The invention relates to an offshore wind power prediction method, and aims to improve the accuracy and reliability of prediction. The method comprises the following steps: (1) a data preprocessing stage: detecting an abnormal value in data by using a DBSCAN clustering algorithm, reconstructing the abnormal value by using a KNN interpolation method, detecting time sequence abnormity through an LSTM automatic encoder, and performing regression reconstruction by using LSTM to ensure the retention of time sequence features; (2) a feature engineering stage: screening out key features through correlation analysis, generating a label column through K-means clustering and wind direction classification, and extracting features such as wind direction change rate, time periodicity and wind speed interaction; and (3) a model construction stage: constructing a composite model in combination with multi-scale convolution (Inception), a bidirectional long short-term memory neural network (BiLSTM) and a multi-head self-attention mechanism, extracting local features through a convolution layer, capturing a time dependency relationship through a bidirectional LSTM layer, enhancing the attention of key features by using the multi-head self-attention mechanism, and finally realizing high-precision wind power prediction.

A photovoltaic power generation few-sample transferable prediction method based on a large language model comprises the following steps: carrying out extreme value normalization and block cutting on photovoltaic time sequence data to generate an embedded module adaptive to the input of the large language model; fusing high-order partial correlation analysis and a gating residual network to dynamically allocate feature weights, and extracting space-time dependence and nonlinear correlation features in the multi-dimensional data; mapping the structured time sequence features to a large language model semantic space, and realizing high-fidelity alignment with a pre-training corpus by using a mask multi-head attention mechanism; generating a cue word prefix containing a data background, a domain mode and statistical characteristics based on a preset template, and guiding a large language model to accurately analyze time sequence semantics; and generating a natural language prediction result through the pre-training large language model of the frozen parameters, and finally performing inverse analysis through linear projection to obtain a numerical power generation prediction value. The method can effectively solve the problems that a traditional model is insufficient in generalization ability and poor in cross-station migration prediction stability in a data scarcity scene.

The invention relates to the technical field of data transmission of the Internet of Things, in particular to a self-adaptive data compression and transmission method of a low-power-consumption wide-area Internet of Things. According to the method, a three-layer collaborative architecture comprising an equipment layer, a fog computing layer and a cloud computing layer is constructed, data preprocessing and feature analysis are performed on the equipment layer, and data types, entropy values and repeatability information are extracted; the fog calculation layer selects an optimal compression algorithm based on a multi-dimensional decision engine, and reduces redundancy through spatial-temporal correlation analysis and data aggregation; the cloud computing layer collects compression performance data, adopts reinforcement learning and federal learning to train a global optimization model, and dynamically issues strategy parameters; and the fog node adaptively adjusts a compression strategy in combination with the system state to realize the optimal balance between the compression ratio and the reconstruction precision. The method is suitable for field deployment environments with limited electric quantity and network, has the advantages of low power consumption, high efficiency, strong adaptability and the like, and can be widely applied to Internet of Things scenes such as remote monitoring, smart energy and the like.

The invention discloses a method for dynamically predicting the underground water level during excavation of an overtopping ground layer foundation pit, and relates to the technical field of dynamic prediction of the underground water level, which comprises the following steps of: acquiring multi-source data related to the underground water level in an engineering range; comprising information of soil moisture content, stratum permeability coefficient, rainfall capacity, foundation pit excavation depth, foundation pit dewatering scheme and foundation pit waterproof and drainage measures; historical underground water level, underground aquifer data and Yangtze River water level change information are obtained. According to the method, data quality is optimized through multi-source data acquisition, correlation analysis and z-score standardization, nonlinear modeling is realized in combination with a support vector machine (SVM) and Bayesian optimization, and model precision and generalization ability are improved. Model parameters are dynamically adjusted through a real-time monitoring and feedback optimization mechanism, the adaptability to sudden hydrological changes is enhanced, engineering risks are effectively reduced, and construction safety and economical efficiency are guaranteed.

The invention discloses a heavy duty gas turbine compressor scale deposition detection method based on a liquid neural network, and relates to the technical field of anomaly detection, and the method comprises the following steps: collecting operation data for correlation analysis; the selected feature data is preprocessed; extracting time characteristics of the operation parameters of the gas compressor; calculating weights of interaction among the parameters, and outputting spatial correlation; constructing a space-time fusion model, and inputting normal operation data to train the model; and a parameter prediction sequence is output, a multi-dimensional residual vector of a predicted value and an actual value is calculated, and by constructing a dynamic threshold function, when the model converges and residual distribution deviates from a normal working condition confidence interval, a gas compressor fouling anomaly detection result is output. According to the invention, through the dynamic topological structure of the liquid neural network and in combination with a spatial-temporal characteristic collaborative mining mechanism, quantitative evaluation of the scale deposition degree of the gas compressor is realized, and the precision and robustness of gas compressor scale deposition anomaly detection are improved.

The invention relates to the technical field of data processing, and particularly provides an address text correlation analysis method, system and device and a storage medium, and the method comprises the steps: receiving a target address text; based on the semantics of the target address text, retrieving a plurality of candidate standard address texts from a pre-constructed address knowledge base; on the basis of the target address text and the retrieved candidate standard address text, a cue word is constructed, and the cue word comprises an instruction used for guiding a large language model to conduct address relevance judgment and context information; and inputting the cue word into a large language model trained in an address field, and obtaining and analyzing the output of the large language model to determine a standard address text associated with the target address text and the association degree thereof. According to the method, the domain knowledge base and the large language model are fused, so that the accuracy and practicability of address correlation analysis are remarkably improved.

The invention discloses a multi-source data resource key information mining method and system, and the method comprises the steps: obtaining multi-source data of various forms and types, and carrying out the preprocessing; performing division according to data types; word frequency statistics and word correlation analysis are carried out on the character data, and numerical value meanings of the digital data are extracted through context semantic correlation; constructing a cross-modal key information data set based on the key information data set of each data type; designing a multi-branch long-short-term memory neural network model, inputting a cross-modal key information data set into the model for training, and generating key information mining models for different fields by dynamically adjusting network parameters; and selecting to carry out key information real-time extraction and updating on the optimal model, generating a knowledge graph, and carrying out visual operation. The method has the advantages that through multi-source data fusion and processing, the long-short term memory neural network model is combined, the knowledge graph is generated, and domain-specific key information mining and visualization operation are efficiently carried out.

The invention relates to a correlation analysis method and device for computing power resources of a data center and energy consumption of refrigeration equipment, and the method comprises the steps: obtaining related data of the computing power resources of the data center and the energy consumption of the refrigeration equipment, carrying out the correlation analysis of the preprocessed related data, and obtaining key parameters related to the computing power resources of the data center and the energy consumption of the refrigeration equipment; selecting a model according to the key parameters and the modeling target, training the selected model by using historical data to learn the correlation between the computing power resource and the energy consumption of the refrigeration equipment, and evaluating the performance index of the selected model; according to the modeling target and the constraint condition, an optimization algorithm is selected to carry out joint optimization logic of the data center computing power resources and the refrigeration equipment energy consumption, the optimization algorithm is operated, and distribution of the data center computing power resources and operation parameters of the refrigeration equipment are adjusted through iteration; and obtaining a performance index for evaluating the correlation between the computing power resource of the data center and the energy consumption of the refrigeration equipment. According to the method, the prediction accuracy, generalization ability and interpretability of the model are remarkably improved.

The invention belongs to the technical field of traffic pollution prediction, and particularly relates to a LightGBM-based multi-source data fusion pollutant concentration prediction method, which comprises the following steps of: 1, integrating and processing multi-source data, and constructing a plurality of feature sets; and 2, carrying out feature analysis on meteorological, pollutant and traffic index variables through correlation analysis and time sequence analysis, and providing support for modeling and model interpretation. And step 3, dividing a training test set according to a time sequence, and training and optimizing the LightGBM pollution prediction model through parameter tuning by taking minimization of RMSE as a target. And 4, verifying the performance of the model from prediction precision, spatial distribution and wind direction influence. And 5, analyzing and displaying the key driving factor and quantifying the contribution of the key driving factor. According to the invention, through feature engineering processing and model optimization of heterogeneous data such as weather and traffic, high-precision prediction and influence factor analysis of lane-level pollutant concentration are realized.

The invention provides a crop population three-dimensional reconstruction and organ phenotypic character analysis method, and relates to the field of three-dimensional reconstruction, and the method comprises the steps: collecting cotton envelope image data, carrying out the data preprocessing, and carrying out the motion recovery processing and Gaussian sputtering of the cotton envelope image data, the method comprises the following steps of: preprocessing a group cotton point cloud, removing a ground point cloud, obtaining a complete cotton group point cloud model, segmenting a cotton individual point cloud model and a corresponding organ point cloud model from the complete cotton group point cloud model, automatically analyzing cotton key phenotypic characters, and performing correlation analysis. According to the method, a cotton original habitat three-dimensional point cloud model is constructed based on mobile visual equipment, and key characters such as cotyledon node height, plant height and leaf area can be automatically analyzed, so that the problems that phenotypic character extraction in the prior art is high in cost, low in flux, easy to make mistakes, inconsistent in standard, generally destructive, labor-consuming, time-consuming and the like are solved.

The invention discloses a multi-dimensional integrated power system fault detection algorithm, and relates to the technical field of power system fault detection, and the algorithm comprises the steps: S1, data collection and preprocessing, S2, correlation analysis, S3, mean square error calculation, S4, extended Kalman filter application, S5, principal component analysis dimension reduction, and S6, fault detection and visualization. According to the fault detection algorithm based on the multi-dimensional integrated power system, multiple correlation analysis methods are integrated, the operation state of the power system is analyzed from multiple angles, the fault detection accuracy can be improved, different correlation analysis methods can capture different types of relations among power parameters, and the fault detection accuracy is improved through the comprehensive analysis result. By means of the method, faults in the system can be recognized more comprehensively and more accurately, the situation of misjudgment or missed judgment caused by limitation of a single analysis method is avoided, and therefore stable operation of the power system is guaranteed.

The invention relates to the technical field of data processing, in particular to a data mining system based on artificial intelligence. According to the method, a finer computing resource regulation and control mechanism is constructed through links of inter-task correlation analysis, load task priority adjustment, computing cost evaluation, abnormal task identification, resource use trend analysis and the like. A topological structure is constructed for the relevance between the tasks through a graph neural network, and fuzzy matching is performed in combination with the computing resource sharing degree, so that automatic reasoning of the task dependency relationship is realized, and the intelligence of scheduling optimization is improved. The task priority adjustment adopts a mode of combining a CPU utilization rate and a calculation time proportion, load tasks are dynamically marked, a scheduling sequence is optimized in combination with a task time overlapping degree, and calculation resource conflicts are effectively reduced. According to calculation resource consumption analysis, task calculation load influence coefficients are calculated from multiple dimensions of task execution time, data reading amount and I / O operation times, and the reasonability of calculation resource allocation is improved.

The invention relates to a user power consumption mode identification method and device considering multi-factor association and a medium. The method comprises the steps of obtaining historical load data and historical meteorological data and performing preprocessing; processing the historical load data and the historical meteorological data under different time resolutions, and constructing a load data set and a meteorological data set under different resolutions; constructing a load curve of each day based on the load data set, performing coarse clustering on the load curves by using a self-organizing mapping neural network, extracting a power utilization mode of the user, and identifying whether the user has an energy storage device or not; calculating the relevance between the meteorological factors and the load through correlation analysis, and selecting key factors; and based on the selected key factors, performing fine-grained analysis on the power consumption mode of the user by adopting a mode of combining a time convolutional network and a pyramid attention mechanism, and accurately identifying the power consumption mode. Compared with the prior art, the method provided by the invention can realize the identification of the power consumption mode of the user with coarse and fine granularity under the multi-resolution condition and multi-factor association.

The invention discloses a driver trust degree prediction method and system considering environmental parameters and perceptual risks, and the method comprises the steps: building a takeover scene feature set through collecting actual environmental data, building a plurality of automatic driving takeover scenes based on the takeover scene feature set, carrying out the automatic driving takeover simulation test, and obtaining a driver trust degree prediction result under different automatic driving takeover scenes. Taking over multi-modal data in the process; performing feature extraction on the multi-modal data, and quantifying subjective feeling features; performing feature screening by calculating correlation between subjective feeling features and objective feature sets and between subjective feeling features and takeover scene feature sets, and constructing and training an LSTM algorithm model group through a plurality of feature sets; according to the method, feature extraction is performed on environment data in an actual takeover scene through a correlation analysis algorithm and a clustering algorithm, a highly realistic simulation environment is constructed, multi-modal data of a driver is acquired, multi-dimensional data support is provided for an LSTM algorithm model group, and the reliability of a prediction model is improved.

The invention relates to the technical field of sports event content processing, in particular to a sports event content comprehensive processing method based on artificial intelligence, which comprises the following steps: generating a plurality of popularity events from sports event content data propagated by a network through a popularity word mining model based on improved BERTopic; constructing a preheating degree event weight and a popularity index weight for the plurality of popularity indexes corresponding to the plurality of popularity events through an expert evaluation method; calculating and adjusting the weight of the preheating degree event according to the time sequence data of the multiple heat indexes through a correlation analysis model based on grey correlation analysis; and the evaluation matrix, the time sequence popularity event weight and the popularity index weight are used to determine a comprehensive sports event attention event through a popularity event judgment model based on a fuzzy evaluation method. According to the method and the device, the hot events of the sports events can be timely and accurately determined, and the content of the sports events can be spread and popularized more in accordance with audience interests.

The invention discloses an indoor personnel trajectory tracking and anti-interference method based on a millimeter wave radar. The method comprises the following steps: firstly, dividing echo data into dynamic and static point cloud branches for respective processing; performing static clutter suppression on the dynamic point cloud branch, then performing speed dimension fast Fourier transform, and performing direct processing on the static point cloud branch to obtain a distance-Doppler spectrum; determining a target signal and a background clutter; carrying out point cloud screening by taking the point with the strongest energy as a spectrum peak center, and constructing compact representation of a target; and finally, predicting the target state of the current frame, and carrying out target track association. And carrying out track intersection judgment and recording an intersection state, and outputting a human body target position and a track. The method provided by the invention can dynamically adapt to human body sensing requirements in different scenes by combining various correlation analysis technologies, has relatively low calculation complexity and relatively high robustness, and is particularly suitable for human body sensing detection tasks in low-power-consumption application scenes. The system has the advantages of low cost, small size and low power consumption.

The invention discloses a summer rainfall sub-season prediction method and system fused with multi-scale deep learning, and the method comprises the steps: collecting multi-source weather forecast data and observation data, and carrying out the empirical orthogonal decomposition of the observation data, and obtaining a rainfall main mode and a mode sequence; performing multi-scale signal extraction on the observation data, and performing correlation analysis on the observation data and the modal sequence to obtain respective weight fields; constructing and training a deep learning model fusing a multi-pole attention mechanism and time sequence decomposition; inputting the forecast data into the trained model to carry out transfer learning, and optimizing the model; and substituting forecast data of preset time into the trained model to generate a high-quality summer rainfall sub-season forecast product. According to the method, the synergistic effect of sea, land and gas and the interaction of multi-scale signals are fully considered, the model is constructed based on an artificial intelligence method and a numerical model forecasting product, the sub-season forecasting skill of summer rainfall is effectively improved, and the method plays an important role in disaster prevention and reduction.

The invention relates to the technical field of mine gas disaster prediction, in particular to a gas concentration time sequence prediction method based on MSADBO-CNN-GRU-Attention. Firstly, dynamic Pearson correlation analysis is used, weighting is conducted on all indexes based on a time window of one hour, key factors leading gas concentration in different time periods are revealed, and therefore interference of irrelevant factors is weakened, and meanwhile the signal strength of the key factors is enhanced. Thirdly, performing local feature extraction on the data by applying a convolutional neural network (CNN), capturing multi-scale information, learning a time sequence data long-term dependency relationship and transmitting state information between time steps in combination with a gating cycle unit (GRU); an Attention mechanism is added to weight the hidden state output by the GRU so as to highlight the importance of key time nodes and improve the accuracy of multi-step prediction. And then, an improved sine algorithm, adaptive Gaussian-Cauchy mixed variation disturbance and a Bernoulli chaotic mapping improved dung beetle search algorithm are fused to obtain MSADBO, and then model hyper-parameters are globally and adaptively optimized. And finally, training the model, establishing a prediction model based on MSADBO-CNN-GRU-Attention, and verifying the performance of the prediction model by comparing the model. According to the gas concentration multi-index multi-step time sequence prediction method provided by the invention, parameter adaptability, feature mining and depth time sequence modeling are combined, the prediction performance of the gas concentration is improved, and a solution is provided for high-precision multi-step time sequence prediction of the gas concentration under complex working conditions.

The invention discloses a lithofacies intelligent identification method, device and equipment, a medium and a program product, and the method comprises the steps: building a sample data set through logging parameters, drilling parameters and historical drilling data corresponding to lithofacies labels, screening out a feature subset for modeling based on correlation analysis, and building a lithofacies identification model based on a Transform neural network; and training the constructed lithofacies identification model by using the sample data set to obtain an optimized lithofacies identification model, inputting the feature subset of the well to be identified into the optimized lithofacies identification model, and outputting a lithofacies prediction result of a corresponding depth. The intelligent lithofacies identification method relies on the Transform model, has high identification precision, high automation degree and strong generalization ability, can effectively integrate two types of information of logging data and drilling engineering data, provides a more comprehensive basis for lithofacies judgment, and can meet the requirement of real-time lithofacies identification while drilling.

The invention discloses a power demand data analysis method and system based on artificial intelligence, and belongs to the technical field of intelligent power grid and power system automation, and the method comprises the steps: collecting multi-source heterogeneous data, and carrying out the preprocessing of the data; analyzing a causal relationship among the multi-source data by using a causal discovery algorithm, eliminating irrelevant factors, selecting a final variable, and outputting a causal reasoning result; constructing a deep learning model, and outputting a prediction result; and optimizing a power dispatching strategy according to a prediction result. According to the method, through the time alignment and anomaly detection mechanism of the multi-source heterogeneous data, the problem of time sequence dislocation during multi-protocol data fusion is effectively solved, and the data quality is improved; in combination with a causal discovery algorithm and statistical test, a real causal relationship is accurately identified, and false correlation interference in traditional correlation analysis is eliminated; the time sequence neural network and the causal reasoning model are fused, the interpretability of the model is enhanced while historical data time sequence features are reserved, and the prediction robustness under the extreme working condition is remarkably improved.

The invention discloses a debris flow susceptibility evaluation method based on a progressive optimization framework. The debris flow susceptibility evaluation method comprises the steps of collecting and sorting basic data of a research area; selecting evaluation factors, and performing correlation analysis on the evaluation factors; dividing drainage basin units, constructing a frequency ratio model to carry out susceptibility evaluation, and selecting an optimal drainage basin unit; adopting semi-supervised learning to select negative samples, and constructing a machine learning data set; through comparison of different Boosting algorithms and hyper-parameter optimization algorithms, an evaluation model with the highest precision under a village and town scale is constructed through multiple evaluation indexes, and an SHAP technology is adopted, so that contribution of different evaluation factors to an evaluation model decision process is quantitatively analyzed. According to the method, accurate and explainable theoretical support can be provided for risk assessment of the geological disasters in the village and town areas through a progressive optimization framework method, and important practical reference can be provided for optimization of disaster management and prevention and control strategies, so that potential risks and economic losses are effectively reduced.