3284 results about "Long short term memory" patented technology

The invention relates to the technical field of bearing fault detection, and discloses a bearing fault detection method and system based on a health state index. The method comprises the following steps: collecting multi-source sensing signals at least comprising a vibration signal, a temperature signal and an acoustic signal during bearing operation; respectively performing time domain feature extraction and frequency domain feature extraction on the multi-source sensing signals, and performing normalized fusion on the extracted time domain features and frequency domain features to generate a multi-dimensional health state index sequence; constructing a long-short-term memory network model based on an attention mechanism, inputting the multi-dimensional health state index sequence into the model for training, and outputting a bearing health state prediction sequence; and calculating a dynamic early warning threshold according to the historical health state prediction sequence, comparing the current prediction value with the dynamic early warning threshold in real time, and generating a fault early warning signal. The method can improve the accuracy of bearing health state evaluation and fault early warning, and is suitable for complex operation conditions.

The invention relates to the technical field of intelligent power grids, in particular to a power distribution network battery digital dynamic management system based on digital twinning. Comprising a data acquisition unit; the digital twinborn modeling unit is used for constructing a battery-power grid-environment multi-dimensional dynamic twinborn body and realizing virtual-real bidirectional mapping and adaptive updating by combining a multi-physics field coupling model and a long-short-term memory network time sequence prediction algorithm; a dynamic optimization unit; and executing the feedback unit. Through a distributed heterogeneous sensing network of a data acquisition unit, multi-dimensional operation data of a battery pack and a key node of a power distribution network are acquired, and a high-fidelity data set containing four-dimensional labels of a battery state, a power grid parameter, time and a position is generated in combination with a spatial-temporal feature extraction technology; the deep fusion of the full life cycle state of the battery and the global operation data of the power distribution network is realized, and the comprehensive data support covering the global is provided for the optimization decision.

The invention discloses a real-time surrounding rock deformation monitoring and data acquisition method and system, which is applied to long-distance weak surrounding rock tunnel construction, and comprises the following steps: determining the dynamic change trend of underground water seepage rate and ground stress distribution gradient by adopting a time sequence analysis method; based on the trend, carrying out risk partitioning on the tunnel construction section by adopting a K-means clustering algorithm, determining a deformation sensitive area, and optimizing the spatial distribution of the monitoring points according to the deformation sensitive area; monitoring data are acquired in real time, and when the data fluctuation period exceeds a threshold value, the data acquisition frequency of the corresponding monitoring point is automatically improved; processing high-frequency acquired data by adopting a long-short-term memory network to obtain a real-time surrounding rock deformation prediction result; the prediction result and the multi-source real-time geological parameters are fused, a Bayesian updating method is adopted for processing, a quantitative surrounding rock stability evaluation result is obtained, closed-loop self-adaptive optimization of a monitoring scheme and accurate risk prediction are achieved, and the safety early warning capacity of tunnel construction and the utilization efficiency of monitoring resources are remarkably improved.

The invention provides a BIM intelligent management platform oriented to a whole life cycle of project construction. A building information model, Internet of Things sensing data and a block chain evidence storage mechanism are integrated through a multi-source data fusion technology, and a whole-process data chain of association planning, design, construction, operation and maintenance is associated. The platform adopts space optimization Huffman coding to realize model lightweight, combines a constraint genetic algorithm to optimize a construction path, and applies a bidirectional long-short-term memory network to analyze an equipment state. A three-chain block chain system is reconstructed on the architecture, intelligent association of engineering quantity and payment nodes is realized through cooperation of a main chain, a calculation quantity side chain and an auditing side chain, and mobile terminal offline interaction is supported based on a digital-analog separation technology. The platform covers an intelligent design management unit, a block chain investment management unit, a dynamic correction management unit, a quality safety responsibility tracing unit, an NLP risk management unit and a digital twin operation and maintenance unit. The units achieve cross-system cooperation through a unified data bus, and a closed-loop management architecture covering the whole life cycle of project construction is formed.

The invention discloses a safety monitoring system of a liquid cooling over-charging pile, and relates to the technical field of over-charging pile monitoring, the system comprises a data acquisition module, a data processing and analysis module, a dynamic model construction module, a temperature prediction module and a safety early warning module; according to the method, the dynamic model is constructed through the long short-term memory network LSTM, the complex nonlinear relation and the time sequence dependence of the multi-dimensional data are mined by using the gating mechanism of the dynamic model, the accurate characterization of the operation state of the liquid cooling over-charging pile is realized, the actual operation state of the equipment can be accurately described, the temperature data time sequence modeling is performed through the LSTM, and the accuracy of the temperature data time sequence modeling is improved. Parameters such as multi-source temperature and cooling liquid flow are fused, real-time prediction of the temperature change trend is achieved, the defect that a traditional algorithm is insufficient in temperature time sequence dependence capture is overcome, temperature abnormity can be recognized in advance, a safety threshold value is dynamically adjusted through a fuzzy logic algorithm, and self-adaptive threshold value adjustment is achieved in combination with parameters such as charging power. The problem that a traditional fixed threshold value is poor in adaptability is solved, and the early warning accuracy is improved.

The invention relates to a time sequence knowledge graph multi-hop reasoning method and system oriented to the legal field. The method comprises the following steps: establishing a dynamic mapping relationship through a semantic association technology; extracting life cycle states of the legal provisions, and introducing a legal conflict detection algorithm; setting a revocation influence factor; determining time embedding representation, and introducing clause conflict matrix elements to construct a time sequence knowledge graph; constructing a space-time coupled completion model based on the graph attention network and the long short-term memory network to perform path completion; determining three selected agents, performing reinforcement learning, designing a reward function for each agent, constructing a collaborative arbitration mechanism in combination with a dynamic priority strategy and a weight adaptive algorithm, and performing multi-hop reasoning. By collecting multi-source legal data, the authority, the real-time performance and the relevance of the data are ensured; according to the method, the path reasoning capability in a low-coverage scene can be improved, so that a complex legal knowledge multi-hop path reasoning task can be accurately and efficiently completed.

The invention provides a water conservancy project safety monitoring method and system based on data processing, and relates to the technical field of monitoring, and the method comprises the steps: obtaining and carrying out the multi-dimensional preprocessing of water conservancy project multi-source heterogeneous monitoring data through the deployment of a sensor network, and extracting multi-scale space-time fusion features from the data; performing structural state modeling, anomaly prediction, risk assessment and early warning by using a long-short-term memory neural network model integrated with a multi-head attention mechanism; and intelligent suggestions oriented to maintenance decisions are generated, so that comprehensive, accurate and prospective evaluation and early warning of the structural state of the water conservancy project are finally realized, the exception identification and risk prediction capabilities are effectively improved, the false alarm rate is reduced, refined and initiative intelligent maintenance decisions are provided, resource allocation is optimized, and the service life of the project is prolonged.

The invention relates to the technical field of robot control, and discloses a heavy-load robot motion trail method and system based on machine learning. The method comprises the steps that historical movement track data of the heavy-load robot in a working scene are collected, and the data comprise a joint position sequence, an end effector pose sequence and environment obstacle distribution information; the data is preprocessed, track features are extracted, a space-time correlation matrix is constructed, and the matrix is used for representing the dynamic coupling relation between joint movement and the tail end pose; training a trajectory prediction model containing a long and short-term memory network and an attention mechanism based on the matrix, and generating a collaborative mapping relation between a joint position and a tail end pose; obtaining a current task target pose sequence and an environment constraint condition in real time, and outputting a candidate track set meeting dynamic constraint through a model; and adopting a multi-objective optimization algorithm to screen candidate tracks, generating an optimal track instruction and issuing the optimal track instruction to an execution mechanism. The method adapts to the complex characteristics and variable working conditions of the heavy-load robot, and the track adaptability is improved.

The invention discloses a visual language navigation method for cross-modal alignment in a dynamic shielding environment, and the method comprises the steps: collecting multi-modal data through a visual sensor, an inertial measurement unit, a laser radar and the like, and carrying out the preprocessing and time synchronization; sensing the dynamic shielding object through a model composed of a convolutional neural network and a long-short-term memory network, and estimating the future change of the dynamic shielding object in combination with a space-time sequence prediction algorithm; a double-branch convolutional neural network and a Transform based on a dynamic attention mechanism are adopted to respectively extract visual and semantic features and fuse the visual and semantic features; on the basis of occlusion prediction, potential occlusion region features are extracted in advance from a time dimension, an occluded image is repaired by using a generative adversarial network and geometric constraints in a space dimension, and cross-modal feature alignment is optimized through an attention mechanism; planning a path by using a hybrid reinforcement learning algorithm based on a deep Q network-space and a fast exploration random tree, and dynamically adjusting according to real-time shielding; according to the method, the accuracy, adaptability and reliability of visual language navigation in a dynamic shielding environment are improved.

The invention relates to the technical field of coal mine safety monitoring, in particular to an underground coal mine dust concentration monitoring method and system based on multi-modal data fusion, and the method comprises the steps: synchronously collecting dust concentration time sequence data, dust image data, sound wave signal data and environmental parameters through a multi-modal sensor array deployed in an underground coal mine; carrying out preprocessing and feature extraction on the collected data; predicting the decomposed high-frequency and low-frequency component signals by adopting a long short-term memory neural network and a grey Markov model; when the environment humidity is greater than 80%, carrying out light scattering compensation on the predicted value; inputting various predicted values into an improved D-S evidence theory fusion device, and outputting a fusion dust concentration monitoring value; and when the threshold value is exceeded or the temperature and humidity composite condition is reached, an acousto-optic alarm is triggered and a spraying dust-settling device is started. According to the method, the problems of low precision of a single sensor, multi-source data conflict, high-humidity environment measurement deviation and insufficient time sequence and fusion precision in underground coal mine dust concentration monitoring can be solved.

The invention discloses an energy storage system real-time diagnosis and networking control method and system based on digital twinning and deep learning, and the method comprises the steps: collecting the electrical, thermal and aging state data of an energy storage battery cluster through a multi-mode sensor, constructing a multi-physics field coupled digital twinborn model by using a graph neural network and a long short-term memory network; performing synchronous mapping on battery cluster operation data acquired in real time and the digital twinborn model to generate a state evolution sequence in the battery cluster with advanced prediction capability; based on the state evolution sequence, predicting a dynamic stability boundary of the key node of the power grid and a possible instability risk time period in the future; and according to the dynamic stability boundary and the instability risk time period, generating a cooperative adjustment instruction of the output voltage amplitude, the phase and the virtual impedance of the network construction type energy storage equipment. According to the embodiment of the invention, the diagnosis reliability, the control foresight and the operation safety of the energy storage system in a complex power grid environment can be improved.

The invention discloses an equipment temperature adjusting method fusing a long short-term memory network, and particularly relates to the technical field of temperature control, which comprises the following steps: determining the deployment position of a sensor through thermal simulation, collecting multi-source heterogeneous data, dynamically adjusting the sampling frequency in combination with the temperature and the load current change rate, and adjusting the temperature of the sensor; after data preprocessing, an attention mechanism enhanced LSTM prediction model is constructed, a time sequence sample data set is divided according to equipment thermal response characteristics, training is carried out, and an optimal model is obtained through early stop mechanism optimization; predictive feedback double-closed-loop regulation and control is achieved based on the optimal model, outer loop PI control is combined with an integral separation mechanism to generate a basic control quantity, an inner loop outputs a correction quantity through fuzzification, reasoning and defuzzification, an actuator is driven after superposition, and safety linkage is triggered synchronously; constructing an incremental data buffer pool to screen effective samples, and adaptively updating model parameters by adopting a layered fine tuning strategy; the temperature regulation and control precision and the long-term self-adaptive capability are obviously improved, and the over-temperature risk of equipment is reduced.

The invention discloses a water quality time sequence prediction method of an SSA-VMD-LSTM-XGBoost hybrid model, and belongs to the technical field of water quality monitoring and prediction. Comprising the following steps: (1) data preparation and preprocessing; (2) optimizing the water quality time sequence decomposition of the VMD based on SSA: optimizing a penalty factor and a modal number of the VMD by adopting a sparrow search algorithm (SSA), and decomposing the water quality time sequence into a plurality of sub-components with high stability and low complexity by utilizing the optimized VMD; (3) construction and training of an LSTM-XGBoost hybrid prediction model: constructing a hybrid prediction model fusing long-short term memory (LSTM) and extreme gradient boost (XGBoost), inputting a high-frequency component into the LSTM model, inputting a low-frequency component into the XGBoost model, and finally performing superposition and integration on prediction results of the models; and (4) multi-component prediction result integration and performance verification. According to the method, adaptive optimization of VMD parameters is realized through SSA, the feature extraction and time sequence modeling capability is improved by combining the advantages of LSTM and XGBoost, and the prediction precision and stability of the water quality time sequence are effectively improved.

The invention provides an ocean wind field prediction method based on a neural network, and belongs to the technical field of ocean wind field prediction.The method comprises the steps that sparse ocean observation data are collected, a spatial covariance matrix is established, the spatial covariance matrix is converted into a graph structure, and then multi-hop neighborhood feature aggregation is conducted through a graph convolutional network; a tensor decomposition algorithm is combined for modeling high-order feature interaction to generate a gridding wind field, a bidirectional long-short-term memory network encoder is used for extracting space-time invariant features, a multi-layer perceptron predictor is used for directly mapping a future multi-step wind field, and a course learning strategy and a Shenchang differential equation boundary layer are matched for correction. The technical problem that sparse ocean observation data are difficult to accurately reconstruct into a high-resolution gridding wind field is solved.

The invention relates to the technical field of safety monitoring, and particularly discloses an embankment danger rapid identification method and system, and the method comprises the steps: collecting multi-modal data in real time through arranging a multi-source sensor network; according to the phase space trajectory, extracting a Lyapunov exponent spectrum, correlating the dimension and the Kolmogorov entropy, and forming a structure response chaos degree index; calculating a hydrogeological coupling coefficient in combination with multi-scale decomposition and mutual information analysis; and fusing the two into a three-dimensional dangerous case feature tensor, inputting the three-dimensional dangerous case feature tensor into a pre-training model based on a deep convolutional neural network and a long-short-term memory network, realizing intelligent discrimination of high, medium and low risk levels, generating an adaptive monitoring instruction for a low-risk working condition, outputting a risk evolution trend map, and supporting closed-loop management and control.

The invention relates to the technical field of Internet of Things anomaly identification, in particular to an Internet of Things anomaly traffic identification method and system based on a deep neural network. The method comprises the following steps: collecting communication data of each piece of IoT equipment in real time from an edge gateway of the Internet of Things; preprocessing the collected communication data, and constructing a multi-dimensional feature vector; based on a convolutional neural network and a bidirectional long-short-term memory network, performing time sequence feature extraction and anomaly discrimination on the multi-dimensional feature vector to output a traffic anomaly probability; and comparing the abnormal probability output by the depth time sequence modeling neural network with a dynamic threshold value, and if the abnormal probability exceeds a preset threshold value, determining that the traffic is abnormal. A gating mechanism is introduced into a bidirectional long-short-term memory layer, a gating coefficient is calculated at a time step level, the influence weight of time step information on final output is dynamically adjusted, feature expression of key time steps is strengthened, noise or irrelevant information is suppressed, and the sensitivity of a model to time sequence data is improved.

The invention relates to the field of semiconductor manufacturing, and discloses a wafer processing quality backtracking evaluation method driven by a cross-modal knowledge graph, comprising the following steps: acquiring and preprocessing time sequence process data and text log data; modeling the time series data by adopting a long short-term memory network to predict the defect probability, extracting entities and relationships from a text by adopting a BERT model, and respectively constructing a process knowledge graph, a defect knowledge graph and an equipment knowledge graph; aligning the cross-modal features by using a generative adversarial network, and fusing the cross-modal features into a unified cross-modal knowledge graph; based on the unified atlas, probability backtracking reasoning of defect causes is carried out, and an active process parameter correction strategy is generated; and outputting a comprehensive quality score through the self-weighted dynamic evaluation model. According to the method, intelligent closed loop from passive diagnosis to active optimization can be realized, the accuracy and efficiency of defect positioning are remarkably improved, and a dynamic and comprehensive quality evaluation means is provided.

The invention relates to the technical field of photovoltaic power prediction, and discloses a photovoltaic power prediction method and system, and the method comprises the steps: obtaining the historical operation data of each photovoltaic station, carrying out the abnormal value elimination, expanding the sample data through a generative adversarial network, constructing a first training sample set, and carrying out the variable dimension reduction, screening principal component factors influencing the photovoltaic power to construct a second training sample set; calculating similar days by using the second training sample set, and screening and sorting; establishing a deep learning framework fusing the long short-term memory network, the maximum temperature prediction model and a parameter optimization algorithm, and based on a preset photovoltaic power prediction precision evaluation index, performing model training by taking the number of days of similar days and the weight as independent variables to establish a power prediction model; based on preset reanalysis data and weather forecast data, photovoltaic power prediction is carried out by using the trained power prediction model, precision evaluation and dynamic optimization of model parameters are carried out, and the photovoltaic power prediction precision and adaptability to different scenes are improved.

The invention relates to the technical field of atmospheric environment monitoring, in particular to an atmospheric pollution source tracking and early warning system based on deep learning, which comprises a multi-source data acquisition layer for acquiring multi-dimensional data of atmospheric pollutant concentration, weather, geography and the like through multi-element equipment such as satellite remote sensing and ground sensors; the data preprocessing layer completes data cleaning by using a statistical method and a machine learning algorithm, and realizes heterogeneous data standardization processing through feature extraction, multi-source integration and space-time alignment, and the deep learning model layer realizes heterogeneous data standardization processing by means of a space-time convolutional neural network, a graph neural network and a long-short term memory network in combination with a space-time attention mechanism. The traceability analysis layer is combined with a back diffusion algorithm and a geographic information system to position a pollution source and generate a thermodynamic diagram. And the early warning decision-making layer sets threshold early warning through transfer learning of the adaptive area, and generates an optimal pollution control scheme based on reinforcement learning.

The invention relates to the technical field of protection device testing, in particular to a verification method of an intelligent comprehensive protection device, which comprises the following steps of: extracting starting time and response time in a tripping state transition sequence of the protection device, calculating execution delay and action segment length, and comparing and identifying an abnormal link based on a state number sequence; the method comprises the following steps: realizing analysis and standard reconstruction of a state logic structure, forming an ordered tripping path, introducing a long short-term memory neural network to carry out time alignment on a state change sequence and a current signal segment, identifying a potential mismatch or abnormal change trend in a state chain through linkage analysis of a state-current abrupt change position, and reconstructing a state path. According to the method, path continuity and dynamic adaptability are enhanced, a random forest is adopted to take device numbers, the number of jump segments and response residual errors as multiple inputs for training modeling, device scores are output through an integrated tree voting mechanism, and recognition sorting of abnormal frequency significant devices is achieved.

The invention provides a production energy efficiency optimization method and system based on industrial big data, and the method comprises the steps: generating an industrial production data set and creating an industrial knowledge graph according to multi-dimensional operation parameters, energy consumption state data and production line constraint information generated by a target factory, mining a causal association relationship among the multi-dimensional operation parameters through ontology reasoning analysis to generate a causal association path; executing a sequential association rule mining operation on the energy consumption state data to obtain association rule mining information of energy consumption fluctuation and operation parameter change, and matching and fusing the association rule mining information and a causal association path to generate a candidate root cause set of energy efficiency abnormality; inputting the candidate root cause set into a bidirectional long short-term memory network, positioning root cause information of energy efficiency abnormity through time dimension relevance modeling and spatial dimension feature reinforcement, and finally generating energy efficiency optimization guidance containing parameter adjustment priority and process optimization suggestions. The accuracy of energy efficiency anomaly root cause positioning and the pertinence of optimization measures are improved.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.

The invention discloses a river water level dynamic monitoring and flood overflow risk prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-source hydrological data, and constructing a time series data set; s2, performing interpolation, denoising and normalization processing on the data to generate a unified time sequence format; s3, constructing a water level prediction model comprising a bidirectional long short-term memory network and an attention mechanism; s4, inputting the preprocessed data into the water level prediction model, and outputting a multi-time-step predicted water level sequence; s5, a dynamic threshold value is set according to the historical extreme value and the real-time hydrological condition, and the flood overflow risk is judged; s6, generating and caching a risk tag, and recording an error; s7, outputting a prediction result and risk information through a communication interface; and S8, periodically updating the input data in a rolling manner, and repeatedly executing the prediction and monitoring process. According to the invention, depth prediction and a dynamic threshold control mechanism are fused, and water level monitoring and flood overflow risk intelligent early warning are realized.

A system and method for cross-domain knowledge transfer in federated compression networks. The system enables efficient lossless data compression across diverse data types by intelligently sharing compression strategies between domains. A cross-domain knowledge transfer system identifies relationships between different data domains, adapts compression parameters accordingly, and optimizes learning processes to maximize knowledge reuse. The architecture may include a knowledge repository for storing domain features and compression patterns, domain mapping components that identify similarities, and transfer learning optimization that enables efficient adaptation with minimal examples. This approach significantly accelerates model training for new domains while improving compression performance. Applications include satellite telemetry systems where efficient compression is critical for transmitting large information sets between distant locations. The system may employ probability prediction driven arithmetic coding paired with long short-term memory networks, enhanced by cross-domain knowledge sharing that adapts successful compression strategies from one domain to another while preserving domain-specific optimization.

The invention relates to the technical field of wind power generation, and discloses a wind power plant wind speed correction method and system based on dynamic space-time modeling, and the method comprises the steps: obtaining a whole power curve, obtaining the whole wind speed of a historical period, and constructing a multi-modal training data set; inputting a convolutional neural network to extract local features, inputting a long-short-term memory network, calculating the correlation of each time step feature, obtaining an attention weight, and finally obtaining global feature representation; setting two multi-layer perceptron branches to carry out wind speed prediction correction to obtain a common weather branch prediction value and an extreme weather branch prediction value; constructing a correction curve of each sector and obtaining a correction curve prediction value; and according to the common weather branch prediction value, the extreme weather branch prediction value and the correction curve prediction value, carrying out weighted fusion to obtain a final wind speed correction value. According to the method, the correction precision and robustness are improved, and the interpretability and applicability of the model are enhanced.

The invention discloses a carbon emission real-time regulation and control method and system based on multi-source data fusion and AI decision, and relates to the field of data processing systems or methods specially suitable for administrative, commercial, financial, management, supervision or prediction purposes, in the method, real-time production data is collected through Internet of Things equipment, and the real-time carbon emission intensity is calculated in combination with a carbon accounting engine. And constructing an AI optimization layer comprising a process knowledge graph, a long-short-term memory network and a reinforcement learning agent, converting process constraints into mathematical boundary conditions, predicting energy demands, carrying out iterative optimization by taking carbon emission intensity minimization as a target, generating a dynamic scheduling strategy, and realizing real-time adjustment of operation parameters of production equipment. The method and the device are used for improving the accuracy of carbon emission monitoring data on production scheduling and reducing the risk of high-carbon-intensity production caused by improper time period selection of enterprises.

The invention relates to the technical field of industrial platform data analysis, in particular to a renewable resource recovery data management system based on the Internet of Things, which comprises the steps of synchronously acquiring weight, spectrum and microwave characteristic data of a tested resource through a data acquisition module, executing moisture weight decoupling operation by utilizing a resource matching module, and performing data analysis; then, an inventory evolution module tracks performance loss of resources in real time by using an evolution model of an integrated long and short-term memory network, generates a resource attenuation weight, and constructs a two-dimensional decision matrix containing scheduling priority and preprocessing strength grade instructions in combination with real-time inventory saturation; and finally, the clearance scheduling module executes dynamic pruning and bidirectional optimization through a resource scheduling model, accurately allocates the loading share and the access sequence of each node, and generates a dynamic instruction set containing a delivery sequence. According to the method, feeding homogenization is realized through industrial data multi-dimensional collaborative analysis.

The invention provides a photovoltaic power station energy storage management system and method, and relates to the technical field of power system energy storage. The photovoltaic power station energy storage management system comprises the following modules: a data acquisition module, a prediction analysis module, an optimization scheduling module, a health management module, a real-time control module, a power grid interaction module and an energy efficiency evaluation module. And the data acquisition module is used for cleaning photovoltaic array output voltage / current data by adopting an improved Kalman filtering algorithm based on an Internet of Things sensing network, integrating environmental parameters such as irradiance and temperature of a meteorological station through a multi-source data fusion technology, and generating a standardized operation data set. According to the method, photovoltaic array output data is cleaned and integrated through an improved Kalman filtering algorithm and a multi-source data fusion technology, a high-precision standardized data set is constructed, a dual prediction architecture of an attention mechanism long-short term memory network and a time convolution network is combined, and the space-time correlation between photovoltaic output and load demand prediction is remarkably improved.

The invention discloses a power supply equipment fault prediction method and device based on deep learning, and relates to the technical field of power system equipment fault prediction and deep learning application. The method comprises the following steps: acquiring a power grid topological structure, an equipment operation state, a historical fault record, a real-time equipment load and environmental condition data; forming a space-time correlation basic diagram according to the power grid topology and the equipment operation state, and calculating the correlation strength by using a diagram neural network; calculating fault time delay and determining a transmission path set by using a long short-term memory network in combination with association strength and historical fault records; fusing multiple data to calculate a cross-regional fault propagation probability, and generating a predicted fault path list; and the fault prediction output of the long-short-term memory network input is updated, and the real-time operation data verification optimization of the power grid is combined, so that accurate cross-regional cascade fault prediction is realized, and safe and stable operation of the power grid is ensured.