565 results about "Shortterm Memory" patented technology

The invention discloses a data center machine room AI energy-saving control method and system, a digital twin model of a machine room operation state is constructed through a holographic perception and heterogeneous data fusion technology, centimeter-level monitoring of an equipment state and environmental parameters is realized, and the system integrates a laser radar array, an acoustic sensor and a gas sensor network. The time-space alignment of multi-modal data is completed by combining edge computing nodes, holographic mapping including thermodynamic characteristics, vibration characteristics and gas leakage risks is formed, historical temperature control strategy characteristics are extracted by adopting a variational auto-encoder based on a dynamic strategy generation mechanism of generative artificial intelligence, and a load trend is predicted by combining a long-short-term memory network. Constructing a self-adaptive strategy pool; the multi-agent reinforcement learning framework enables temperature control, equipment scheduling and power grid response to form game optimization, the strategy robustness in a complex scene is improved, and the system innovatively fuses power grid real-time electricity price and carbon transaction data so as to establish a multi-target decision system.

The invention discloses a fault diagnosis and detection system for production automation equipment. The fault diagnosis and detection system comprises a data sensing layer which is used for carrying out multi-mode signal acquisition and real-time preprocessing; the feature extraction layer is used for constructing a recursive block convolution module, capturing transient impact features in four time steps by using an L1-layer gating convolution unit, associating a 16-time-step cross-block periodic degradation mode with an L2-layer sparse attention mechanism, aggregating multi-sensor spatial-temporal features by using an L3-layer global context node, and performing multi-scale feature extraction; the causal reasoning layer is used for establishing a physical constraint driven causal graph engine and outputting a fault propagation path with probability weight; the state modeling layer is used for constructing a continuous health evolution model by adopting a Shenchang differential equation, embedding a physical constraint loss function, and performing equipment full life cycle health state prediction and residual service life estimation in combination with a three-stage memory fusion mechanism of LSTM short-term memory, differentiable neural dictionary medium-term memory and knowledge graph long-term memory; and the decision support layer is used for generating a personalized maintenance work order.

The invention discloses a rotating machine fault diagnosis method, which comprises the following steps of: acquiring vibration, temperature, acoustic emission and current signals at key parts of a rotating machine, and extracting characteristic parameters such as time domain and frequency domain after preprocessing such as filtering and noise reduction; and inputting the characteristic parameters into machine learning models such as a support vector machine, combining deep learning models such as a convolutional neural network and a long-short-term memory network, performing comparative analysis by using a digital twin model, and fusing diagnosis results to output fault types, positions, severity and maintenance suggestions. The method overcomes single diagnosis limitation, multi-source signal complementation, multi-model collaboration, accurate fault diagnosis and diagnosis reliability improvement, provides a scientific basis for equipment maintenance, and is of great significance for guaranteeing safe operation of rotating machinery, reducing maintenance cost and promoting industrial intelligent development.

The invention discloses a network threat detection method, device and equipment and a storage medium, and relates to the technical field of network security, and the method comprises the steps: executing a preset data collection operation to capture initial multi-dimensional data, and carrying out the preset data processing operation on the initial multi-dimensional data to obtain processed multi-dimensional data; executing a preset entity extraction operation on the processed multi-dimensional data to obtain a target entity, storing the target entity in a preset database, and inputting the target entity into a preset long-short-term memory network model to obtain attack time sequence characteristics; inputting the attack time sequence features into a target graph neural network model to construct a target knowledge graph, and determining a cross-device abnormal behavior chain based on the target knowledge graph; and determining an attack chain integrity coefficient according to the cross-device abnormal behavior chain, and determining a network threat event and a target risk level by using the CVSS vulnerability score, the space-time correction factor and the attack chain integrity coefficient to complete network threat detection. The problems of incomplete single-dimensional data coverage, high false alarm rate and the like can be solved.

The invention discloses a virtualized container environment monitoring device and method based on eBPF, and aims to solve the problems of poor isolation, insufficient security and platform adaptation deficiency in a virtualized container environment. According to the method, time sequence data of monitored events in a virtualized container environment are collected in real time through an eBPF technology, the time sequence data are transmitted to a data analysis module through eBPF mapping and message queues, data are analyzed through a long-short-term memory network, the deviation degree of the current time sequence data and historical data is calculated, abnormal behaviors are judged, and an alarm is given. According to the method, the user-defined program is operated under the condition that kernel codes do not need to be modified, intelligent analysis and abnormal behavior recognition are conducted on the collected data, comprehensive monitoring and dynamic detection of complex threats are achieved, the safety, stability and compatibility of the system are improved, and the method is suitable for virtual environments under cloud computing and big data scenes.

The invention relates to the technical field of energy load prediction distribution optimization, in particular to an energy terminal-oriented dynamic load prediction distribution optimization method and system. The method comprises the following steps: generating a load prediction value by using a long short-term memory network based on acquired terminal load data; constructing a load priority matrix and a load distribution conflict rule set, and constructing a multi-factor integration constraint model in combination with the optimization parameters of the load predicted value; dynamically correcting the model based on the load prediction value; an integer programming problem is solved, and a distribution instruction of each terminal is obtained; and performing execution control based on the distribution instruction. The distributed acquisition unit supports multiple communication modes, acquires and normalizes various types of terminal data, loads a virtual value compensation model for checking abnormal nodes, guarantees data integrity and reliability, improves the processing capability of the system for multi-source heterogeneous data, and enhances compatibility and robustness.

The invention provides a method, a device and a system for switching master and standby control centers of a smart home. In the scheme, real-time operation state data of each control center in the smart home and the number of times of downtime in a first preset time period are acquired, and based on the real-time operation state data and the number of times of downtime, a long-term short-term memory network model is used to score each control center to obtain a final score of each control center; sorting all the final scores from large to small, and determining the control centers corresponding to the final scores ranked in the front preset number as candidate control centers; an improved PBFT protocol is adopted to carry out consensus election on the candidate control hub, a target control hub is elected, the main control hub is switched to the target control hub for operation, and the target control hub meets at least one of the following preset conditions: the highest network communication efficiency, the shortest network delay and the lowest power consumption. The problem that the switching efficiency of the main and standby control centers in the smart home system is low is solved.

The invention provides a Beidou ionosphere random error autocorrelation model establishment and correction method and device, and the method comprises the steps: building a deep learning neural network model for predicting a Beidou ionosphere random error through combining a convolutional neural network, a long-short-term memory neural network and an attention mechanism, and carrying out the correction of the Beidou ionosphere random error based on the predicted Beidou ionosphere random error. And respectively fitting the time autocorrelation function of the Beidou ionosphere random error and the space autocorrelation function of the Beidou ionosphere random error, and establishing a Beidou ionosphere random error autocorrelation model. The Beidou ionosphere random error prediction method can predict the Beidou ionosphere random error under the condition of not knowing an accurate physical model, and provides technical support for high-precision prediction of the Beidou ionosphere model and analysis of the Beidou ionosphere random error.

The invention provides a sensor anomaly detection method and device based on a long short-term memory network, electronic equipment and a medium, which are applied to the technical field of fault detection, and effectively reduce data noise and improve the input quality of a model by performing preprocessing of null value removal and feature screening on acquired sensor data. Inputting the preprocessed sensor data into an improved attention mechanism-long and short-term memory network model, capturing a long-term dependency relationship of a time sequence by utilizing a long and short-term memory network, and dynamically distributing weights and focusing on key information by virtue of an attention mechanism, so that the model prediction precision is remarkably improved; and finally, the prediction result is compared with historical data and real-time data, so that the sensor abnormality is accurately judged, the defects of a traditional method in processing complex time sequence data are effectively overcome, the accuracy and reliability of abnormality detection are remarkably improved, and meanwhile, the method has prediction capability and adaptivity and is suitable for popularization and application. And characteristic differences of different sensors can be adapted.

The invention discloses a power transmission line icing galloping risk early warning system and early warning method, and belongs to the technical field of icing galloping risk early warning, and the system comprises the following modules: a historical data processing module reconstructs historical monitoring data by using a long and short term memory auto-encoder, recognizes abnormal data through reconstruction errors, and sends the abnormal data to an early warning module; removing outliers based on a 3sigma-dynamic threshold algorithm, and then marking a space-time credibility weight; the terrain compensation module constructs a micro-terrain feature vector, calculates the similarity through a Siamese network, and compensates low-confidence data; the real-time data processing module obtains the icing thickness according to the monitoring data of the current time point and the previous time point; the data acquisition and analysis module fuses historical and real-time data and analyzes a galloping state; and the risk early warning module takes the historical data with the weight and the galloping state information as input, outputs a risk value through the prediction model, and triggers early warning. According to the system, through cooperative work of all the modules, the icing galloping risk is accurately warned in real time, and the safety and reliability of power grid operation are improved.

The invention relates to the technical field of organic display data processing, in particular to a touch data processing method based on an organic display, which comprises the following steps of: monitoring a flexible deformation acceleration component and an environmental parameter change rate in real time through a dynamic trigger function, and triggering high-priority baseline calibration when a threshold value exceeds a limit; and generating a self-adaptive compensation amount matched with the current touch signal noise spectrum. And constructing a dielectric constant offset prediction model by using a long short-term memory neural network or a Kalman filter, deducing the dynamic change trend of the dielectric property of the material, and correcting a touch signal compensation coefficient in a grading manner according to the predicted offset. And a closed-loop error correction mechanism is formed based on the spatial distribution entropy reverse optimization model weight parameter and the trigger threshold boundary of the contact coordinate residual matrix. According to the method, the problem of time domain mismatch of fixed period calibration and organic material nonlinear offset is solved, the accumulation of touch coordinate analysis errors in a high-curvature deformation scene is effectively inhibited, and the touch positioning precision and the interaction response real-time performance are improved.

The invention discloses a computing power scheduling management method and system based on an algorithm. The method comprises the steps that an initial resource consumption data set is obtained, and the data set comprises the processor occupancy rate and the memory usage amount of a plurality of calculation tasks in different calculation stages; according to the initial resource consumption data set, a fixed time window is adopted to segment historical data, and a smooth resource consumption sequence is generated through moving average calculation; for the smooth resource consumption sequence, applying a trend decomposition method to separate long-term trend and periodic fluctuation features, and constructing a resource dynamic feature matrix; and according to the resource dynamic characteristic matrix, constructing a multi-layer long-short-term memory network model, and optimizing parameters through a gradient descent method to generate a resource demand prediction curve and the like. According to the method disclosed by the invention, the resource consumption data of the computing task is deeply analyzed and processed, so that efficient computing power resource allocation and scheduling are realized to cope with the change of the demand of the computing task on the processor and memory resources in different stages.

The invention provides a PCIe equipment fault monitoring and dynamic processing method based on intelligent prediction, and the method comprises the steps: obtaining operation parameters from a BMC (baseboard management controller), an operation system and a PCIe equipment register, carrying out the processing and time sequence feature extraction, and inputting a lightweight long short-term memory network LSTM model deployed in the BMC for fault probability prediction. And combining the prediction result and the operation parameters to calculate an equipment health score, and triggering an alarm, bandwidth degradation or hardware isolation operation according to the score. And the hardware isolation module sends a control instruction to the complex programmable logic device CPLD through the IC bus to realize non-interruption power-off isolation of the target equipment. And uploading real-time data to a cloud server through encryption communication, executing fine adjustment of the model, and returning an update weight to realize continuous optimization and version control of the model. The method can be widely applied to computing equipment such as a server, and the availability and the fault-tolerant capability of the system are improved.

The invention provides a user dialogue generation method and system based on memory fusion, and relates to the technical field of artificial intelligence, and the method comprises the steps: building a user memory knowledge base based on a preset knowledge graph tool; based on the user memory knowledge base, reading current text data input by the current dialogue user and historical text data of the current dialogue user; obtaining short-term memory data, long-term memory data and scene memory data based on the user memory knowledge base, a preset large language model, a preset cue word engineering algorithm, the current text data and the historical text data; and generating a user dialogue based on the preset large language model, the preset cue word engineering algorithm, the short-term memory data, the long-term memory data and the scene memory data. According to the invention, the memory range of the user from recent interaction to long-term important information is fully covered, and the reliability of responding to the user is improved.

The invention discloses an intelligent disk monitoring system based on multi-modal data fusion and a fault early warning method, and relates to the technical field of power plant management, and the method comprises the steps: collecting the real-time operation data of equipment; performing dimension reduction processing on the operation data by using a principal component analysis algorithm; inputting the data subjected to dimension reduction into a long short-term memory network model, modeling the time sequence dependence of the data by using the long short-term memory network model, and predicting the future operation state of the equipment; based on the prediction result of the operation state, performing feature extraction on the data through a convolutional neural network; according to an output result of the convolutional neural network, alarm signals of different levels are generated according to set threshold levels; and according to the grade of the alarm signal, adjusting operation parameters of the equipment through a feedback control mechanism. According to the intelligent disk monitoring system based on multi-modal data fusion, the accuracy, the real-time performance and the intelligent level of equipment monitoring are improved, and the fault early warning precision and the system response speed are remarkably improved.

The invention discloses a truss structure wind-induced dynamic response prediction method and system based on physical enhancement. The method comprises the following steps: carrying out feature extraction and alignment fusion on input data containing condition parameters and wind speed time sequence data by utilizing a long short-term memory network and a physical enhancement attention mechanism; extracting multi-scale features from the fusion features through expansion convolution, and performing weighted aggregation on the multi-scale features; the physical priori knowledge of structural vibration is fused into position coding and a self-attention mechanism so as to carry out response prediction; and integrating physical model information of the truss structure and a dynamic control equation into a loss function, and calculating physical information residual loss so as to improve the physical interpretability of a prediction result. According to the method, data heterogeneity can be eliminated, complementary information can be fused, the multi-scale characteristic of wind-induced response is coped with, the accuracy and efficiency of wind-induced dynamic response prediction of the truss structure are effectively improved, and the physical interpretability and generalization ability are enhanced.

The invention discloses a pre-inquiry method based on a long and short term memory management large model. The pre-inquiry method comprises the following steps: S100, acquiring identity information and complaint symptom information of a patient; s200, establishing long-term memory and short-term memory corresponding to the patient; all information generated in the pre-inquiry dialogue process is stored in the short-term memory; the long-term memory and the short-term memory are updated and maintained based on a memory storage mechanism; s300, based on the long-term memory and the short-term memory, using a large model to construct multiple rounds of pre-inquiry dialogues, and obtaining patient condition information; and S400, summarizing the patient identity information, the complaint symptom information, the long-term memory and the short-term memory to generate a pre-inquiry report. According to the pre-inquiry method based on the long-short-term memory management large model, the long-short-term memory management is separated from the large model to carry out multiple rounds of pre-inquiry dialogues, the method does not need to depend on the memory ability of the large model, and the memory loss phenomenon in multiple rounds of dialogues commonly seen in an existing large model can be effectively avoided.

The invention discloses a multi-mode bearing residual life prediction method and system based on balance optimization. The method comprises the following steps: firstly, in an offline stage, acquiring time sequence data of a vibration signal from a bearing operation data set, and generating a time-frequency image through wavelet transform to ensure that the data comprises two modes of a time sequence and an image; secondly, extracting time sequence features of the time sequence data by using a long short-term memory (LSTM) network, and extracting spatial features of a time-frequency image by using a graph convolutional network (GCN); then, features extracted by the LSTM and the GCN are spliced to generate a joint feature vector, and the joint feature vector is input into a full connection layer to output an RUL predicted value; in the training process, the loss contribution difference of the LSTM mode and the GCN mode is monitored in real time through an adaptive optimization strategy, the gradient update amplitude of each mode is adjusted through back propagation, the optimization of the dominant mode inhibiting the vulnerable mode is avoided, and the full extraction of the multi-mode features is ensured.

The embodiment of the invention provides a question and answer management method and device based on a large model, a storage medium and a program product. In the scheme, a'short-term first and long-term 'progressive recall strategy is introduced, that is, in a storage stage, session abstracts are generated in a segmented manner under the condition that whether the collection frequency of a preset round and the topic category are changed or not, the session abstracts are sequentially written into a short-term memory storage area and sink to a long-term memory storage area after the preset storage duration is reached, and a subsequent recall path is pre-buried; in the acquisition stage, the latest abstract closest to the current round is recalled from the short-term region, and if the abstract is missing, the cross-session or cross-round historical session abstract continues to be complemented from the long-term region. According to the mechanism, session context management of'instant light and thin 'and'long-term consistent' is considered under the condition that the computing power and the bandwidth cost are not remarkably increased, accurate contexts are provided for complex session scenes with long-period, multi-topic and multi-file cooperation, and a large question and answer model can make more accurate and more consistent questions and answers based on complete and related context information.

The invention provides a time-space attention-driven soil humidity prediction method in a domestic supercomputing environment, and the method comprises the steps: constructing a time-space feature dynamic fusion soil humidity prediction network model based on attention guidance; the attention-guided spatio-temporal feature dynamic fusion soil humidity prediction network model comprises a convolutional long-short term memory network basic framework and a spatio-temporal attention mechanism module. The introduction of the ConvLSTM network effectively integrates the time sequence and space information of the soil humidity, the potential space-time dependence in the data is fully utilized, the space-time attention mechanism enables the model to adaptively pay attention to important time periods and space regions by dynamically adjusting the weight between time steps, and the accuracy of the model is improved. The limitation of fixed feature selection in a traditional method is avoided, so that the prediction precision is improved. The model can better capture space-time dynamic feature information, further identifies the relationship between the influence factor and the soil humidity, and especially shows unique advantages in complex space-time feature processing and modeling of long-time sequence data.

The invention relates to the technical field of crane equipment health monitoring, in particular to a crane running state health monitoring system and method. A sensing data acquisition unit is used for acquiring a winding drum vibration harmonic component, a steel wire rope leakage magnetic field gradient and a pulley block real-time load; the data analysis unit performs rotating speed synchronous variational mode decomposition on the vibration component to extract resonance characteristics, performs temperature and stress double-compensation correction on a leakage magnetic field gradient, quantifies an energy entropy attenuation rate through wavelet packet decomposition, constructs a phase difference model of a load and vibration to output a slip risk phase offset, calculates a load spectrum damage cumulant, and calculates a load spectrum damage cumulant; and after the four heterogeneous features are fused, a residual life coefficient is output through a dual-channel convolution-long and short-term memory hybrid neural network, and an execution unit triggers crane speed reduction control when the residual life coefficient is lower than a threshold value, so that the problems of insufficient multi-source data fusion and lack of dynamic compensation in the traditional technology are solved, and the fault early warning accuracy is improved.

The invention discloses a complex metering sensor abnormal feature analysis method based on long-term accumulated data, and relates to the technical field of state monitoring and fault prediction. Static statistical characteristics and dynamic frequency characteristics of data and transient changes in non-stationary signals can be comprehensively captured, meanwhile, a model in which a double-layer long-short-term memory network is combined with an attention mechanism is constructed, short-term time sequence dependence in a first-layer LSTM learning data fragment and long-term evolution trend between second-layer LSTM learning fragments are constructed, and the time sequence dependence in a second-layer LSTM learning data fragment is constructed. The attention mechanism focuses on the key period, and the deep fusion from feature extraction to model construction enables the model to accurately identify the difference between normal data and abnormal data, thereby realizing the accurate detection of the sensor abnormality, and in the practical application, the normal fluctuation and real abnormality of the sensor can be effectively distinguished, and the accuracy of the sensor abnormality detection is improved. And reliable guarantee is provided for stable operation of the system.

The invention discloses a water conservancy and hydropower construction resource management scheduling method and system based on artificial intelligence. The method comprises the following steps: collecting construction multi-source data in real time; based on an ST-GNN space-time diagram neural network, predicting the resource demand of each construction area in a future T time period, and combining with an LSTM long short-term memory network to perform joint modeling on the construction progress and environmental disturbance to obtain a demand prediction model; adjusting an inertia weight and a cognitive factor of the demand prediction model by using an IPSO improved particle swarm optimization algorithm, and learning an optimal scheduling strategy in a simulation environment in combination with an RL reinforcement learning training agent to obtain a target demand prediction model; and inputting the construction multi-source data into the target demand prediction model to output a scheduling scheme, and performing digital twin simulation verification and adjustment on the scheduling scheme to obtain a target scheduling scheme. And the prediction accuracy of future resource demands and the management scheduling efficiency are improved.

The invention relates to a man-machine interaction method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: acquiring interaction content input by a user; scene recognition is carried out on the interaction content through a large language model, and a scene label of the interaction is determined; determining a target long-term memory partition corresponding to the scene tag from a plurality of long-term memory partitions preset for the user; inputting target long-term memory content in the target long-term memory subarea and short-term memory content stored in a short-term memory area preset for the user into an intelligent agent; the intelligent agent is used for generating reply content for the interaction content according to the target long-term memory content and the short-term memory content. By adopting the method, effective association and collaborative calling of different scene memories can be realized, and the dynamic requirement of a user on coherent interaction in multi-scene switching is met.

The invention discloses a cross-regional computing power resource collaborative allocation method based on computing power centers, and relates to the technical field of computing power resource scheduling and optimizing.The cross-regional computing power resource collaborative allocation method comprises the steps that real-time load data, historical task execution data and network delay data of all computing power centers are collected, and a regional load feature database is constructed; predicting the load demand of each computing power center in a future time window by using a long short-term memory network model to generate a load prediction value; calculating a resource gap coefficient and a resource margin coefficient of each region according to the load prediction value and the current resource capacity, identifying a resource insufficient region and a resource surplus region, and generating a resource collaborative matching matrix between the regions; and optimizing and solving the cross-regional task allocation scheme by adopting an improved genetic algorithm to generate an optimal resource allocation strategy, and allocating the to-be-processed task to a corresponding computing power center according to the optimal resource allocation strategy. According to the method, the cooperative utilization rate and the distribution efficiency of the computing power resources in the cross-regional scene are effectively improved.

The invention relates to an intelligent water affair remote monitoring and control system based on the Internet of Things, in particular to the field of intelligent water affair remote monitoring and control, and the system comprises the steps: firstly, pre-training a long and short-term memory network model through a cloud, and generating a lightweight edge prediction model to reduce calculation and transmission loads; the data transmission module effectively reduces the occupation of transmission bandwidth based on a dynamic quantization threshold value of a residual error and a differential coding technology, meanwhile, the high precision of the data is kept, the verification feedback module monitors the accuracy of the transmission data in real time through confidence coefficient detection, and triggers model re-calibration when the confidence coefficient is reduced, so that the long-term stability of the system is ensured, and the reliability of the system is improved. The sampling optimization module dynamically adjusts the sampling frequency according to the confidence coefficient and the residual fluctuation characteristics, balance is achieved between bandwidth occupation and reconstruction precision by using the Lyapunov optimization theory, the real-time performance and accuracy of water affair monitoring are improved, and meanwhile bandwidth consumption and energy consumption are effectively reduced.

The invention provides a vehicle formation control method fusing graph nerve and reinforcement learning, and the method comprises the steps: constructing a graph structure model between vehicles, extracting the structural interaction characteristics between a current vehicle and a neighbor vehicle through a graph attention network, and combining with the dynamic evolution characteristics of the modeling historical time steps of a long-short-term memory network, and predicting a neighbor aggregation state at a future moment. And processing the two-dimensional reflection image acquired by the vehicle sensor by using a convolutional neural network, and extracting the spatial semantic information of the surrounding environment. A vehicle state, neighbor features and environment features are fused as strategy input, joint training is performed based on a multi-agent depth deterministic strategy gradient algorithm, and end-to-end control strategy optimization is realized by adopting a centralized value network and distributed strategy network structure. According to the invention, the intelligent decision-making level of the formation system is improved, and the system has good environment adaptability and cooperative control performance.

The invention discloses a heat pump system state anomaly detection method based on a depth auto-encoder, and the method comprises the steps: collecting compressor data, and carrying out the standardization processing to construct a multi-dimensional time sequence; a spatial-temporal feature extraction depth auto-encoder with a thermodynamic coupling attention mechanism is constructed, coupling attention is utilized to calculate physical parameter coupling strength weights to extract spatial features, time features are extracted in combination with a long and short-term memory network, and normal state data are reconstructed and predicted through a decoder after fusion; residual vectors of predicted normal state data and original data are calculated, and a weighted mahalanobis distance is calculated by using a covariance matrix to generate an abnormal score; and constructing a sliding probability distribution model based on historical normal data, calculating a current score occurrence probability, and comparing the current score occurrence probability with a preset threshold to output an anomaly detection result. According to the method, a multi-physical parameter space coupling relationship and a time evolution rule are captured through a thermodynamic coupling attention mechanism, and the anomaly detection accuracy and robustness are improved.

The invention relates to the technical field of power distribution cabinet equipment monitoring, discloses a power distribution cabinet standby regulation and control system based on machine vision, and aims to improve the stability and operation efficiency of a power system. The system collects equipment images in the power distribution cabinet in real time through a high-definition camera, performs equipment identification and state monitoring by using a convolutional neural network (CNN) algorithm, and predicts the future state trend of the equipment in combination with a long short-term memory (LSTM) algorithm. And the regulation and control decision module intelligently generates a regulation and control instruction according to the current and future states of the equipment by adopting a deep reinforcement learning algorithm. And the execution control module is responsible for receiving the instruction and controlling the power distribution cabinet equipment. According to the invention, intelligent monitoring and accurate regulation and control of power distribution cabinet equipment are realized, the automation level and stable operation capability of a power system are effectively improved, the manual inspection cost is reduced, the timeliness and accuracy of fault handling are improved, and powerful support is provided for intelligent management of the power system.

The invention discloses a heterogeneous task low orbit satellite task unloading method based on a space-time diagram attention network. The method comprises the steps of obtaining space-time diagram data related to a to-be-unloaded task; the time-space diagram data are input into a time-space conversion model, a prediction result of a subsequent network state is obtained, the time-space conversion model integrates a graph convolutional network module, a long-short-term memory network module and a multi-head attention module, the graph convolutional network module extracts spatial features of satellite network topology under each time step, and the multi-head attention module extracts the spatial features of the satellite network topology under each time step; the long-short-term memory network module captures satellite network states and time sequence dependence of task loads, and the multi-head self-attention module learns association weights among different spatial-temporal characteristics and outputs prediction results of future satellite network states; and based on the prediction result and the attribute of the to-be-unloaded task, setting an optimization objective function of an opportunity task unloading algorithm problem to determine a decision result of task unloading. According to the method, the task unloading efficiency and the resource utilization rate in the low-orbit satellite network can be remarkably improved.