4713 results about "Outcome predictor" patented technology

The invention relates to a power equipment fault prediction method based on multi-source heterogeneous data and a twinborn model, and the method comprises the following steps: S1, obtaining multi-modal data related to the operation of power equipment, constructing a joint feature encoder through an attention mechanism in deep learning, and carrying out the joint modeling of multi-modal information, and obtaining a joint feature representation; s2, based on joint feature representation, adopting a dynamic twin model of self-supervised learning and fusion reinforcement learning, and dynamically updating the equipment health state; s3, constructing a complete-cycle prediction framework based on Transform, capturing long-term dependency and dynamic change characteristics in equipment operation, and identifying trigger factors of faults in combination with causal relationship analysis; and S4, based on the equipment health state and the fault prediction result, optimizing equipment operation parameters by adopting an intelligent operation and maintenance optimization engine, and dynamically generating an optimal operation and maintenance plan in combination with a genetic algorithm. The reliability and the operation efficiency of the power equipment are remarkably improved.

This application relates to the field of geological disaster early warning technology, and discloses a method for monitoring, predicting and warning of geological disasters based on artificial intelligence, including the following steps: data collection: collecting geological disaster monitoring data; data preprocessing: time alignment and missing completion of the collected data; spatiotemporal correlation matrix construction: analyzing the preprocessed monitoring data; feature extraction: extracting the nonlinear spatiotemporal characteristics of geological disaster monitoring data; time series prediction: outputting the geological disaster risk level in the future period; feedback optimization: based on the error between the prediction result and the actual observation data; warning release: generating geological disaster warning information based on the predicted risk level. Through data collection, preprocessing, feature extraction, prediction optimization and warning release, a complete geological disaster monitoring and prediction method is constructed, which integrates multi-source data, extracts key features and dynamically predicts risks, so as to ensure the accuracy of the model and provide a scientific basis for disaster prevention and control.

The invention provides a power prediction precision improvement method and system for a wind and light station, and relates to the technical field of new energy power generation, and the method comprises the steps: obtaining the historical power, weather and equipment operation data of the wind and light station through a data preprocessing module, and carrying out the time sequence decomposition, spatial interpolation, abnormal value detection and correction, and obtaining the fusion feature data. And constructing a deep learning prediction model based on the fused feature data, extracting spatial features by using a convolutional neural network, processing the spatial features by using a long-short-term memory network to obtain an initial prediction result, introducing an attention mechanism to perform weight allocation to obtain weighted features, and combining to obtain power prediction data. And judging the weather type according to the real-time meteorological condition, and correcting by adopting the matched correction model when the extreme weather is identified. And finally, an error compensation model is constructed based on historical prediction errors for compensation, a final power prediction result is obtained, error correction is performed according to actual power data, and compensation model parameters are updated.

The invention discloses an adapter life prediction system based on digital twinning and a dynamic early warning method. The system comprises a multi-source data acquisition module, a digital twinning model construction module, a data coordination module, a life prediction module and a calibration module. According to the method, the adapter full-life-cycle digital twins are constructed, the limitation of one-way static analysis of a traditional life prediction technology is broken through, and dynamic health assessment under multi-dimensional data driving is achieved; a cross-dimension feature fusion and closed-loop calibration mechanism is innovatively proposed, and the industrial problems that multi-source asynchronous data is weak in relevance and sudden abnormal response lags behind are effectively solved; through the synergistic effect of the generative adversarial network and the attention model, the stability and credibility of a prediction result are remarkably improved under a complex working condition; the technology can be adapted to a harsh use environment of an industrial adapter, and quantifiable and traceable decision support is provided for intelligent operation and maintenance of power electronic equipment.

The invention provides a construction method of a comprehensive intelligent early warning system for a traumatic patient, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: carrying out the processing of multi-modal data through a feature engineering framework, and extracting multi-scale time sequence features and correlation features; carrying out abnormal state identification and complication risk prediction by adopting a deep learning prediction framework, and carrying out feature fusion through an attention mechanism; constructing a medical knowledge graph to obtain vector representation of domain rule knowledge; and performing integrated verification through a bidirectional mapping mechanism to generate a prediction result. According to the invention, accurate early warning of disease development of the trauma patient can be realized, the early warning accuracy and interpretability are improved, and effective support is provided for clinical decision making.

The invention discloses a water turbine state intelligent prediction method which comprises the following steps: collecting multi-source sensor data of a water turbine, including vibration, temperature, pressure, flow and electrical parameters; performing space-time alignment preprocessing on the multi-source sensor data to generate a space-time associated data set; extracting and fusing the features of the space-time associated data through a multi-modal space-time diagram network, and generating a joint feature vector; performing health state prediction on the joint feature vector based on a dynamic digital twinborn model, and outputting a health score, a fault probability and a confidence interval; analyzing a fault propagation path by using a causal reasoning module, positioning a fault root cause and generating an interpretable report; and triggering an early warning or maintenance decision according to the prediction result. According to the technical scheme, the technical problems that multi-source heterogeneous data fusion is difficult, fault coupling and propagation are uncertain, real-time performance and computing resources are contradictory, and interpretability and reliability are insufficient are solved.

The invention discloses a marketing decision analysis system and method based on artificial intelligence, and aims to improve the intelligent level of marketing decision, optimize resource allocation and improve the rate of return on investment. The system comprises a data analysis module, a prediction evaluation module, an AI decision engine and a knowledge management module. The data analysis module obtains marketing-related data from a plurality of data sources and generates a market insight result. And the prediction evaluation module predicts the potential effect of the marketing scheme by adopting a statistical analysis method based on the market insight result and the historical marketing data, and obtains a marketing prediction result. And the AI decision engine receives the market insight result and the marketing prediction result, and determines an optimal marketing decision scheme based on multi-round dialogue management, knowledge graph construction and decision reasoning technologies. According to the marketing decision analysis system and method, accurate, efficient and reusable marketing decisions are realized in a data driving mode, and the scientificity and the performability of enterprise marketing strategies are improved.

The invention discloses a solar radiation space-time prediction method and system based on physical information constraint and a neural network, and the method comprises the steps: collecting multi-dimensional time sequence meteorological data, extracting high-dimensional time sequence dynamic features, converting geographic space data into a fuzzy set, and carrying out the defuzzification of the fuzzy set through an inference rule, thereby obtaining geographic space features; and a gating mechanism is adopted to realize deep fusion of the space-time features to generate high-dimensional space-time fusion features. In a model training stage, an energy conservation equation is introduced into an optimization process, a physical residual error is constructed by calculating a time derivative and a space derivative of a predicted value, a physical constraint total loss function is formed in combination with a data loss item, and model parameters are updated by using a gradient descent method. According to the method, the accuracy and reliability of a prediction result are remarkably improved while the calculation efficiency is ensured, and the method is particularly suitable for solar radiation prediction under complex meteorological conditions; according to the method, abnormal prediction caused by data noise can be effectively corrected, and a solution with physical rationality and data adaptability is provided for the fields of solar resource evaluation, photovoltaic power generation power prediction and the like.

The invention discloses a source-grid-load-storage integrated optimal scheduling method and system for a micro-grid, and relates to the technical field of smart grids and micro-grids, and the method comprises the steps: firstly collecting multi-source data in a specified micro-grid, and carrying out the optimization storage of the multi-source data in a time sequence database; constructing a time sequence prediction model, predicting generating capacity and load demand according to historical data, generating a preliminary scheduling strategy according to the generating capacity and load demand, realizing comprehensive cost minimization, analyzing the strategy, evaluating deviation and abnormity, and finally correcting the strategy according to an evaluation result; comprising an intelligent data aggregation module, a time sequence data prediction module, a preliminary scheduling generation module, a preliminary scheduling strategy analysis module and a real-time optimization feedback module, influences of various data sources are considered, the model can be adjusted in real time, variable power demands and power generation conditions in a micro-grid environment can be better coped with, and the real-time optimization feedback is realized. The accuracy of a prediction result is greatly improved, so that a powerful basis is provided for scheduling decision making, and risks possibly caused by prediction deviation are avoided.

The invention discloses a data-mechanism dual-drive-based rock burst early warning method and system, and the method comprises the following steps: deploying a multi-modal sensor network to collect coal and rock stratum data, building a rock burst disaster precursor information sample database, providing a rock burst disaster multi-modal data precursor feature recognition algorithm, and carrying out the recognition of rock burst disaster multi-modal data precursor features. Mining the relevance between the multi-modal data and disaster-causing key risk indexes, and establishing a rock burst disaster multi-modal data prediction model; establishing a three-dimensional geological geometric model, fusing a multi-field coupling dynamics constitutive model and a catastrophe criterion, constructing a PINN physical information neural network prediction model of the rock burst disaster, and obtaining a time-space evolution rule of an energy field of a target area; providing a loss function coupling calculation method of a multi-modal data driving sample error and a physical driving control equation residual error, dynamic data and mechanism prediction result weight, comprehensively calculating a risk score, and accurately judging a top disaster danger level.

The invention discloses a method for analyzing a matching degree between a demand and an output result based on text semantics. The method comprises the following steps: step 1, labeling a data set; step 2, technical document preprocessing; 3, training and predicting a single-parameter model; 4, integrating prediction results of the multi-parameter model; the method has the beneficial effects thatthe method is simple; deep learning and the NLP technology are applied to the field of project association degree calculation of enterprise project management for the first time. Calculating an association matching degree between the two projects according to project requirements and result description; the associated project positioning difficulty is effectively reduced; meanwhile, the demand side can be helped to quickly and efficiently locate high-quality projects adapting to the demand of the demand side; time and resource investment for achievement screening and matching are greatly reduced, the association matching degree between projects is calculated by means of text data of existing project achievement technical documents and project declaration guidelines, and then large enterprises are assisted in screening high-quality projects with the high matching degree in the project bidding and tendering link.

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

The present application discloses a cloud resource prediction method and system based on a GC-LSTM neural network model, an electronic device, and a readable storage medium. The method comprises two stages: a training stage and a prediction stage. In the training stage, historical data of cloud resources is collected and normalized to obtain a training data set, input data and labels used for training a neural network are organized and made, and an established GC-LSTM neural network model is trained. In the prediction stage, by means of acquired real-time sampling data, the trained model is loaded to perform result prediction, and, on the basis of a prediction result, resources may be scheduled and distributed to respond in advance. The present application aims to predict customers' demands for resources in advance and optimize customers' cloud experience. The model provided by the present application is simple and has few parameters, can guarantee the real-time performance of the prediction result, is practical and feasible, is suitable for the prediction of cloud resource utilization, and provides a guarantee for instant scheduling of resources.

The invention discloses a new energy equipment health prediction and intelligent maintenance method based on multi-source data fusion, and the method comprises the steps: building a dynamic health state evaluation model through a system in combination with equipment operation parameters and environment variables; monitoring the running state of the equipment in real time, and grading the health degree of the equipment; based on fault prediction of data, a deep learning algorithm is introduced to carry out modeling on time series data, and possible fault occurrence time and types of equipment are predicted by capturing potential trends and change modes in operation of the equipment; a dynamic prediction function is provided, a prediction result is updated in time, and synchronism of a model and an actual equipment state is ensured; and generating an optimized maintenance plan according to the health diagnosis and prediction result. Through innovative design of a multi-source data fusion technology and an intelligent prediction algorithm, a core pain point in operation and maintenance of current new energy equipment is solved, a feasible path is provided for full-life-cycle management of the equipment, and the method has remarkable economic value and industry popularization potential.

The invention relates to the technical field of health management and artificial intelligence, in particular to an old people health analysis system and method based on combination of medicine and nursing. The method comprises the steps of collecting and processing multi-modal health data, and generating a health data vector set; mapping the vector set to a standardized health knowledge ontology, constructing a health map initial structure and generating a map node vector; generating a health portrait and an evolution prediction result based on time sequence modeling; identifying risk features according to the health portrait and the prediction result, and generating a medical care service strategy set; and issuing the strategy set to an execution unit and collecting feedback, and finally updating a graph structure and optimizing model parameters based on feedback data. According to the invention, multi-modal data-driven old people health dynamic evolution modeling and personalized service strategy closed-loop optimization are realized, and the intelligent level and response efficiency of medical and nursing services are improved.

The invention relates to the technical field of computer science and artificial intelligence, and discloses a digital twin basin flood disaster early warning plan generation system based on deep learning, and the system comprises a data collection and processing module which obtains and standardizes multi-source data of basin meteorology, hydrology, terrain, remote sensing and the like in real time, and provides real-time data input; the digital twinborn modeling module is used for constructing a digital twinborn model of a watershed hydrological dynamic process according to the real-time data and outputting a simulation result; the deep learning prediction module is used for receiving a simulation result, training a prediction model and outputting space and time distribution prediction of flood disasters; the emergency response generation module is used for generating an emergency plan of the flood disaster according to the prediction result; and the system feedback module is used for evaluating an emergency plan and a prediction effect and providing an adjustment basis for future disaster early warning. According to the invention, accurate flood disaster prediction based on real-time data can be realized, the accuracy and response efficiency of an emergency plan are improved, and disaster loss is reduced.

The invention discloses a power flow generating capacity prediction method based on STL decomposition and multi-model fusion, which adopts an STL decomposition method to perform trend term, season term and residual term decomposition on historical generating capacity data, optimizes the decomposition process through a Bisquare weighting function and an internal loop iteration mechanism, and enhances the processing capacity for abnormal values and high-frequency fluctuations. The method comprises the following steps: extracting multi-scale frequency domain features by using a TimeNet model in combination with fast Fourier transform (FFT), predicting a trend term and a seasonal term, and performing accurate modeling on a residual term by using an Itransform model in combination with a self-attention mechanism and gating residual connection. Meteorological factors related to the tidal current generating capacity are screened, prediction is carried out in combination with historical operation data, and high-precision prediction of the tidal current generating capacity is achieved. The method has high adaptability and robustness, can effectively deal with multi-source influence and high-frequency fluctuation in tidal current power generation data, and provides an accurate and stable prediction result.

The invention discloses an interpretability prediction analysis system for multivariable time series data. According to the method, the prediction precision and the decision support capability of the complex time series data are remarkably improved through multi-module cooperation. Firstly, an adaptive learning optimization module dynamically adjusts model parameters and a prediction strategy, so that the model can quickly adapt to time-varying characteristics of data distribution, for example, when a causal relationship between variables suddenly changes, the weight of latest data is automatically enhanced, and historical noise interference is reduced. The dynamic causal interpretation engine tracks the influence intensity and hysteresis effect of key variables in real time, converts traditional black box prediction into a traceable causal relationship chain, and helps a user to intuitively understand driving factors of a prediction result, for example, it is identified that prediction value sudden increase in a certain period is mainly derived from hysteresis effect accumulation of an upstream variable A.

The invention relates to a model for evaluating and predicting mild cognitive impairment risk of old people in a pension institution. The model sequentially comprises a behavior analysis module, a language recognition module, a social modeling module, a toughness calculation module, a feature fusion module, a risk reasoning module and the like. Behavior deviation characteristics and abnormal time periods are extracted by collecting behavior data of daily life, diet, social contact and the like of old people and comparing the behavior data with an institution work and rest template; in combination with nursing records, extracting language anomaly features; analyzing social frequency and structure changes in the abnormal time period, and extracting social variation features; a cognitive toughness index is calculated by integrating the health archive and the recovery ability to the health event; and performing toughness weighting on the multi-dimensional features to construct a time sequence tensor, and inputting the time sequence tensor into a recursive model to predict a cognitive impairment risk value. And if the risk value suddenly changes, the system automatically backtracks the feature trajectory of nearly 7 days, constructs and screens a prediction path with the strongest interpretation force, outputs a dominant prediction result and a key factor sequence, and realizes high-interpretability and high-reliability early recognition and intervention reference.

The invention discloses a power load prediction method and device based on multi-factor influence, and particularly relates to the technical field of power load prediction. Historical weather data and power load data are collected, preprocessing and feature extraction are carried out, temperature change deviation features and power load lag features are extracted and input into a prediction model for training, the accuracy of a prediction result is evaluated, and when the prediction accuracy is low, the prediction result is subjected to prediction. The abnormal degree is further analyzed and predicted, and the power dispatching strategy is adjusted in time, so that the violent fluctuation of the load demand is dealt with, the stable operation of the power grid is ensured, the accuracy of power load prediction is effectively improved, and the risk of overload or collapse of the power grid is reduced especially in the case of extreme weather.

The invention discloses a water quality prediction deep learning model and prediction method based on spatio-temporal feature fusion and LSTM, and the model comprises one or more time convolution networks which comprise a plurality of TemporalBlock modules, and one or more classic convolution networks which comprise one or more convolution layers, a pooling layer, and a full connection layer. A Xavier initialization method is adopted to initialize parameters, a feature fusion mechanism is provided, and each convolution layer comprises a convolution operation, a ReLU activation function, a Dropout layer and a pooling layer; at least one additional temporal convolutional network; and a long-short-term memory network, a feature fusion module and an output module which flexibly adjust the output into single-step prediction and multi-step prediction and are used for generating a final prediction result. According to the method, the time sequence dependence and the nonlinear relation in the water quality monitoring data can be effectively processed, and the deep learning model of the multi-source information can be flexibly integrated, so that the accurate prediction of the water quality change is realized.

The invention relates to the technical field of medical systems, and particularly discloses an anesthesia complication prediction model construction method based on deep learning, and the method comprises the following steps: S1, obtaining multi-source heterogeneous anesthesia medical data; s2, constructing a multi-modal feature fusion module; s3, designing a hierarchical deep neural network architecture which comprises sub-networks for processing different modal data in parallel and a full-connection prediction layer fusing multi-modal features; s4, continuously outputting a complication probability curve in a sliding time window mode by adopting a dynamic risk trajectory prediction mechanism instead of a single static prediction result; and S5, deploying a clinical real-time decision interface, and mapping a prediction result to an anesthesia monitoring equipment alarm system in real time. A bidirectional LSTM + 1D-CNN hybrid encoder and a cross-modal attention mechanism are adopted, time sequence dependence of physiological signals and spatio-temporal characteristics of operation events are synchronously captured, deep semantic fusion of multi-source data is achieved, and the characterization capacity of a model for precursor characteristics of complications is improved.

The invention relates to the technical field of respiratory system risk prediction, and provides a respiratory system risk prediction method and system based on a graph neural network, and the method comprises the steps: collecting the multi-modal medical data of a patient, and constructing a multilayer heterogeneous graph based on the multi-modal medical data; constructing a weighted adjacency matrix and a node feature vector through the multi-layer heterogeneous graph; matrix product operation and convolution operation are carried out based on the weighted adjacent matrix and the node feature vector, splicing combination with historical moment state information is carried out, graph state representation is obtained, weighted aggregation of time dimensions is carried out, and time sequence attention features are obtained; performing coding processing based on the clinical examination data to obtain multi-modal fusion features; and inputting the multi-modal fusion features into a risk classifier for classification calculation to obtain a respiratory system risk level prediction result, generating a risk assessment report, and outputting respiratory risk early warning information. The accuracy and clinical practicability of respiratory system risk prediction are improved.

The invention discloses a gait sub-phase prediction method and device based on deep learning model fusion. The method comprises the following steps: synchronously collecting multi-source gait data of a wearer walking at various speeds; preprocessing the collected multi-source gait data, dividing a time window by adopting a sliding overlapping window technology, and constructing a supervised learning training data set based on double tags; hierarchically fusing CNN, Bi-LSTM and MHA, and constructing a gait sub-phase prediction model; automatically searching an optimal hyper-parameter combination by using a Bayesian optimization algorithm; a stable prediction time (Pstable) index is defined, and model performance is comprehensively evaluated through one-by-one subject rejection cross validation and a speed migration robustness experiment in combination with a classical index; a modular gait sub-phase prediction system architecture is constructed based on a Simulink platform, and a multi-thread buffer mechanism is integrated to process data streams in real time; and solidifying model parameters to an online reasoning module, and dynamically outputting a prediction result.

The invention discloses a cleaning path planning method and a cleaning device, and belongs to the technical field of automatic control, and the method comprises the steps: collecting the point cloud data of a to-be-cleaned region, constructing an initial composite map, setting a perception marking method, dividing the point cloud data into sub-regions, and marking a sudden change region after the sub-regions are matched with historical map features. Locally updating the composite map by using map optimization; constructing a safety buffer zone at the edge of the to-be-cleaned area, setting a buffer adjustment method, performing trajectory prediction on the abrupt change area, and dynamically adjusting a safety buffer distance according to a prediction result; calling a basic cleaning path, generating a layered path, setting a planning adjustment method, updating the layered path according to the abrupt change area in the cleaning process, calculating a pollution index of the to-be-cleaned area, constructing a time sequence prediction model, pre-judging a pollution diffusion trend, and performing dynamic adjustment; and after the to-be-cleaned area is cleaned, decision iteration is carried out to adapt to user habits.

Multi-faceted machine learning model systems and methods are disclosed that integrate historical data, user-specific criteria, and prioritized real-time event updates. The machine learning model systems and methods may be selected and weighed to optimize simulation accuracy based on event type, player status, and user-defined constraints, such as risk tolerance and preference for specific teams, sports or players. The machine learning model is applied to a first historical outcome and a second historical outcome to generate a predictive outcome responsive to a user query, the user query having a first keyword vector and a second keyword vector. The machine learning model is applied to a real-time event and the predictive outcome to generate an updated predictive outcome. A prediction error indicative of a difference between the predictive outcome and the updated predictive outcome can be determined.

The invention discloses an intelligent operation and maintenance management system and method based on a charging pile, and belongs to the technical field of fault early warning, and the method comprises the steps: building a unified time sequence operation data matrix through collecting multi-source state data generated in the operation process of the charging pile; key features are extracted to construct feature vectors, and a multi-classification neural network model is utilized to evaluate a health state; a micro-degradation evolution path model is constructed in combination with the health trend in the continuous observation period, and a fault prediction curve is generated; performing similarity matching on a prediction result and a fault prior curve library, calculating a risk weight coefficient, identifying potential fault nodes and outputting an early warning list; constructing a regional task scheduling graph based on the high-risk pile position, fusing geographic position, power level and residual life information, and optimizing to generate an operation and maintenance path and a resource configuration scheme; according to the method, the fault prediction accuracy and operation and maintenance efficiency of the charging pile can be remarkably improved, and intelligent operation and maintenance and response optimization are realized.

The invention provides a power load long time sequence prediction method and a related device, and relates to the technical field of power load prediction. The method comprises the steps of collecting power load time sequence data; preprocessing the power load time sequence data; decomposing the preprocessed power load time sequence data into a seasonal component and a trend component through a mobile decomposition method; inputting the trend components into a trend clustering enhancement network, performing channel clustering on the trend components through a K-means clustering algorithm, applying independent linear transformation to each clustering group, and extracting long-term trend features; inputting the seasonal components into a seasonal causal attention network, and extracting short-term seasonal features through a causal mask mechanism and a multi-head attention mechanism; fusing the long-term trend features and the short-term seasonal features to generate fused features; and inputting the fusion features into a decoder, and outputting a power load prediction result through a full connection layer. The change trend of the power load data is fully considered, and the prediction precision of the power load long sequence is improved.

The invention discloses a clinical research data analysis method based on machine learning, and the method comprises the steps: constructing a multi-modal variable structured causal map, and building a direction adjustable mechanism of a causal path; constructing a bidirectional nested structure attention mechanism, and capturing a cross-modal dependency and dynamic evolution relationship between variables; recording each layer of information propagation path and variable participation degree, and realizing reverse reconstruction of a model decision path in a reasoning stage; target-oriented attribution path regularization is introduced to carry out regularization constraint on an attribution path set of the key target variables; and constructing a nested attribution graph visualization system, and realizing interactive presentation of interpretation sub-graphs corresponding to prediction results so as to improve cognitive trust of model output. According to the method, from structure expression, path tracing and causal constraint to visual presentation, the core problems that a deep model is poor in interpretability, clinicians are not trusted, and existing interpretation tools are insufficient in applicability are solved in a full-link mode.

The invention discloses a purchase demand prediction method and system based on big data analysis, and relates to the technical field of supply chain management and intelligent prediction, and the method comprises the steps: collecting multi-source heterogeneous data, and carrying out the data fusion and preprocessing; based on a causal inference technology, key influence factors of purchase demand changes are analyzed, and a purchase demand prediction model is constructed; dynamically adjusting the prediction model by using real-time data, and generating a real-time or periodic purchase demand prediction result; carrying out data simulation and model optimization aiming at an extreme scene of a purchase demand; and optimizing a supply and demand matching strategy based on a prediction result, and carrying out iterative improvement on the prediction model through a feedback mechanism. Through multi-source heterogeneous data fusion, causal inference, dynamic adjustment and feedback optimization mechanisms, high-precision purchase demand prediction is realized, a supply and demand matching strategy is optimized, the adaptability of the model to market fluctuation and extreme scenes is enhanced, the purchase cost is effectively reduced, and the supply chain management efficiency and stability are improved.