2559 results about "Feature Dataset" patented technology

The invention relates to the technical field of intelligent operation and maintenance, and discloses an intelligent operation and maintenance method fusing multi-modal data and active learning, and the method comprises the steps: deploying a hierarchical Internet of Things equipment network in a target operation and maintenance region, collecting a heterogeneous operation and maintenance data set, and generating an operation and maintenance feature data set through employing a unified building operation and maintenance service framework cooperating with a plurality of MCPs; inputting the operation and maintenance feature data set and the user feedback information into a deep learning intention recognition model for intention recognition and demand analysis to obtain structured user demand data and an operation and maintenance task priority sequence; according to the operation and maintenance feature data set and the structured user demand data, performing real-time evaluation on the equipment operation state to obtain fault risk early warning data; autonomous decision analysis is carried out through an agent type artificial intelligence engine, an equipment maintenance scheme and a resource scheduling scheme are generated, and then the problems that in traditional operation and maintenance, fault prediction is single, the model adaptability is poor, and prediction results are difficult to convert into effective decisions are solved.

The invention discloses a ring main unit monitoring method and system based on the Internet of Things, and the method comprises the steps: building a multi-source data collection network of a ring main unit, extracting the multi-dimensional time-frequency domain features of a signal through an edge computing device, generating a multi-source feature data set, and constructing a dynamic digital twinborn model, real-time mapping of the running state of the ring main unit is achieved through a physical-virtual synchronization algorithm, and twin data volumes are output; according to the node influence coefficient of the twin data volume, analyzing a topological structure of a ring network system by adopting a graph neural network, identifying a key fault propagation path and generating a priority monitoring node list; based on the priority monitoring node list and the twin data volume, dynamically generating an adaptive monitoring strategy through a reinforcement learning algorithm, executing the adaptive monitoring strategy and collecting feedback data in real time, and completing closed-loop iteration of the monitoring strategy through an online learning algorithm; through closed-loop control of multi-source perception-digital twinning-intelligent decision, high efficiency, intelligence and reliability of ring main unit monitoring are realized.

The invention discloses an intelligent file classification and retrieval method and system, and the method comprises the steps: carrying out the unified format conversion and feature extraction of text, image and audio data according to the original data format of a file through a multi-modal data fusion algorithm, and obtaining a standardized file feature data set; based on a deep learning adaptive classification model, performing classification processing on the standardized archive feature data set to generate multi-level archive classification labels; analyzing the semantic relationship between the archive classification labels through an enhanced semantic network construction technology, generating a semantic association map, and optimizing the semantic association map to obtain an optimized semantic association map; and according to a retrieval request input by a user, performing semantic matching and correlation sorting by utilizing the optimized semantic correlation graph, and outputting archive information most related to the retrieval request. By utilizing the embodiment of the invention, the relevance and accuracy of retrieval can be improved, and modernization and intellectualization of archive management are promoted.

The invention relates to the field of road compactness monitoring, in particular to a road compactness real-time regulation and control method and system based on digital twinning, and the method comprises the steps: collecting vibration acceleration, temperature and position data, and generating a time-space aligned multi-source fusion feature data set after processing; inputting a pre-trained LSTM model to construct a dynamically updated digital twinborn body, and outputting a three-dimensional compaction energy spectrum; dispersing the atlas and calculating parameters, and generating a compaction degree deviation matrix; a regulation and control instruction is generated and issued based on matrix optimization; and according to the measured data and the predicted value residual error, triggering re-optimization and calibrating the sensor. According to the method, the problems of non-uniform compaction quality and over-high energy consumption caused by poor adaptability, decision lag and error coupling of a traditional static model are solved.

The invention provides a prediction model construction method and system for personalized equipment operation and maintenance, and relates to the technical field of data processing, and the method comprises the steps: collecting equipment operation data and environment data, extracting the change characteristics of the equipment operation state, carrying out the correlation analysis of the environment data based on historical equipment fault records, and obtaining a prediction model. Performing normalization processing, performing time sequence analysis, generating a joint feature matrix, segmenting the joint feature matrix into time slices of a fixed window according to a time sequence, classifying the time slices into a normal state and an abnormal state, performing feature contribution degree analysis, generating a feature weight matrix, and performing feature extraction; re-combining the equipment features in the time slice feature data set with the environment factors according to feature weights, generating fusion feature data layer by layer, performing supervised learning training on the fusion feature data, constructing an equipment prediction model, and outputting the equipment prediction model as a prediction model of personalized equipment operation and maintenance; according to the invention, the operation and maintenance effect of personalized equipment is improved.

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The invention relates to the technical field of detection, and discloses a polyurethane material performance detection method, which comprises the following steps: clamping a material to be detected on a loading platform, cooperatively controlling and simulating the stress effect of a target application scene, and capturing a time sequence image of the microstructure of the surface of the material by adopting a visual unit; synchronously acquiring internal strain distribution data through a distributed strain sensing array, acquiring a dynamic stress response spectrum in combination with a piezoelectric mechanical sensor, and generating a multi-modal deformation characteristic data set; performing crack density and texture orientation analysis on the time sequence image, extracting a crack growth rate and a direction consistency coefficient, and marking a strain concentration area; calculating an apparent elastic modulus attenuation rate based on the dynamic stress response spectrum, establishing a material performance degradation model, generating a dynamic flexible performance index through a weighted fusion algorithm, and performing graded evaluation on the material; material detection is upgraded from a single mechanical test to a multi-physics coupling intelligent evaluation system, and accurate and reliable detection support is provided for multiple application scenes.

The invention discloses an e-commerce intelligent operation monitoring and collaborative decision-making method based on big data analysis, and the method comprises the following steps: S1, collecting multi-source heterogeneous data, and constructing an original data set; s2, performing data cleaning and characterization processing to generate a feature data set; s3, constructing a knowledge graph and dynamically updating the knowledge graph by using the graph embedding model; s4, generating potential causal paths and weights in combination with the neural relationship reasoning model and the diffusion model, and introducing a microknowledge enhancement layer to generate causal enhancement features; s5, identifying abnormal behaviors in combination with a multi-level anomaly detection mechanism; s6, designing a dynamic game optimization model, determining a collaborative optimal solution under a multi-target conflict through Nash equilibrium, and processing a dominant and subordinate relationship in combination with a Stackelberg game to generate a collaborative optimization strategy; and S7, displaying the monitoring state and the collaborative optimization strategy through a visual interface. According to the invention, big data analysis, a knowledge graph and a dynamic game technology are combined, so that e-commerce intelligent operation monitoring and collaborative decision-making are realized.

The invention relates to a multimedia equipment control method and system based on adaptive protocol matching. The method comprises the following steps: firstly, collecting communication protocol data and extracting features to form a protocol feature data set; secondly, performing confidence coefficient verification on a data set type identification result, and if the confidence coefficient is lower than a threshold value, extracting a depth feature through a convolutional neural network; and calling a national standard protocol adaptation conversion engine based on a classification result, converting the heterogeneous protocol into data in a unified format, and generating a secure communication data frame through national secret algorithm encryption and bidirectional identity verification. And finally, performing hash check and structured analysis on the data frame, constructing an interaction information packet in combination with user behavior characteristics, and dynamically optimizing a control strategy through real-time feedback data to generate a self-adaptive interaction control scheme. According to the method, through a technical closed loop of intelligent analysis-standard conversion-security reinforcement-closed loop optimization, the high efficiency, the intelligent level and the security performance of multimedia equipment management are remarkably improved.

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The invention discloses a network security threat intelligent identification and defense method based on artificial intelligence, and the method comprises the steps: S1, collecting network flow data, user behavior data and system log data from a network security system, and outputting a finally converged network feature data set; s2, performing global optimization search on the network feature data set by applying a hyper-entropy differential evolution algorithm to generate network feature data optimized by hyper-entropy differential evolution; s3, constructing a network traffic anomaly detection probability model based on variational Bayesian reasoning by using the network feature data after hyper-entropy differential evolution optimization; s4, inputting the network feature data set into the network traffic anomaly detection probability model, and evaluating the risk probability of an abnormal behavior; and S5, generating a self-adaptive defense strategy based on a detection result, and forming defense strategy data. According to the invention, the overall stability and defense capability of the network security system are improved.

The invention relates to the field of industrial robots, and discloses an industrial robot real-time maintenance system and method in combination with edge computing, and the method comprises the steps: obtaining the multi-source operation state data of an industrial robot, and constructing an operation feature data set of key parts of the robot in combination with a state coding mechanism of an edge end and a feature coupling analysis method; carrying out rapid distributed processing on the operation characteristic data set, constructing an equipment health state model based on a lightweight time sequence modeling algorithm, and introducing a multi-dimensional correlation analysis mechanism to carry out incremental learning on the model; judging whether the edge side state recognition result is stable or not based on the change trend of the trigger frequency; according to the corrected state mapping relation, performing response level division on the potential fault trend by applying a multi-scale fault prediction mechanism, and extracting matched maintenance plan parameters; and based on the maintenance scheduling plan, in combination with a preset fault handling knowledge base, performing automatic evaluation and optimization on the current maintenance strategy. The method has the advantage of improving the response speed.

The invention belongs to the technical field of industrial data analysis, and discloses a variational auto-encoder heat metering data anomaly detection method fusing multi-head attention, which comprises the steps of collecting original heat metering data to construct a feature data set, taking feature data in the feature data set under a normal working condition as a training set, and taking the remaining feature data as a test set; taking a variational auto-encoder as an anomaly detection model; training an anomaly detection model by using the training set, and calculating a loss function in the training process to update parameters of the anomaly detection model; after training is finished, the test set is used for testing the anomaly detection model, and if the test is passed, the anomaly detection model is output for real-time anomaly detection of the heat metering data; otherwise, training the anomaly detection model again. The method can better adapt to a complex heat supply system environment, the dependence on labels is reduced, and the anomaly detection accuracy is improved.

The invention discloses a bridge health state detection early warning method and system based on image recognition, and relates to the field of bridge health monitoring, and the method comprises the steps: collecting a bridge crack image, carrying out the preprocessing of the bridge crack image, and forming a bridge crack image data set; carrying out manual amplification on the bridge crack image data set through a sliding window algorithm, and carrying out feature extraction to generate a feature data set; performing model training by adopting a convolutional neural network to obtain a crack recognition model; acquiring bridge health state data in real time through a sensor, and performing crack detection by using a crack recognition model; modeling the health state of the bridge by adopting a time sequence analysis method, and generating a dynamic damage prediction result; carrying out bridge health state evaluation, and carrying out early warning on bridge damage; according to the invention, amplification and feature extraction of the bridge crack image are carried out through the adaptive sliding window algorithm, and the precision and robustness of crack identification are improved in combination with the convolutional neural network and the dynamic attention mechanism.

The invention discloses a knowledge and data fused chronic disease collaborative management method and system, and relates to the technical field of medical management. Comprising the following steps: generating a feature data set, processing the feature data set by using a feature gain modeling method, and generating an enhanced feature data set; dynamically generating a chronic disease comprehensive knowledge graph, performing node supplementation and weight optimization on the knowledge graph, and generating a specific knowledge sub-graph of the patient; carrying out multi-time sequence prediction by utilizing the specific knowledge sub-graph, and carrying out short-term and long-term trend prediction; a patient health track model is constructed through the state transition probability in combination with the prediction result; according to the dynamic risk labeling result of the health track model, the health state of the patient is comprehensively evaluated in combination with the specific knowledge sub-graph, and personalized management suggestions are generated according to the evaluation result. According to the collaborative management platform, cooperation between different medical institutions and medical staff can be promoted, information sharing and resource integration are realized, and the overall level of medical services is improved.

The invention relates to the technical field of streaming media decoding, and discloses a streaming media decoding method and device for a network high-definition player and a storage medium, and the method comprises the steps: carrying out the multi-thread parallel collection and storage of real-time temperature data, network bandwidth jitter data and video code rate fluctuation data of a decoding chip in the network high-definition player; obtaining a streaming media dynamic feature data set; performing classification training to obtain an inter-frame correlation prediction model; constructing a multi-stage streaming media caching system based on the inter-frame correlation prediction model; monitoring a cache state, a network state and a chip state in the multi-stage streaming media cache system in real time to obtain a decoding event sequence; according to the decoding event sequence, the decoding parameter is adjusted in real time, and the streaming media decoding control instruction is obtained.The collaborative optimization of a network layer, a decoding layer and a display layer is achieved, the playing starting delay is reduced, and the overall stability of a network high-definition player is improved.

The invention relates to the technical field of inspection monitoring, and discloses an unmanned aerial vehicle intelligent inspection monitoring method and system based on a sensor, and the method comprises the steps: obtaining an initial inspection data set; obtaining a feature data set; generating a unified target feature data set; performing anomaly detection on the target feature data set to obtain an anomaly inspection area data set; performing security level division on the abnormal inspection area to obtain a division result; performing risk degree screening on the safety risk area in the division result to obtain a plurality of high-risk point position types, and when the change threshold value of one high-risk point position reaches a preset threshold value, preliminarily determining a high-risk occurrence zone; the unmanned aerial vehicle is controlled to carry out spot hovering to carry out key monitoring and refined inspection on the high-risk occurrence zone, and a secondary inspection data set is obtained; obtaining an analysis result; and secondarily confirming that the current inspection area is in the high-risk zone, and generating a corresponding emergency response early warning strategy and a corresponding regulation and control strategy, thereby accurately monitoring the inspection area in real time.

The invention relates to the technical field of image recognition processing, in particular to a black and odorous water body recognition method and system based on image recognition processing. The method comprises the following steps: based on black and odorous water body image data, adjusting a pixel value range through sampling, comparing color channel distribution, extracting gray value distribution characteristics, analyzing partition brightness values, and generating a basic characteristic data set; according to the method, the pixel value range is adjusted, the gray value distribution characteristics are extracted, the partition brightness value analysis is carried out, the characteristics of the water body pollution area are refined, the texture gradient variation and the gray value distribution are analyzed, the pollution area boundary is divided in combination with accumulation analysis and edge strength, and the pollution types are classified in combination with geometric parameters, gray values and color channel changes. The diffusion direction and radius are analyzed through vectorization expansion, the coverage range is constructed in combination with density and path information, the accumulated pollution value is measured and calculated, the pollution identification accuracy and the dynamic tracking capability are enhanced, and the evaluation systematicness is comprehensively improved.

The invention provides a tunnel surrounding rock blasting excavation parameter setting method and system based on a neural network, and relates to the technical field of tunnel construction, and the method comprises the steps: obtaining geological characteristic data, blasting excavation historical records and excavation contour data of surrounding rock; sparse subspace clustering processing is carried out according to blasting excavation historical records to obtain a feature data set; performing stress state analysis according to the geological characteristic data, the excavation contour data and the characteristic data set to obtain a dynamic stress state and topological change data of the surrounding rock; performing association rule mining according to the blasting excavation historical record and the feature data set to obtain an optimized decision rule; performing nonlinear finite element analysis according to the dynamic stress state and the topological change data to obtain an equivalent material model of the surrounding rock; and according to the equivalent material model and the optimization decision rule, a blasting excavation parameter setting scheme is obtained. According to the invention, dynamic response analysis is carried out by using the neural network, and real-time monitoring of the surrounding rock under different blasting conditions is realized.

The invention belongs to the technical field of intelligent lighting, and discloses an intelligent adjustment method and system for the spectrum of a quantum dot light-emitting lamp. The objective of the invention is to solve the problem that an existing lighting system cannot meet personalized healthy lighting requirements. The method comprises the following steps: firstly, acquiring user biological rhythm, environment spectrum, use scene, user preference and physiological health data through multi-dimensional data acquisition, and constructing a feature data set; then human factor optical feature extraction and cross-situation demand analysis are carried out, and a spectral response model is established; carrying out quantum dot material photoelectric characteristic mapping and spectrum synthesis modeling based on the model, and determining a luminescence parameter space; constructing a spectrum optimization model, performing multi-objective optimization by comprehensively considering visual comfort, energy efficiency and health influence, and generating a dynamic spectrum regulation and control strategy; closed-loop verification and adaptive optimization are carried out based on real-time physiological feedback, and an intelligent spectrum adjustment scheme is formed. According to the invention, the advantage of spectrum adjustability of the quantum dot material is fully exerted, and real personalized intelligent illumination is realized.

The invention discloses an Internet-based artificial intelligence power dispatching management system, which relates to the technical field of power system intelligent dispatching and comprises a distributed data acquisition module, a deep neural network module, a hierarchical reinforcement learning module, an intelligent execution module and a safety management module. The distributed data acquisition module acquires operation data of the power system through a multi-dimensional sensor array to form a feature data set; the deep neural network module processes the feature data set based on a double-layer cross validation mechanism to generate state evaluation data; the hierarchical reinforcement learning module constructs a multi-target power dispatching model and dynamically generates a control parameter matrix; and the intelligent execution module realizes parameter self-tuning through a built-in self-correction controller. According to the method, an improved Transform structure is designed, deep extraction is performed on the characteristics of the power system by fusing the space attention network and the time sequence attention network, and the model performance is optimized by adopting a three-stage strategy, so that the accuracy of state evaluation is improved.

The invention provides an intelligent monitoring and early warning method and device for photovoltaic energy storage equipment, and the method comprises the steps: carrying out the multi-time scale sliding window feature extraction of the operation data of a photovoltaic energy storage system, calculating a power coupling degree quantitative index and a system response feature index, and generating a real-time feature data set; based on the real-time feature data set, performing deviation propagation path identification under thermal dynamic constraint to obtain a real-time deviation propagation path diagram marking deviation sensitive nodes; performing multi-level dynamic deduction in combination with the real-time feature data set and the deviation propagation path map to obtain a dynamic deviation accumulation situation map displaying a deviation accumulation risk level and a development trend; and performing adaptive threshold early warning judgment according to the dynamic deviation accumulation situation map, and executing response control according to a hierarchical early warning mechanism to obtain a hierarchical early warning response control instruction. According to the method, the cumulative effect of the power prediction deviation can be effectively identified, and early warning of coordination imbalance of the photovoltaic energy storage system is realized.

The invention relates to the technical field of fault prediction, and discloses a construction waste intelligent sorting and cyclic regeneration system which comprises an acquisition subsystem used for installing sensor arrays on the working face of a crusher and the edge of a vibrating screen of a construction waste sorting device and acquiring a feature data set; the simulation subsystem is used for establishing a simulation model of the construction waste sorting device and generating a simulation data set; the actual measurement subsystem is used for collecting an actual measurement abrasion data set with labels under the actual construction waste sorting working condition; the training subsystem is used for inputting the simulation data set and the actually measured wear data set with the label into an adaptive domain adversarial neural network for training to obtain an equipment wear degree prediction model; and the output subsystem is used for identifying wear fault characteristics through the equipment wear degree prediction model and outputting a fault closed-loop response scheme, so that the feeding rate, the crushing rotating speed, the screening frequency and the screening amplitude of the sorting device are dynamically adjusted.

The invention provides an intelligent network connection automobile abnormity detection system and method based on big data analysis, and the method comprises the steps: obtaining metadata in the communication between a vehicle CAN bus and a cloud, the metadata comprising a protocol type and a load feature, carrying out the analysis of the load feature according to the protocol type, and extracting a key field of a data package; constructing a load feature matrix according to the key field, and normalizing the heterogeneous data to obtain a feature data set in a uniform format; a mahalanobis distance algorithm is adopted to calculate the distance between each data point in the feature data set and a preset base line, and if the distance exceeds a preset threshold value, abnormal traffic is judged, and an abnormal traffic mark list is generated; fingerprint features of a transmission link are obtained from the 5G network slices, the fingerprint features of different slices are subjected to standardization processing, and a cross-slice consistent fingerprint feature data set is generated; therefore, security threats in a complex Internet of Vehicles environment can be effectively handled, and the accuracy and reliability of abnormal traffic detection are improved.

The invention relates to the field of new energy charging, in particular to a new energy charging management method for a smart city. The method comprises the steps that a municipal vehicle operation feature data set is acquired, on the basis, municipal vehicles are subjected to type division through a vehicle type classification strategy, and a municipal vehicle driving type set is determined; acquiring a municipal vehicle task set, and on the basis, tracking a dynamic task variable set of a task execution vehicle in a municipal vehicle driving type set in a task execution process; according to the dynamic task variable set, analyzing an association relationship between the dynamic task variable set and the real-time power consumption data through a multi-modal fusion strategy, and determining dynamic power change information; and acquiring a charging station information set, dynamically optimizing a charging decision in combination with the municipal vehicle task set and the dynamic electric quantity change information, and determining and outputting an optimal charging strategy. In the municipal vehicle charging decision-making process, the utilization rate of charging resources is improved, and the continuity of municipal task execution is ensured.

The invention discloses an intelligent detection method for product defects on an automatic production line and a detection system based on machine vision, and relates to the technical field of industrial product quality detection. According to the method, a multi-waveband imaging technology is combined with temperature and chemical component information to generate a multi-dimensional feature data set, and an unsupervised learning algorithm is utilized to perform clustering analysis, so that the limitation of traditional single-waveband imaging is broken through, product features are comprehensively captured, the defect detection range and accuracy are remarkably improved, and a foundation is laid for subsequent analysis and classification; further utilizing an attention mechanism and a deep learning technology to accurately position and classify defects, and triggering deep scanning through a priority index to improve the detection precision and the system adaptability; and finally, combining with a random forest algorithm to analyze defect influence, and feeding back and optimizing production parameters through a dynamic adjustment mechanism, thereby realizing production optimization closed-loop management, and improving production efficiency and product quality detection.

The invention discloses a debris flow disaster early warning method and an emergency response system based on big data analysis, and belongs to the technical field of geological monitoring, and the method specifically comprises the steps: collecting and preprocessing watershed terrain, weather, geology, vegetation coverage and historical debris flow event record data, and obtaining image data through combining a remote sensing technology; extracting and integrating the data and image features to form a debris flow feature data set; a debris flow early warning model is constructed by using a finite volume model, a deep learning algorithm and an Internet of Things detection technology in combination with an integrated learning method, and training and verification are performed through historical data; when the model predicts a risk, issuing early warning information and starting a dynamic response mechanism; and finally, the early warning model is optimized and adjusted according to response effect feedback. According to the invention, the accuracy and timeliness of debris flow early warning are improved, and support is provided for disaster prevention and reduction.

The invention provides an automatic pavement settlement monitoring method and system based on a shield undercrossing airport runway. The dynamic database is constructed by collecting operation parameters such as shield tunneling machine propelling pressure, cutterhead torque and synchronous grouting pressure in real time and combining three-dimensional deformation data obtained through laser scanning. Strain sensing units are arranged in the soil layers to form a layered monitoring network, strain changes of the soil layers of different depths are collected, and spatial correlation is established between the strain changes and the tunneling position. And analyzing a spatial attenuation rule through a dynamic association processing unit, and generating a multi-dimensional feature data set. And establishing a settlement prediction model, fusing the shield dynamic load sequence and the layered strain conduction characteristics, and outputting settlement distribution information. And the system automatically compares the settlement difference value of the adjacent areas with a preset safety threshold value, and generates a graded early warning signal after verification of the three-dimensional deformation data space. According to the technical scheme provided by the invention, real-time three-dimensional prediction and spatial verification type graded early warning of settlement of the airport runway are realized.

The invention discloses an electric power analysis method and system based on artificial intelligence, and relates to the technical field of electric power system intelligent analysis. Establishing a hierarchical depth map neural network model, and setting a cross-layer information interaction channel to connect each sub-network; executing a bidirectional knowledge distillation algorithm, extracting a rule set from neural network output, and forming a parameter-rule bidirectional mapping matrix; deploying a federal reinforcement learning architecture, performing parameter aggregation on the local models on the distributed power nodes, and generating a power system state evaluation result and a risk coefficient matrix; and starting the self-repairing intelligent agent network, calculating a regulation and control parameter set of the multilevel power system, and outputting a regulation and control instruction. According to the invention, through the hierarchical depth map neural network and a cross-layer information interaction mechanism, collaborative optimization among all levels of the power system is realized, and the operation efficiency and stability of the system are improved.

Systems and methods for machine learning model retraining is described. In one example, a system includes a computing device that is configured to determine an anomaly score from a first input of a featurized data set for a machine learning model of a deployment environment. The computing device is configured to determine a feature correlation score for the machine learning model based at least in part on a second input of a featurized historical data set for the machine learning model. A model retraining frequency time period for the machine learning model is determined based at least in part on the feature correlation score and the anomaly score.