164 results about "Neural network modeling" patented technology

The invention discloses an industrial equipment fault prediction method based on multi-modal data, and belongs to the technical field of specific calculation models, and the method comprises the steps: carrying out the preprocessing according to the collected multi-modal data of the operation of industrial equipment, so as to unify the format of the multi-modal data, and obtaining the structural data; extracting features of the structured data one by one according to data categories, and obtaining a multi-modal fusion feature through a dynamic fusion mechanism; according to the multi-modal fusion features, a fault prediction classification score is obtained through a deep neural network model to perform fault prediction; and when the drift parameter of the multi-modal data is greater than a preset threshold value, performing incremental training on the deep neural network model through the multi-modal data collected in real time to update parameters of the deep neural network model. Through multi-modal data unified processing, dynamic feature fusion, deep neural network modeling and an online learning mechanism, the problems of insufficient multi-modal data fusion, prediction uncertainty quantization deficiency, poor model adaptability and the like are solved.

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

The invention discloses a distributed power supply access-oriented power distribution network fault distance measurement method and system, and relates to the technical field of power systems, and the method comprises the steps: collecting the electrical parameters and operation states of distributed power supply access nodes in a power distribution network in real time, building a dynamic manifold model based on an ecological niche theory, and carrying out the calculation of the dynamic manifold model; adaptively adjusting manifold learning neighborhood parameters according to power fluctuation data included in the electrical parameters, and updating node ecological niches to reconstruct a dynamic manifold model; based on the reconstructed dynamic manifold model, fault features are extracted from three scales of a current harmonic component, a feed line inter-harmonic propagation path and whole network voltage influence, and a three-dimensional feature vector is generated through fusion of a graph correlation algorithm; based on the expanded fault sample library and the power fluctuation data, constructing a fault transfer relation model to predict a ground fault and a short circuit risk area; a fault source is modeled by adopting a topological neural network, and a fault point distance measurement value is output through state prediction and strategy deduction.

The invention provides a feeder terminal fault detection method, system and device, a medium and a program product, and the method comprises the steps: obtaining multi-source heterogeneous data comprising feeder terminal operation data, a topological graph structure of a power distribution network and external sensing data, the multi-source heterogeneous data comprises at least one kind of structured or unstructured time sequence data, and the external sensing data comprises at least one kind of structured or unstructured time sequence data; the external sensing data comprises meteorological data, geographic space information and historical fault records; and preprocessing the multi-source heterogeneous data, inputting the preprocessed time-aligned multi-source heterogeneous data into the trained time-space diagram neural network model to extract features and perform joint modeling, and outputting a fault type classification result and a fault probability distribution result corresponding to each feeder terminal node in the power distribution network. According to the method, a multi-source heterogeneous data fusion and time-space diagram neural network modeling mechanism is introduced, so that the model has a dynamic response capability to complex environment changes, and the identification precision of a potential fault mode is effectively enhanced.

The invention relates to a collaborative obstacle avoidance method and system based on multiple unmanned aerial vehicles, and the method comprises the steps: enabling each unmanned aerial vehicle to scan an environment, capturing dynamic obstacle information, carrying out the modeling of a LSTM neural network, generating a future motion trajectory probability graph, and converting the future motion trajectory probability graph into a dynamic thermodynamic diagram; then, traditional path key points are upgraded to space-time key points, and an obstacle avoidance path is planned according to the time-space key points; broadcasting a path key point sequence by each unmanned aerial vehicle, detecting conflicts and triggering priority arbitration, and adding a waiting ring to the unmanned aerial vehicle subjected to arbitration delay; meanwhile, continuously monitoring the environment, and immediately triggering emergency obstacle avoidance if the dynamic obstacle trajectory is not consistent with the prediction; according to the method, comprehensive perception is realized through multi-source dynamic data acquisition and fusion, a thermodynamic diagram is generated by space-time trajectory modeling, collaborative path planning and conflict detection arbitration are carried out, paths can be dynamically adjusted and optimized according to environmental changes, obstacle avoidance strategies are adjusted, and the obstacle avoidance efficiency is improved by means of emergency obstacle avoidance and safety redundancy design. And efficient and safe collaborative operation of the multiple unmanned aerial vehicles in a complex dynamic environment is ensured.

The invention discloses an intelligent charging and discharging management method and system for a lithium battery, and the method comprises the steps: collecting multi-dimensional parameter data of the lithium battery, and generating a data set with a timestamp; and analyzing the data features based on the battery type classification model, and determining a battery type identifier. And utilizing the recurrent neural network to model the relevance between the capacity attenuation and the health state, and predicting the residual capacity and the health score. If the capacity or health score is lower than the threshold value, extracting the environment temperature and the load demand to generate a temperature-load feature vector; and matching the candidate strategy set from the pre-established index database through the hash table. And distributing weights according to health scores and load demands, sorting strategy efficiency and life influences by adopting a linear regression model, and screening an optimal strategy. And if the strategy calculation complexity exceeds the equipment capability, iteratively optimizing parameters by utilizing a genetic algorithm, simplifying a strategy instruction, generating a charging current, a voltage curve and a discharging rate control instruction, and executing the charging current, the voltage curve and the discharging rate control instruction in real time by a battery management system. The method prolongs the service life of the battery and improves the charge-discharge efficiency.

The invention discloses a power equipment defect detection method based on image recognition, and belongs to the technical field of power equipment defect detection, and the method comprises the steps: carrying out the defect simulation based on physical mechanism driving according to an equipment three-dimensional model and physical field simulation parameters, and obtaining a defect simulation data set; according to the defect simulation data set and the real inspection data, training a cross-modal deep learning network based on physical law constraint to obtain a defect identification model; performing time-space diagram neural network modeling according to the historical time sequence inspection data and the defect identification model to obtain a state evolution model; and inputting inspection data acquired in real time into the equipment health state evolution model, and performing online reasoning to obtain a defect detection result. The problems that an existing electrical equipment defect detection method excessively depends on scarce real defect samples, the generalization ability for complex working conditions is weak, and the defect evolution trend prediction ability is lacked are solved.

The invention relates to the technical field of capacitor manufacturing and state monitoring, in particular to a capacitor aging curve optimization method based on deep learning, which comprises the following steps: step 1, multi-modal data acquisition and standardization processing; 2, modeling and training a physical constraint neural network; 3, executing a dynamic control and maintenance strategy: dynamically adjusting the switching frequency of the inverter based on a comparison result of the residual life prediction value output in the step 2 and a preset threshold value, and enabling the ripple frequency to deviate from a capacitor impedance sensitive frequency band; and according to the aging mode classification result output in the step 2, triggering a corresponding temperature sampling frequency adjustment or bus voltage reduction operation. Through fusion of multi-modal data, a physically constrained neural network model and a dynamic control strategy based on a prediction result, the aging process of the capacitor is successfully optimized, the use efficiency of the capacitor is improved, and the life cycle of the capacitor is effectively prolonged.

The invention provides a construction method and system of a rock-soil shear strength parameter prediction model, and relates to the technical field of model construction.The construction method comprises the steps that CT scanning data of a real rock-soil sample is obtained and preprocessed, and an irregular three-dimensional particle set for geometric modeling is obtained; performing particle surface roughness modeling analysis on the three-dimensional particle body set to obtain a surface roughness coefficient set of the three-dimensional particle body set; performing particle size cumulative distribution function segmentation processing and fractal dimension control on the surface roughness coefficient set to obtain a structure grading sample set; constructing a THMC multi-field coupling model based on the structure grading sample set to obtain a working condition particulate matter physical response parameter set; and performing a simulation experiment on the working condition particle physical response parameter set, and performing deep neural network modeling processing based on an experiment result to obtain a strength response model capable of predicting the cohesive force and the internal friction angle of the rock-soil body under any working condition input. According to the invention, the prediction efficiency and accuracy are significantly improved.

The invention discloses a digital twinborn monitoring method for a self-evolution concrete-filled steel tube arch bridge, and the method comprises the steps: collecting multi-source monitoring data through deploying a multi-mode sensor at a key component of a bridge, and constructing an initial structure topological graph; and dynamic evolution and anomaly detection of the structure topology are realized by using a graph neural network and a Transform structure. Intelligent recognition and fault diagnosis of a structure state are realized by constructing a multi-physical field five-dimensional tensor and a health knowledge graph. And finally, generating and optimizing a structure alternative scheme by adopting a graph generative adversarial network and a genetic algorithm, verifying the performance of the scheme through finite element simulation, and realizing intelligence of bridge health monitoring and maintenance decision making. According to the method, real-time sensing, dynamic modeling and intelligent diagnosis of the structural state of the concrete-filled steel tube arch bridge are achieved by fusing multi-modal sensor data, multi-physical field coupling analysis, graph neural network modeling and an intelligent reasoning mechanism, the real-time performance and accuracy of monitoring are improved, and a scientific basis is provided for structural optimization design of the bridge.

The invention relates to the technical field of ship design optimization, and discloses a hull shape optimization method based on neural network modeling. In the data acquisition stage of the method, initial appearance parameters and hydrodynamic performance data of a ship body are obtained, the appearance parameters comprise geometric dimensions and shape features, and the performance data comprise resistance coefficients and wave-making resistance values. In the neural network construction stage, a neural network model with a multi-layer perceptron structure is trained by using collected data, weights are updated through a back propagation algorithm, and a nonlinear mapping relation between appearance parameters and hydrodynamic performance indexes is established. In the shape optimization stage, the trained neural network model is used for carrying out iterative adjustment on the shape of the ship body, fluid dynamic performance indexes are recalculated through the model after each adjustment until preset convergence conditions are met, and finally optimized ship body shape data are output. According to the method, partial complex calculation is replaced by the neural network, and intelligent optimization of the hull appearance is realized.

The invention discloses an equipment working state recognition method based on a voiceprint recognition model, and relates to the technical field of industrial equipment operation state recognition. The equipment working state recognition method based on the voiceprint recognition model comprises the following steps: collecting operation audio waveform data of target equipment, extracting acoustic representation data containing parameters such as short-time energy, a frequency spectrum centroid, a spectrum flux, MFCC and a zero crossing rate, inputting the acoustic representation data into a pre-trained voiceprint recognition model to extract voiceprint feature representation vectors, and carrying out voiceprint feature representation on the target equipment; according to the method, the audio signal is divided into the frames, the acoustic features such as short-time energy, spectrum centroid, spectrum flux, MFCC and zero crossing rate are extracted, the inter-frame evolution relation is modeled in combination with the bidirectional neural network, and the attention mechanism is introduced to highlight the key frame segment, so that the real-time performance of the audio signal is improved, and the real-time performance of the audio signal is improved. The recognition capability of working conditions such as fuzzy state boundary or unobvious transition is effectively enhanced, and the time sequence analysis and state judgment precision is improved.

The invention belongs to the cross technical field of artificial intelligence and image recognition, and discloses a cursive script image sequence recognition method for modeling by fusing a stroke structure, and the method comprises the steps: preprocessing and normalizing a cursive script image, eliminating interference information, normalizing the form of the cursive script image, and guaranteeing the stability and effectiveness of a subsequent processing flow; based on stroke decomposition and feature coding of structural analysis, stroke-level structural features are extracted through character skeleton modeling, and cursive writing characters are converted into time series data; modeling a cursive script recognition neural network fused with structural semantics, and recognizing a stroke time sequence by adopting a BiLSTM neural network; an identification output and result optimization module is designed to ensure the final identification quality; through application of scene integration and platform deployment interface design, practical deployment of the model and butt joint of a front-end platform are realized, and a complete cursive script recognition ecology is constructed. According to the method, not only is the structured representation capability of the cursive script image improved, but also the recognition precision and generalization performance of the model on complex handwriting are remarkably enhanced.

The invention specifically relates to a photovoltaic building design and control system based on a multi-modal neural network, and relates to the technical field of building energy saving, renewable energy utilization and artificial intelligence application. Comprising a multi-modal data acquisition and fusion module, a multi-modal neural network modeling and optimization design module, a real-time control strategy generation and execution module and a digital twin platform and continuous learning module. According to the method, global optimization is designed, powerful nonlinear fitting and feature fusion capabilities of the multi-modal neural network are utilized, multi-dimensional complex factors such as climate, buildings, users and a power grid are comprehensively considered, the optimal BIPV integration scheme which is high in power generation efficiency, small in influence on building performance and good in economical efficiency is rapidly generated, and the design efficiency and the scheme quality are remarkably improved.

The invention discloses a multi-task adaptive learning method based on dynamic strategy switching. The method comprises the following steps: S1, constructing a multi-task learning model, and initializing a task encoder, a feature extraction network and a decoding module; s2, encoding an input sample into a task encoding vector; s3, extracting task feature representation; s4, inputting an improved Cross-pitch structure, and carrying out the feature cross fusion of the improved Cross-pitch structure; s5, inputting a strategy scheduling controller, and generating fusion weight and path configuration based on loss change, gradient difference and feature distance; s6, executing nonlinear cross fusion to generate shared feature representation; s7, generating a task prediction result; s8, calculating loss and updating parameters; and S9, circularly training until convergence. According to the method, adaptive regulation and control of a fusion strategy and dynamic optimization of a feature sharing structure are realized, and the method is suitable for a multi-task neural network modeling scene.

The invention discloses an error compensation method of a strain type six-dimensional force sensor based on multi-source sensing information fusion, and belongs to the technical field of intelligent sensors. The error compensation method comprises the following steps: data acquisition; preprocessing and fusing data; constructing an error prediction model by adopting a time sequence neural network; training data construction and model training; performing online error compensation and feedback; and verifying, adjusting and optimizing the system. Based on fusion of multiple sensors (IMU, a temperature module and a timer) and time sequence neural network modeling, combined online compensation of errors caused by gravity, temperature drift and time drift of the six-dimensional force sensor is achieved, the method has the advantages of being high in compensation precision, high in response speed and high in adaptive capacity, and the application requirements of various high-precision measurement and control systems are met.

The invention provides an intelligent bill recognition method based on a deep neural network model, and relates to the technical field of intelligent bill recognition based on the deep neural network model.The intelligent bill recognition method comprises the steps that various bill data are collected, image features and text features are extracted respectively and preprocessed, and complete multi-modal data support is provided for subsequent modeling; secondly, modeling image features by using a convolutional neural network and modeling text features by using a deep neural network, splicing the image features and the text features into a joint feature vector, and deeply fusing information of an image and a text modal through a cross-modal attention mechanism to enhance multi-modal relevance; and finally, designing a dynamic weight adjustment mechanism, adaptively adjusting the optimization weights of the cross entropy loss of the classification task and the mean square error loss of the field analysis task based on the cross entropy loss of the classification task and the mean square error loss of the field analysis task, and balancing optimization conflicts in a multi-task scene.

The invention relates to the technical field of industrial artificial intelligence and intelligent manufacturing, and provides a defective product root cause analysis method and system based on a multi-modal space-time diagram neural network. The method comprises the following steps: S1, constructing a dynamic factory knowledge graph; s2, multi-modal space-time diagram neural network modeling and risk prediction are carried out; s3, root cause interpretation based on an attention mechanism; and S4, reinforcement learning parameter optimization of man-machine cooperation. According to the method, the whole medicine production system is abstracted into a dynamic'factory knowledge graph ', and unified modeling is performed on complex space-time association in the graph by using a multi-modal space-time graph neural network; on the basis, autonomous and safe optimization of key process parameters is realized through a reinforcement learning module fused with expert knowledge feedback, so that a complete closed loop from sensing, diagnosis, decision making and optimization is formed.

The invention discloses an injection molding defect reason traceability system based on a knowledge graph, and the system comprises a multi-source data collection and processing module which is used for collecting and processing data in an injection molding production process; the knowledge graph construction module is used for extracting entities and relationships to generate a time-varying heterogeneous knowledge graph; the two-channel heterogeneous graph neural network modeling module is used for constructing a two-channel heterogeneous graph neural network on the time-varying heterogeneous knowledge graph; the defect traceability path searching and scoring module is used for executing traceability path searching and scoring and sorting candidate paths; the evidence fusion and cause sorting module is used for calculating a defect cause confidence coefficient and outputting a defect cause sorting result; the process adjustment scheme module is used for outputting a process adjustment scheme based on the defect cause sorting result; and the knowledge graph dynamic updating module is used for collecting an execution result of the process adjustment scheme and writing the execution result back to the time-varying heterogeneous knowledge graph. According to the invention, knowledge graph reasoning is adopted to realize injection molding defect cause intelligent traceability.

The invention discloses an intelligent fault diagnosis system for a cold and heat source center, and belongs to the technical field of fault judgment of heating, ventilation and air conditioning equipment. The system collects an operation feature vector X (t) in real time, and a state signal database stores data; a signal processing module of the upper computer subsystem adopts empirical mode decomposition X (t) to generate an IMF sequence and a residual sequence rn (t), a characteristic analysis module judges chaotic characteristics by calculating the maximum Lyapunov index of each IMF, and a neural network modeling module selects a modeling path according to chaotic / non-chaotic marks to generate a predicted value. The result integration module fuses the multi-path prediction results, compares the multi-path prediction results with a preset threshold interval, and outputs normal, early warning or fault state marks; and the result display module dynamically visualizes the state curve and the maintenance suggestion. According to the system, real-time judgment and early warning of abnormal operation of the cold and heat source equipment are realized, and decision support is provided for preventive maintenance.

The invention discloses a spiking neural network driven robot dynamic adaptive control system, which relates to the technical field of robot control, and comprises seven modules: an environment sensing module which integrates various sensors and collects and transmits environment, attitude and interaction information; the signal preprocessing module processes data through composite filtering and feature extraction; the spiking neural network modeling module constructs a three-layer structure and performs training based on a fusion learning rule; the dynamic decision output module converts the pulse signal into a control instruction and adjusts gain; the actuating mechanism driving module drives the actuator to act; the state feedback monitoring module monitors and feeds back motion parameters and system states; and the adaptive optimization module optimizes the network and module parameters based on feedback data, and dynamically matches the environment. The control precision and the response speed of the robot in a complex environment are improved, the adaptive capacity is enhanced, the operation reliability and safety are guaranteed through multi-module cooperation, and the application scene is expanded.

The invention discloses a dynamic routing optimization transmission method based on a neural network. The method comprises the following steps: S1, collecting original state data in a network environment; s2, preprocessing the original state data; s3, constructing a causal graph based on the historical abnormal event log, and performing vectorization representation on the causal graph; s4, modeling is carried out on the state features and the topological features, state and structure fusion processing is carried out, and a performance prediction tensor is output through a multi-layer perceptron; s5, constructing a joint optimization target based on the embedded vector and the performance prediction tensor, and screening an optimal path set; and S6, generating a routing control instruction according to the optimal path set, and issuing the routing control instruction to the network forwarding equipment. According to the method, causal graph embedding and neural network modeling are fused, dynamic path optimization is realized, and the method has the advantages of high intelligence, high stability and high adaptability.

The invention discloses a photovoltaic power generation power prediction and power system scheduling method and a system for realizing the method. The method comprises four steps of data acquisition and preprocessing, similar day selection and training set construction, GA (Genetic Algorithm)-fuzzy RBF (Radial Basis Function) neural network modeling and power scheduling plan generation. The method comprises the following steps: selecting a meteorological variable with high correlation degree through a Spearman rank correlation coefficient, and constructing a time sequence feature; similar days are utilized to construct training samples, and the generalization ability of the model is improved; a fuzzy RBF network optimized by GA is adopted to enhance the nonlinear modeling precision; and inputting a prediction result into the power system simulation model, and generating an optimal scheduling instruction by adopting dynamic programming. According to the method, the photovoltaic power prediction accuracy and scheduling efficiency can be remarkably improved, the power grid stability is enhanced, and the new energy consumption capability is promoted.

The invention relates to the cross technical field of material calculation science and machine learning, and particularly discloses a data collaborative optimization-based high-temperature alloy constitutive relation neural network modeling method, which comprises the following steps of: obtaining an original experimental data set of a high-temperature alloy material, and preprocessing the original experimental data set to obtain a preprocessed experimental data set; generating a test data set according to the original experiment data set; training a neural network model according to the preprocessed experimental data set; testing and verifying the trained neural network model according to the test data set; and inputting the current temperature, the current strain rate and the current strain of the high-temperature alloy material into the trained neural network model for stress prediction so as to output the predicted stress of the high-temperature alloy material. According to the invention, through the strong nonlinear fitting capability of the neural network, the stress response of the high-temperature alloy under complex conditions can be predicted with high precision; and a standardized data processing flow ensures a model training effect.

The invention discloses a natural gas production yield prediction method and system based on deep learning, and the method comprises the steps: obtaining dynamic production data, geological parameters and environmental factor data, carrying out the preprocessing of the collected data, and carrying out the multi-scale decomposition through EMD (Empirical Mode Decomposition), and obtaining the multi-scale feature data; capturing instantaneous fluctuation features based on an LSTM (Long Short-Term Memory) network, extracting a periodic trend through a CNN (Convolutional Neural Network), modeling spatial relevance between gas wells by utilizing a GNN graph neural network, and introducing an attention mechanism to dynamically weight and fuse each branch feature to obtain a mixed neural network model; and inputting the multi-scale feature data into the target hybrid neural network model for prediction, and outputting a yield prediction result. And data can be comprehensively understood, so that the yield prediction precision is improved.

The invention relates to the technical field of industrial safety monitoring, in particular to a fallen object accurate identification FOD calculation method based on a fixed tower and application of the fallen object accurate identification FOD calculation method. Comprising the following steps: synchronously acquiring multi-source heterogeneous sensing data; carrying out multi-modal data preprocessing and feature extraction; performing cross-modal feature fusion and representation construction; modeling and reasoning a time-space diagram neural network; and performing multi-dimensional threat quantification and decision output. According to the design of the invention, multi-modal sensor groups are deployed at the tower bottom, the tower middle and the tower top of a fixed tower in a layered manner, and a high-definition multispectral camera, a high-density laser radar and a distributed acoustic sensor are integrated, so that 7 * 24-hour full-automatic data acquisition is realized; according to the design, the time and environment limitation of manual inspection is eliminated, and the complex weather detection capability is improved by 80%; meanwhile, the sensor synchronously triggers the mechanism and the grading detection process, the single-frame data processing delay is less than or equal to 50ms, and the real-time monitoring requirement of an aircraft take-off and landing high-frequency scene is met.

The invention relates to the field of digital marketing, and discloses a video understanding-oriented hierarchical spatio-temporal semantic extraction method, which analyzes video content through a three-stage progressive analysis framework: firstly, detecting an object instance in a key frame sequence and constructing a spatio-temporal trajectory to ensure object identity continuity; modeling an object state evolution law by using a recurrent neural network, and quantifying an interaction relationship between objects; and finally, constructing an event causal graph based on the state mutation points, and generating a directed causal chain through a statistical causal test algorithm. Video content is abstracted into event causal logic from pixel features, a semantic path of'track-state-causal 'is formed, structured semantic description of decisions can be directly driven, video semantics and user behavior data are coupled, user preference portraits are dynamically constructed, and personalized material generation is guided.

The invention discloses a system call behavior modeling method based on a graph neural network, and the method comprises the following steps: S1, collecting system call event data, and carrying out the standardization processing of a resource identifier to generate a uniform resource identifier; s2, constructing a double-layer hypergraph model based on a uniform resource identifier, and jointly establishing a calling-resource layer and a constraint layer; s3, executing graph neural network modeling on the double-layer hypergraph model, and fusing two layers of embedding to generate a unified representation; s4, calculating joint loss and optimizing parameters of the double-layer hypergraph model by utilizing unified representation in a training stage; s5, adopting a reversible sliding window in a reasoning stage, outputting an abnormal score and generating a structure reconstruction plan; and S6, generating an anti-fact explanation based on the structure reconstruction plan, and outputting a calling set and a related identification sequence. According to the method, the double-layer hypergraph is constructed, and the graph neural network is combined for modeling, so that accurate detection and interpretable analysis of the system calling behavior are realized.

The invention discloses a dynamic graph distribution external detection method and system based on spectrum sensing enhanced evidence learning, and belongs to the field of graph data mining and anomaly detection. In order to solve the problem that distribution outside samples in a dynamic graph structure are difficult to accurately identify, a virtual negative sample generation method based on graph spectrum disturbance is mainly adopted, Dirichlet posterior distribution is modeled in combination with a dynamic graph encoder and an evidence neural network, and the distribution outside samples are discriminated by using uncertainty measurement. According to the method, the distributed external detection performance in a dynamic graph environment can be effectively improved, and the method has relatively high robustness and generalization ability.

The invention belongs to the technical field of power system data management and intelligent scheduling, and discloses a power grid data consistency detection method combining a knowledge graph and an AI algorithm, which realizes consistency intelligent detection of power grid multi-source heterogeneous data by fusing the knowledge graph and a graph neural network algorithm. The method has the advantages that the problems that semantics are not uniform and structural conflicts are difficult to recognize in a traditional method are effectively solved, and the accuracy and expansibility of data fusion are improved; secondly, the graph neural network is used for modeling power grid topology and state characteristics, so that the structural anomaly recognition capability and the detection precision are remarkably enhanced; thirdly, an interpretability mechanism is introduced, so that an abnormal result has a tracing path and a logic description, and data management and operation and maintenance decision making are facilitated; and 4, the maintenance cost of the rule engine is reduced, the self-adaption and intelligent upgrading of the model is realized, and the automation and intelligent level of power grid data management is comprehensively improved.