9701 results about "Model prediction" patented technology

The invention provides a dynamic health adaptive monitoring method and system using artificial intelligence, and relates to the technical field of health monitoring, and the method comprises the steps: carrying out the noise reduction through a hierarchical diffusion probability model through multi-source heterogeneous health data, extracting preliminary features through combining with an improved attention auto-encoder, learning discriminative features through a contrast learning network, and carrying out the recognition of the discriminative features; inputting the discriminative features into an adaptive wavelet neural network for multi-scale feature decomposition, inputting the discriminative features into a dynamic graph neural network to obtain a health feature tensor, obtaining time sequence features by using a hierarchical memory enhancement network, and generating a health knowledge graph in combination with a dynamic routing capsule network and a causal reasoning network; and inputting the time sequence characteristics and the health knowledge graph into a deep Bayesian network for health risk assessment, and obtaining an adaptive monitoring scheme through a model prediction control network by using a hierarchical reinforcement learning network and combining a multi-agent collaborative optimization system and a deep deterministic strategy gradient network.

The invention provides an industrial park multi-target collaborative optimization scheduling method and system based on artificial intelligence, relates to the technical field of intelligent scheduling of industrial parks, and aims to establish a digital twinborn model and a dynamic topological graph, perform resource flow relationship modeling by using a graph neural network, and perform resource demand analysis in combination with a multi-model prediction framework. A multi-agent reinforcement learning algorithm is adopted to generate a scheduling strategy, migration from a global strategy to a local decision module is realized through a strategy distillation technology, robustness of the scheduling strategy is simulated and verified in a digital twin model, the strategy is adjusted according to a simulation result, and finally efficient scheduling of park resources is realized. The method effectively improves the intelligentization and collaboration level of the operation of the industrial park, reduces the energy consumption and carbon emission, enhances the ability of the park to cope with complex scenes, and is suitable for the efficient management of the modern industrial park.

The invention discloses a tidal river reach storm surge rapid forecasting method based on deep learning and an AI large model. The method comprises a data acquisition and preprocessing step, a meteorological element forecasting step, a typhoon path and flow forecasting step and a storm surge forecasting step. Multi-source data fusion is realized through a four-dimensional variational assimilation method and an adaptive weight fusion algorithm; a multi-model integrated system such as FourCatNet and the like is used for forecasting meteorological elements; an improved XLSTM-Transformer model is adopted to predict the flow and the typhoon path; and carrying out storm surge forecasting based on a three-layer nested model system and a multi-process coupling technology. According to the method, full-process automation from data acquisition to forecasting is realized, the forecasting timeliness is improved by 60%, the average absolute error is reduced by 45%, and a reliable basis is provided for disaster prevention and reduction decisions.

The invention relates to the technical field of port ecological construction, and particularly discloses a port ecological construction evaluation method and system, and the method comprises the following steps: collecting dynamic ecological environment data in port construction operation through a multi-source sensor array; performing space-time coupling analysis on the data, calculating a dynamic attenuation rate # imgabs0 # of each dimension environment index by adopting a self-adaptive sliding time window algorithm, adjusting an ecological response time constant theta in combination with hydrodynamic conditions of a sea area around a port, performing synchronous integral calculation on a historical cumulative effect # imgabs1 #, and generating an environment health degree evolution curved surface; constructing a comprehensive evaluation model; and predicting an environmental risk value based on the hybrid prediction model. According to the invention, an intelligent, automatic, safe, reliable, green and low-carbon evaluation solution is provided for ecological construction of the port, and the ecological protection and sustainable development level of the port is remarkably improved.

The invention provides an intelligent fault diagnosis method for a numerical control machine tool, which is applied to a control system of the numerical control machine tool. The method comprises the steps of collecting multi-source sensor data in the operation process of the numerical control machine tool, wherein the multi-source sensor data comprises at least two of a vibration signal, a temperature signal, a current signal and a sound signal; performing preprocessing and feature extraction on the multi-source sensor data to obtain preprocessed data; based on the preprocessed data, constructing and training a machine tool dynamic model to obtain a target machine tool dynamic model; when abnormal operation of the numerical control machine tool is detected, fault state estimation is carried out based on the target machine tool dynamic model and a model prediction control method, and a fault estimation result is obtained; and based on the fault estimation result, constructing a fault evolution path by adopting a Monte Carlo tree search algorithm, and outputting a fault diagnosis result. The accuracy and efficiency of fault diagnosis of the numerical control machine tool can be improved.

A computer model is monitored during operation to evaluate performance of the model with respect to different groups evaluated by the model. Performance for each group is evaluated to determine an inter-group performance metric describing how model predictions across groups differs. A threshold for excess inter-group performance differences can be calibrated using withheld training data or out-of-time data to provide a statistical guarantee for detecting meaningful variation in inter-group performance metric differences. When the inter-group performance exceeds the threshold, the computer model may be considered to deviate from expected behavior and the monitoring can act to correct its operation, for example, by modifying actions that may otherwise occur due to model predictions or by initiating model retraining.

A computer model is monitored during operation to evaluate performance of the model with respect to different groups evaluated by the model. Performance for each group is evaluated to determine an inter-group performance metric describing how model predictions across groups differs. A threshold for excess inter-group performance differences can be calibrated using withheld training data or out-of-time data to provide a statistical guarantee for detecting meaningful variation in inter-group performance metric differences. When the inter-group performance exceeds the threshold, the computer model may be considered to deviate from expected behavior and the monitoring can act to correct its operation, for example, by modifying actions that may otherwise occur due to model predictions or by initiating model retraining.

The present application discloses an underground water real-time monitoring method and system, and a medium. The method comprises: acquiring historical environment data and historical monitoring data in an underground water monitoring area for spectral clustering data partitioning, and importing related data into a pollution monitoring model for predictive analysis and model training; acquiring pollution monitoring data in monitoring sub-areas in real time, importing the pollution monitoring data into the pollution monitoring model for pollution prediction, and obtaining pollution diffusion directions and pollution source prediction areas; and on the basis of the pollution source prediction areas and environment data of the monitoring sub-areas, calculating a monitoring demand degree of each monitoring sub-area, and generating a monitoring scheme on the basis of the monitoring demand degree. By means of the data spectral clustering method in the present application, accurate grouping and acquisition of training data can be effectively implemented, and model prediction precision is improved. In addition, the present application can realize dynamic adjustment of monitoring areas, effectively conserve human and material resources in underground water monitoring areas, and realize the cost reduction and efficiency improvement of underground water pollution monitoring.

The invention discloses a method for inverting key parameters in an underground water pollutant biodegradation numerical model based on a neural network algorithm. Carrying out preprocessing and refined dimension reduction on the data, and processing high-dimensional multi-scale data; building a multilayer neural network architecture under a physical information neural network framework, embedding a related reaction equation of a pollutant natural attenuation process, and coupling a biochemical reaction mechanism and a data driving model; a neural network model is trained, errors in the training process are calculated, and the accuracy of an inversion result is improved by using a sub-when optimization algorithm; integrating observation data and a model prediction result, determining final parameter setting of a numerical model, and carrying out underground water damage real object quantification; comparative verification of a neural network parameter inversion model is carried out, and feasibility and accuracy of the parameter inversion method are ensured. According to the method, the problem that the key parameters of the biodegradation numerical model are difficult to accurately invert in the real object quantification process of underground water pollution under complex geological conditions is solved.

The invention provides an unmanned aerial vehicle return detection method and system based on AI recognition, and relates to the technical field of image recognition, and the method comprises the steps: extracting multi-scale features through an improved moving window hierarchical converter model, obtaining target attitude information through the combination of cross-modal comparative learning and a dynamic routing capsule network, and obtaining target attitude information; an alignment feature representation is obtained by using a feature enhancement module and an adversarial feature alignment method, the alignment feature representation is input into a graph attention diffusion model to obtain an accurate state representation, and a state evolution trajectory is obtained in combination with an adaptive causal convolutional network and an ordinary differential equation network; posterior distribution is determined through a variational inference network and an autoregression flow model, a state sequence is predicted by using a neural process network and a dynamic point process model, an expert strategy is learned by using a hierarchical generative adversarial imitation learning network and a distributed trust domain evolutionary strategy algorithm, and optimal parameter configuration is obtained; and accurate prediction of the return state of the unmanned aerial vehicle is realized.

The invention relates to a temperature control method, device and equipment of semiconductor equipment and a storage medium. The method comprises the following steps: performing finite element mesh division and modal coordinate transformation on a plurality of temperature control areas of semiconductor equipment to obtain a temperature control area dynamic model; collecting measurement data of a temperature sensor and performing time-frequency domain transformation to generate a temperature prediction data sequence; gaussian mixture modeling is carried out to obtain prior distribution, and temperature parameter probability distribution is obtained; performing sub-domain division on the temperature control range to obtain a temperature inter-domain feature mapping matrix; and a temperature interval thermal coupling coefficient matrix is calculated based on the temperature control area dynamic model, a temperature control objective function is constructed in combination with the temperature prediction data sequence and the temperature inter-domain feature mapping matrix, and a temperature control quantity sequence is solved by adopting a model prediction control algorithm. According to the invention, the accuracy and dynamic response speed of temperature control are improved, and the stability of temperature control and the energy utilization efficiency are ensured.

The invention relates to the technical field of disease follow-up visit, in particular to a health state assessment system applied to disease follow-up visit, which comprises a data acquisition module, a data preprocessing module, a health state assessment module, a personalized health management suggestion module, a dynamic health risk prediction module and a visualization module, the data acquisition module acquires multi-dimensional data of a patient and performs data fusion and integration, the health state evaluation module evaluates the health state of the patient based on multi-level feature extraction and a deep learning model, and the personalized health management suggestion module combines a health state evaluation result and historical data to obtain a personalized health management suggestion result. Customized health management suggestions are generated through a rule engine, and the dynamic health risk prediction module predicts future health risks of patients and provides real-time early warning by using a time sequence analysis model. The visualization module displays health assessment and management suggestion results through a chart and an interactive interface, and the accuracy and individuation degree of health management are improved.

The invention discloses a hemispherical resonator gyroscope inertial navigation system error online calibration method, device and system and a readable storage medium. The method comprises the following steps: establishing a dynamic observation equation of a gain error and a misalignment angle error; adaptively extracting error features of different time scales, and constructing a high-dimensional feature vector; decoupling the gain error and the misalignment angle error to obtain an error estimation value; constructing a dynamic compensation matrix, and correcting and measuring the angular velocity in real time; an online self-correction mechanism of the error compensation model is designed, and a compensation matrix is dynamically adjusted to adapt to dynamic changes of errors; an adaptive time sequence regression model is introduced to predict an error long-term drift trend, and a nonlinear compensation method is combined to actively counteract an error accumulation effect; dynamically adjusting the optimization rate of the compensation matrix through stability analysis; and dynamically optimizing error model parameters based on convergence evaluation of error residual errors. According to the invention, the limitation of a traditional method in a dynamic environment, especially the coupling problem of a gain error and a misalignment angle error, is solved.

The invention discloses an optical storage direct-flexible system power scheduling method based on market model prediction response, which comprises the following steps: determining the composition of an optical storage direct-flexible system, and collecting operation state data and external environment data; constructing a hybrid prediction model, and predicting the market price and the uncertainty interval of the market price in the scheduling period; establishing a power scheduling optimization model, and solving to obtain an optimal power scheduling strategy; according to the optimal power scheduling strategy, a control instruction is issued to each control unit of the optical storage direct flexible system, and cooperative adjustment is carried out through a bidirectional coupling feedback mechanism of voltage and power; the market price and the system operation state are monitored in real time, and when the market price deviation exceeds a price deviation threshold value, scheduling strategy online correction is triggered. According to the robust optimization architecture, traditional power scheduling is converted into a confrontation game structure between the system and the market, and the resistance of the system to extreme price situations is improved while economic benefits are guaranteed.

The invention relates to the technical field of wind turbine generator data analysis, and discloses an offshore wind turbine generator operation data analysis and prediction system. The system comprises a marine environment data integration module for collecting data to generate a multi-source time-space synchronization data set; the multi-modal feature fusion module is used for extracting cross-modal correlation features to generate a high-dimensional fusion feature tensor; the dynamic fault prediction module is used for constructing a two-way gating circulation network model to predict the degradation probability and the residual life of key components of the equipment; and the self-adaptive optimization control module is used for constructing a multi-target dynamic programming model to optimize a fan operation strategy. In addition, the system is further provided with a feedback correction module for correcting prediction model parameters, and a virtual sensor module based on a physical information neural network is used for monitoring tower stress and diagnosing sensor faults. According to the system, comprehensive monitoring, accurate fault prediction and optimal control of the offshore wind turbine generator are realized, the operation efficiency, reliability and safety of the wind turbine generator are effectively improved, and the operation and maintenance cost is reduced.

The invention discloses an integrated cold station intelligent control system based on multi-modal data fusion, and relates to the technical field of data fusion, an intelligent decision and optimization module adopts reinforcement learning and model prediction control based on fused data to realize dynamic optimization of start and stop of a water chilling unit, chilled water temperature setting and water pump frequency adjustment; the fault detection and adaptive optimization module evaluates the data fusion accuracy by calculating an environment entropy value and cold station dynamic response delay time, and dynamically adjusts a control strategy based on a prediction deviation rate and the data fusion accuracy; the closed-loop feedback module analyzes and adjusts control parameters through operation data, intelligent control models between different cold stations are optimized through transfer learning, intelligent deployment of a new cold station is accelerated, and the generalization ability of the models is improved. More accurate, more energy-saving and more stable intelligent control of the cold station is realized, and the operation efficiency and reliability of the cold station system are improved.

The invention discloses a multi-task cooperative processing method and system based on digital employees, and relates to the technical field of cloud computing, and the method comprises the steps: firstly decomposing a task submitted by a user into a plurality of sub-tasks, and enabling the sub-tasks to be executed by the digital employees; and then, an initial task scheduling scheme of the digital employees is generated by adopting an ant colony algorithm in combination with task complexity evaluation and execution time prediction. In the execution process of the digital employee, the task execution condition and the resource occupation trend in a period of time in the future are predicted through the long-short-term memory neural network, task allocation is dynamically optimized and adjusted by using a model prediction control algorithm, and real-time feedback and adjustment of scheduling decision and actual execution are realized. The problem of low task execution efficiency in digital employee multi-task collaboration in the prior art is solved. According to the method, the task execution efficiency and the load balance are dynamically optimized while the task dependency constraint is met, so that business scenes such as business process automation and cross-department collaboration are effectively realized.

The invention discloses a ground motion intensity measure optimization method for seismic response prediction. The method includes the following steps: (1) building a building damage database; (2) optimizing ground motion intensity measures; (3) constructing a machine learning model; and (4) predicting damage states. According to the method, a combination of optimal ground motion intensity measures is selected by an elastic-net algorithm, wherein the efficiency and practicability in a current selection criterion are updated based on the goodness of fit and regression coefficient of the elastic-net model, thereby avoiding an assumption that intensity measures and structure response need to obey a logarithmic linear relationship and the limitation of intensity measure dimensions in traditional methods. By using the combination of optimal ground motion intensity measures as input variables, the prediction accuracy can be remarkably improved, and meanwhile the number of samples required is reduced.

The invention relates to the technical field of intelligent navigation and control of ships. The invention provides a complex channel ship intelligent navigation and emergency decision-making method and system. The method comprises the following steps: acquiring environment data through a multi-source heterogeneous sensor array, and establishing a channel three-dimensional dynamic environment model; establishing a multi-objective optimization function, and performing dynamic path planning by adopting an improved model prediction control algorithm; synchronizing motion state parameters of an actual ship and a virtual ship model in real time, constructing an emergency decision tree in combination with an expert knowledge base, and verifying the feasibility of an emergency decision through Monte Carlo simulation; carrying out local route optimization by adopting edge computing nodes, carrying out multi-ship trajectory prediction through a federated learning mechanism, and generating a corresponding collaborative collision avoidance strategy; and establishing a dynamic priority scheduling mechanism, implementing hierarchical response, and confirming a global avoidance scheme through a distributed consensus algorithm. The problems that an existing inland ship intelligent system is limited in perception, rigid in decision and weak in collaboration in a complex scene are solved.

The invention relates to an intelligent power distribution harmonic monitoring and dynamic compensation system which comprises a monitoring unit, a correction unit and a compensation unit. The monitoring unit continuously collects high-frequency harmonic voltage and current data in a distribution line at a high sampling frequency, extracts transient harmonic components through wavelet packet transformation and empirical mode decomposition, and generates low-dimensional feature vectors based on sparse representation. And the correction unit decodes the low-dimensional feature vector, recovers harmonic time-frequency features, calculates a phase drift rate, predicts a harmonic propagation path and an accumulation node by combining real-time power distribution network topology construction and adopting a nonlinear dynamic prediction model, and generates a correction instruction when abnormality is detected. And the compensation unit adopts pulse sequence density modulation to dynamically adjust a compensation current phase according to the correction instruction, and meanwhile, an inductive coupling device is utilized to transfer harmonic energy to a low-risk node, so that harmonic voltage distortion of a target node is quickly recovered to a stable level in a fundamental wave period after early warning.

The invention relates to the field of logistics information processing, in particular to a logistics scheduling method and system.The method comprises the steps that multi-source data such as traffic flow, weather, orders and logistics network states are collected in real time, and dynamic logistics state data are generated through a data fusion model; on the basis of the data, performing fine-grained modeling of path fragments by using distributed simulation nodes, performing simulation of the state of the path fragments in combination with a cellular automaton model, and performing adaptive adjustment by using a reinforcement learning algorithm to generate optimized path decision data; global state modeling and resource scheduling optimization of a multi-layer feedback interaction mechanism are carried out through a multi-agent system, and finally dynamic prediction and automatic adjustment of a global logistics state are realized through model prediction control and a multi-layer self-correction mechanism. According to the invention, the flexibility and response speed of logistics scheduling are effectively improved, and efficient execution of logistics tasks is ensured.

The invention relates to the technical field of economic resource management, and discloses an economic resource management optimization method based on intelligent decision making. According to the method, multi-source heterogeneous data, including resource stock, demand fluctuation and the like, of an economic system are collected firstly; a dynamic resource pool is divided based on multi-dimensional feature analysis, and a nonlinear optimization model is constructed to predict resource supply and demand changes so as to generate an allocation scheme; and then optimizing a distribution path by using a multi-stage decision tree, updating model parameters through an adaptive learning mechanism according to market feedback and constraint condition changes, and correcting a deployment scheme in real time. In addition, key technical details such as an elastic quota adjustment formula and a fuzzy clustering algorithm membership function are given. According to the method, complex data can be effectively integrated, resources are scientifically scheduled, supply and demand are accurately predicted, a distribution path is optimized, environmental changes are adapted, the efficiency and benefits of economic resource management are remarkably improved, and scientific and reasonable resource management decision support is provided for economic subjects.

The invention discloses a distributed optical storage micro-grid control system based on a large model and an energy management method, and the system collects various data through a data collection module, captures a time sequence long-term dependence relation based on a self-attention mechanism through a large model prediction system, and predicts the photovoltaic power generation amount, the load demand and the energy storage charging and discharging demand. The network-forming inverter integration module dynamically adjusts the output power, the energy storage strategy and the interaction power of the power generation system according to a prediction result, the distributed control strategy module adopts a distributed consensus algorithm to realize information sharing and collaborative decision making, and the energy management module makes a multi-time scale plan and introduces an economic optimization model. The energy management method comprises the steps of data collection, real-time monitoring, prediction modeling, plan making, distributed control, economic optimization, system monitoring, fault processing and the like. The method can improve the new energy utilization rate, the electric energy quality and the system stability, adapts to the change of environmental factors, and maximizes the economic and environmental benefits of the micro-grid.

The invention discloses an intelligent port crane operation monitoring method and system, and relates to the technical field of crane systems.The system comprises a data acquisition unit, an edge computing node, a cloud processor and an early warning monitoring unit, through deep fusion and efficient treatment of multi-source data, a dynamic twinborn data pool architecture is established, and the dynamic twinborn data pool architecture is established; cross-data-type space-time alignment is realized through multi-modal coding, and the feature fusion accuracy is improved; the dynamic twin data pool is intelligently analyzed, and the crane operation path is optimized in real time, so that the track of the crane operation path is dynamically adjusted, and the working efficiency is improved; through a digital twin model and integration of a multi-modal physical engine, the operation physical state deviation of the crane is evaluated, an operation plan is corrected in real time, and the operation precision is improved; video streams are deeply analyzed, virtual-real comparison is carried out, and model prediction video deviation is monitored, so that the analogue simulation precision is improved; by establishing a closed-loop early warning mechanism, full-process digital management and control are realized, and intelligent upgrading of a port is promoted.

The invention relates to a medical image processing technology, and discloses a medical image segmentation and labeling method and system based on multi-modal information fusion, and the method comprises the steps: constructing a model structure of a medical image segmentation and labeling model based on multi-modal information fusion; preprocessing the data set, constructing the data set by using a random sampling method, and dividing the data set into a training set, a verification set and a test set according to a proportion; training and convergence of the model: training the model through a loss function and an optimizer until the model is converged; and S3, cutting an organ to be segmented from the original image, inputting the cut organ to the model trained in the step 3, inputting a text prompt word for semantic segmentation, and taking a mask predicted by the model as a model segmentation labeling result. According to the method, the model structure is constructed, then the medical image data set is subjected to necessary preprocessing, and then the medical image data is segmented in combination with the guiding text semantic information, so that the efficiency of segmenting and marking the medical image from 0 to 1 is effectively improved.

The invention provides a generator excitation regulation and control optimization system based on model prediction, and relates to the technical field of data processing, and the system comprises a data acquisition module which is used for collecting the operation parameters of a generator; the state estimation module is used for calculating a state variable of the generator excitation system and constructing a state space model of the generator excitation system; the model prediction control module is used for calculating excitation current prediction values at multiple moments in the future by adopting a rolling horizon optimization method, and performing dynamic correction based on a prediction error correction mechanism; the optimization calculation module is used for constructing a target optimization function which comprises a generator voltage stability target item and an excitation current adjustment smoothness target item, and calculating an optimal excitation current adjustment amount by adopting a constraint optimization method; the excitation adjusting module is used for controlling a generator excitation system so as to adjust excitation current; according to the invention, the autonomy and accuracy of the generator excitation regulation and control optimization system are improved.

The invention provides a vehicle driving track prediction method, system and device and a medium, and belongs to the technical field of vehicle track prediction.The method comprises the steps that vehicle state information and environment information of each track point of a vehicle are collected to serve as track data, and a deep learning denoising algorithm is used for preprocessing and feature sequence extraction; constructing a trajectory prediction model by using a Transform in combination with a graph neural network, constructing a training set by using the extracted feature sequence to train the trajectory prediction model, and adjusting model parameters by using a multi-task loss function constructed by trajectory prediction precision and uncertainty estimation in the training process; and using the trajectory prediction model to predict the vehicle trajectory, using the Bayesian optimization method to optimize the trajectory parameters with the goal of minimizing the prediction error, and adjusting the optimization parameters with the vehicle dynamics constraint and the road constraint as constraint conditions in the optimization process. According to the invention, through multi-fusion and multi-stage optimization, high-precision prediction of the vehicle track in a complex traffic environment is realized.

The invention discloses a reef limestone strength prediction method based on pore feature machine learning, and belongs to the technical field of crossing of geotechnical engineering and artificial intelligence, and the method comprises the steps: obtaining a reef limestone three-dimensional digital core, and extracting pore features after preprocessing; establishing a multi-source pore-physical coupling feature data fusion framework based on the pore features; integrating a random forest and a gradient boosting tree, dynamically weighting parameters in the pore features, designing a composite loss function, performing hierarchical training, and embedding physical constraints to construct a reef limestone strength prediction machine learning model; layered K-fold cross validation is adopted, the robustness of the reef limestone strength prediction machine learning model is tested, and interpretability is verified. According to the method, through data processing, the reef limestone strength prediction machine learning model is constructed and optimized, the relationship between the pore characteristics and the reef limestone strength is deeply excavated, the model prediction accuracy, reliability and interpretability are improved, and an effective solution is provided for reef limestone strength prediction.

The invention belongs to the technical field of smart home control, and particularly relates to a smart home generation strategy optimization system based on adaptive learning, and the system comprises the steps: collecting the operation history, sensor data and equipment use modes of a user in a smart home system, and determining an evaluation standard and an optimization direction; extracting features according to user behaviors, environment changes and equipment states, and constructing a feature matrix; selecting a reinforcement learning method for modeling according to an optimization problem, designing an adaptive learning mechanism, and enabling the model to be continuously adjusted according to real-time data in an operation process; according to the method, historical data is used for training the model, a reward mechanism in reinforcement learning is used for guiding the implementation of an optimization target, a control strategy adapting to the current environment and demand is generated based on model prediction, and the method has the effects of better meeting the use demand of a user for the smart home and improving the intelligent level of the system and the user experience.

The invention relates to an intelligent power data searching method based on a resource map, which comprises the following steps of: S1, integrating power data from different systems, cleaning the data, and unifying a data format; s2, performing entity recognition by using Stanford NLP and BERT, and establishing relation mapping among entities by using a relation extraction algorithm; s3, storing a knowledge graph by using a graph database, mapping entities to nodes, mapping relationships to edges, and establishing multiple types of indexes; s4, natural language query is converted into graph database query languages such as Cypher, and query execution is accelerated by utilizing an index of a graph database to be matched with a cache strategy; s5, on the basis of the established multi-type index, using a query mode prediction model to predict a query mode and dynamically adjusting the index; and S6, adjusting a result sequence based on user history and context information, and executing semantic reasoning to expand a query result according to a semantic concept and a hierarchical structure of the knowledge graph. According to the method, the query efficiency on the large-scale power multi-source data set is effectively improved.