2206 results about "Network output" patented technology

The invention discloses a primary and secondary fusion complete ring main unit fault diagnosis method, and particularly relates to the technical field of power distribution fault diagnosis, and the method comprises the steps: collecting multi-path original data, carrying out the frequency domain and time domain combined calibration, carrying out the comprehensive evaluation according to two preset discrimination factors, namely, a data stability deviation amplitude and a multi-path waveform time deviation degree, and obtaining a fault diagnosis result. Two types of feature data sets are constructed subsequently, input data of two interference modeling networks are calculated respectively, then the interference modeling networks are input to output interference grade values, sampling precision dynamic adjustment and fault recognition strategy switching operation are executed according to the interference influence grade values, and diagnosis accuracy and stability in a complex interference environment are improved. According to the method, unified normalization processing of multi-path sensing data is realized, and the sensing accuracy of fault features is improved; through double-factor triggering and feature fusion evaluation, the stability of interference identification is enhanced; and sampling adjustment and strategy switching are executed based on the interference level value, so that the robustness and reliability of diagnosis are improved.

The invention discloses a high-voltage equipment monitoring system and method based on thermal imaging and dynamic sensing fusion of a power plant, and belongs to the field of high-voltage equipment monitoring. The edge intelligent processing module is used for outputting a real temperature field matrix, separating equipment vibration characteristics from environment noise and generating a fused characteristic vector; the multi-modal analysis module is used for inputting the feature vectors into a constructed equipment topological graph neural network and outputting a health index and an abnormal probability distribution diagram of each monitoring point; the environment coupling fault analysis module is used for generating a fault diagnosis report; and the dynamic early warning module is used for executing graded early warning according to the health index and the fault diagnosis report. The fault detection rate and the operation and maintenance efficiency can be improved.

The invention discloses a power transmission line inspection unmanned aerial vehicle obstacle intelligent detection and obstacle avoidance method. The method comprises the steps that multi-source sensing data is acquired, and alignment is completed through calibration and timestamp matching; heterogeneous data preprocessing and feature enhancement; constructing a high-precision environment fusing a geometric structure and a semantic tag, mapping a two-dimensional target detection result output by the recognition network to a three-dimensional coordinate system through spatial transformation, and fusing the two-dimensional target detection result with a point cloud structure to construct a semantic occupation grid map; performing preliminary route planning according to a preset power grid topological structure and task coverage requirements, and generating a barrier-free flight path covering the whole inspection area; reinforcing learning of a dynamic obstacle avoidance strategy; track dynamic reconstruction and energy consumption optimization scheduling are carried out; the technical problems that an existing technical system has defects in the aspects of obstacle recognition accuracy, complex environment adaptability, data fusion capacity and obstacle avoidance strategy intelligence, and the requirements for high-reliability, low-energy-consumption and high-efficiency unmanned aerial vehicle power transmission line inspection are difficult to meet are solved.

The invention relates to the technical field of power system dispatching, and discloses a multi-agent reinforcement learning active power distribution network area coordination method and system, and the method comprises the steps: dividing a power distribution network into a plurality of areas, and each area is managed by an agent; collecting observation information; inputting the observation information into an upper reinforcement learning strategy network, and outputting control parameters; inputting the control parameters into a target function of the lower-layer local physical optimization model, and solving an output setting point of the equipment under the condition of meeting the safety operation constraint; constructing a De-POMDP problem, and obtaining a reward signal of each agent; a sequential updating mechanism is introduced, global network parameters are optimized, and corresponding decisions are obtained; and inputting the multi-agent decision into the global active power distribution network model to obtain the total operation cost, feeding back the total operation cost as an award to the reinforcement learning strategy network, updating global network parameters, and converging to obtain an optimal decision. According to the invention, regional wind-solar-storage multi-energy scheduling can be effectively optimized, and energy balance in the region is realized.

The invention discloses a lithium ion battery health state evaluation method and device based on deconstruction physical information, a medium and a product, and relates to the technical field of lithium ion battery health state evaluation, and the method comprises the steps: extracting a multi-dimensional health factor, inputting the multi-dimensional health factor into a deep physical information neural network fusing a self-attention mechanism module and a Koopman neural operator module, and obtaining a deep physical information neural network; and outputting the SOH estimation value. By extracting the universal health factors suitable for multiple working conditions, the problem that the universality of the health factors is insufficient is solved; a self-attention mechanism is utilized to enhance features and reduce redundancy, and the perception ability of the model to key information is enhanced; physical information is deconstructed by means of a Koopman neural operator, a physical mechanism of battery degradation is fused into the model, and the physical interpretability of the model is enhanced; the deep physical information neural network is fused with multi-dimensional information for estimation, individual differences and complex working conditions of different batteries are effectively dealt with, and therefore high-precision and high-robustness lithium ion battery SOH estimation is achieved.

The invention discloses a source load storage dynamic strategy verification method based on double-layer reinforcement learning, and the method comprises the following steps: S1, collecting the operation data of a source load storage system, and constructing a standardized operation data set; s2, constructing a double-layer reinforcement learning model, generating a global scheduling strategy by an upper-layer strategy network, and outputting an action decision strategy by a lower-layer strategy network; s3, performing joint training on the double-layer reinforcement learning model by adopting a strategy gradient optimization method, and outputting a scheduling strategy; s4, introducing an integral gradient method to analyze a scheduling strategy, and constructing a key scheduling state node set; s5, optimizing the generation logic of the global scheduling strategy to obtain an optimized double-layer reinforcement learning model; s6, constructing a plurality of source-load-storage system operation scenes to form a typical operation scene set; and S7, deploying the optimized double-layer reinforcement learning model in the typical operation scene set, and outputting a strategy verification result. According to the invention, through combination of double-layer reinforcement learning and an integral gradient method, source-load-storage dynamic strategy verification is realized.

The invention discloses a 3D object reconstruction method combining multi-view fusion and a neural network, and the method comprises the following steps: collecting a multi-view image of a target object, carrying out the geometric calibration and view parameter calibration, and generating a standardized image sequence; inputting the image sequence into a feature extraction and voxel fusion module to obtain a preliminary three-dimensional space representation body as a coarse reconstruction model; performing uncertainty evaluation on the coarse reconstruction model, generating a voxel-level confidence coefficient heat map, and dividing the voxel-level confidence coefficient heat map into a plurality of confidence coefficient intervals; based on the confidence interval, constructing an adaptive repair network with a multi-scale residual path, and outputting and activating different repair paths as required by using a path gating mechanism; and fusing the residual output of each repair path with the coarse reconstruction model to generate an optimized final three-dimensional reconstruction model. According to the method, the risk of excessive repair or error repair can be effectively reduced, the adaptability of the model to complex areas such as sheltered areas is enhanced, and the integrity and precision of the whole three-dimensional reconstruction model are improved.

The invention provides a scene adaptive projection vehicle lamp system based on deep reinforcement learning and a control method, and relates to the technical field of intelligent vehicle lamps and automatic driving perception systems. Comprising a multi-modal sensing module, a feature fusion module, a strategy generation module and an execution module. The multi-mode sensing module is used for collecting environment state data and performing primary processing to form an environment data information flow; the feature fusion module is used for generating a unified environment feature vector for the environment data information flow; the strategy generation module is used for receiving the environment feature vector and generating a vehicle lamp adjustment strategy through multi-layer neural network calculation; evaluating a result obtained by executing the vehicle lamp adjustment strategy based on the vehicle lamp, and optimizing strategy parameters of the strategy network based on a PPO algorithm; the execution module is used for controlling the vehicle lamp according to the vehicle lamp adjustment strategy output by the strategy network. The intelligent level of the vehicle lamp is remarkably improved, and the system is widely applied to night driving assistance, urban interaction prompt and low-visibility driving scenes.

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

The invention relates to a large-scale road network traffic control method based on a deep reinforcement learning large model, and belongs to the technical field of intelligent traffic control. The method comprises the following steps: sensing real-time multi-modal road network information including urban road intersections, highway entrance ramps and emergency lanes, and generating a space-time fusion representation vector representing a current traffic network state by fusing a space diagram construction method and a time sequence embedding method; the space-time fusion representation vector and historical state memory are spliced to serve as input, a backbone network of a pre-training large language model is used for state feature distillation so as to enhance state representation, and a traffic control decision is output through a strategy network with a layered action space; through cross-modal knowledge migration and a progressive course learning strategy, a training process of a deep reinforcement learning algorithm is guided and optimized so as to improve model training efficiency and generalization ability. According to the method, the generalization performance and the accuracy of the control strategy are improved while the real-time response speed is ensured.

The invention relates to the technical field of power transmission line detection, and discloses a power transmission line key component defect identification method based on cloud edge cooperation. The method comprises the following steps: acquiring a multi-source inspection data set consisting of a power transmission line component image acquired by an unmanned aerial vehicle, edge end sensor data and a cloud historical defect database; at the edge end, extracting component region features through an image preprocessing algorithm, and generating environment correlation parameters by using a multi-modal feature fusion algorithm; and at the cloud, performing space-time correlation analysis on the historical defect database to generate a part defect evolution graph. And inputting the information into a cloud edge collaborative recognition model to obtain a component defect feature vector, constructing a multi-stage defect recognition network through a dynamic optimization algorithm, and outputting a component defect classification result and confidence. According to the method and the system, the accuracy, the real-time performance and the reliability of defect identification of the key component of the power transmission line are improved, and the method and the system have good application prospects.

The invention relates to a quick river flood forecasting method based on a physical information neural network, and belongs to the field of river flood forecasting. The method comprises the following steps: on the basis of a traditional physical information neural network (PINN), introducing a boundary condition parameter as an input variable, and enabling the PINN to learn a flood wave propagation rule under different boundary conditions. Furthermore, on this basis, a physical information neural network flood fast forecasting framework (RFF-PINN) integrated with a hydrodynamic method is provided, a numerical solution based on grid discretization is reconstructed into a continuous function in a time-space domain through a piecewise polynomial interpolation method, residual error loss between network output and a hydrodynamic model simulation value is constructed, and therefore, a flood fast forecasting result is obtained. The network parameters are optimized in cooperation with the PDE loss, and the problem that the network parameter optimization effect is reduced due to the fact that the PDE loss of the complex flow state area is difficult to converge is solved. The method has the beneficial effect that the water depth change process of each section of the river channel under any boundary condition can be accurately and quickly predicted.

The invention relates to a ship detection method for a complex synthetic aperture radar (SAR) scene, and belongs to the technical field of remote sensing image target detection.The method comprises the steps that an obtained high-resolution synthetic aperture radar image is input into a DPM-YOLO ship detection network, a ship target detection effect picture is output, the DPM-YOLO ship detection network is improved based on a YOLOv11 network, and the ship target detection effect picture is obtained; a DPSConv module is introduced into a backbone network to capture multi-scale context information by using a receptive field while keeping fine feature details, and a PromptFusionMod module is introduced into a neck network to perform multi-modal feature fusion through four processing stages of space compression and prompt fusion, an efficient attention mechanism, a lightweight multi-layer perceptron and output fine processing. And the original detection head is replaced by the MscaleASFHead detection head. Compared with the prior art, the method has the advantages that fine-grained feature extraction, cross-scale semantic alignment and lightweight deployment can be considered, and ship detection precision and robustness in a complex sea condition scene are improved.

The invention belongs to the technical field of computer vision and deep learning, and particularly relates to a PCB defect detection method based on combination of a visual converter and conditional diffusion. Comprising the following steps: constructing an unlabeled PCB image data set and carrying out data preprocessing and enhancement to obtain a preprocessed image; executing a self-supervised pre-training task on the preprocessed image to obtain a feature extraction network; based on a conditional diffusion model, generating a synthetic defect PCB image and a label thereof by using the features output by the feature extraction network and the defect type control vector; mixing the synthetic defect image with a small number of real defect images to construct a training set; performing training adjustment on the defect detection model by adopting the training set to obtain a trained defect detection model; performing PCB defect detection by using the trained defect detection model; according to the method, the robustness and the cross-domain generalization ability are remarkably improved, the missed detection risk is reduced, and the rapid and stable quality control requirement of the production line is met.

The invention discloses a finite time tracking adaptive control method for a mechanical arm based on a neural network, and relates to the technical field of industrial robot control. The method comprises the following steps: constructing a kinetic model of the mechanical arm, obtaining an existence form of an unknown nonlinear term in the model, and defining a joint position tracking error and an error change rate of the mechanical arm; constructing a sliding mode dynamic equation based on the tracking error and the error change rate; a BP neural network is adopted to approach the unknown nonlinear dynamic state of the mechanical arm, and the mapping relation between a network input vector and an output vector is determined; combining a sliding mode dynamic equation with BP neural network output, and designing a finite time control method including adaptive gain; and a self-adaptive updating method of BP network weight and sliding mode gain is deduced, so that the tracking error of the mechanical arm is converged to a zero neighborhood within preset time, and self-adaptive control of the mechanical arm is completed. According to the method, high-precision trajectory tracking within the preset time can be realized, and the anti-interference capability is high.

The invention relates to the technical field of traffic prediction, in particular to a multi-layer space-time structure correlation perception traffic flow prediction model, which comprises a space-time structure decomposition layer for decomposing original traffic flow data into a road hierarchical structure feature matrix, a dynamic time period feature matrix and a hidden space topology feature matrix; the dynamic adjacency matrix generation layer is used for constructing a self-adaptive adjacency weight matrix based on the hidden space topological feature matrix; the cross-level correlation perception layer performs bidirectional feature modulation on the road hierarchical structure feature matrix and the dynamic time period feature matrix to generate a space-time coupling feature tensor; and the prediction layer inputs the space-time coupling feature tensor into a space-time diagram convolution prediction network and outputs a traffic flow prediction value in a future time period. According to the method, the expression ability of the model on potential heterogeneous association between the nodes is improved, and the generalization ability across regions and time periods is effectively improved.

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 technical field of energy consumption prediction, in particular to a building envelope energy-saving effect prediction method, which specifically comprises the following steps: deploying a sensor in a building maintenance structure to collect related data, and constructing a data set; marking the collected data to form a training set; a physical constraint decoupling normalization method is adopted for the data in the training set to generate features after decoupling normalization; a weighted feature vector is generated by adopting an attention mechanism guided by physical prior; constructing an energy-saving effect prediction network, and inputting the weighted feature vectors into the network for prediction; optimizing the network to obtain a trained network; newly-collected data is processed and then input into the trained network, the final prediction probability of each energy-saving grade is output, and the grade with the maximum probability is taken as a prediction result. According to the method, the collected data is preprocessed and then input into the network, so that the defects of inaccurate data processing, unreasonable feature selection and the like can be overcome, and the accuracy of a prediction result is improved.

The invention is suitable for the technical field of robots, and provides a path planning method and a robot, and the method comprises the steps: obtaining the environment information of a target area, building a grid map according to the environment information, and enabling the grid map to comprise a starting point area, an end point area and an obstacle area; determining a global path through a path search algorithm according to the grid map; in the process of controlling the robot to move along the global path, performing local path optimization through a dynamic window algorithm; wherein the dynamic window algorithm calculates the score of each candidate local path through a trajectory cost function and determines the optimal local path, and a strategy network output score obtained by predicting the candidate paths through a pre-trained deep learning strategy model is introduced into the trajectory cost function. The adaptive capacity of the robot in a dynamic environment can be improved.

The invention provides a remote intelligent operation monitoring method and system for an intelligent substation, relates to the technical field of intelligent operation and maintenance of substations, and relates to multi-source heterogeneous sensing, depth feature modeling, fault prediction evaluation and model self-optimization. According to the method, electrical, environmental and meteorological data are collected through heterogeneous sensors, a structured original data set is constructed, time sequence prediction is carried out in combination with a convolution-LSTM model, a Transform fusion network is utilized to output a fault probability and a confidence interval, online early warning and response control are realized, and the method has a federated learning driven adaptive updating capability.

The invention relates to an unmanned aerial vehicle group collaboration and task allocation optimization method and system based on edge computing, in particular to the field of communication, efficient task allocation and threat early warning are achieved through dynamic modeling of a multi-modal sequence prediction model and a heterogeneous relation graph, firstly, real-time environment and historical task data are fused, and the real-time environment and historical task data are fused; generating space threat probability distribution and an environment dynamic coefficient; then, a dynamic adjacency matrix is used for adjusting a subgraph embedding vector, a threat-driven topological structure is reconstructed in real time, a decision-making layer outputs a task instruction and value evaluation based on a hierarchical decision-making network, task acceptance, task abandoning and path selection are intelligently optimized, and task conflicts are solved through a federal consensus mechanism; according to the method, the cooperation efficiency and the task execution accuracy of the unmanned aerial vehicle group in a complex environment are effectively improved, and task allocation and resource use are optimized.

The invention relates to the technical field of power marketing, in particular to an AI large model-based power marketing data analysis method, which comprises the following steps of: acquiring structured data and unstructured data in a power marketing system, and generating unified coded data after space-time alignment and pre-training word embedding model processing; inputting the unified coding data into a pre-trained power field large model, and extracting static, dynamic and semantic features through multi-modal fusion, feature decoupling and semantic anchoring; constructing entity link feature pairs in combination with a power knowledge graph, and enhancing fusion feature expression through a graph attention mechanism; and finally, inputting a dynamic weight gating network, outputting an abnormal user identification tag, a demand response strategy and a customer loss early warning probability, and executing strategy optimization under specific conditions. The method can be widely applied to risk identification, strategy making and user behavior prediction tasks in power marketing.

The invention relates to the cross technical field of artificial intelligence and environment monitoring, and discloses an algal community structure change prediction algorithm and system based on multi-source data fusion, and the algorithm comprises the steps: obtaining water quality, weather and plankton multi-source time sequence data; performing time alignment and missing value interpolation; eliminating and screening key environment factors through recursive features; performing dynamic weighted fusion on the multi-modal features by using a space-time attention mechanism; inputting a three-layer stacked LSTM network to output future algae dominant species abundance prediction; and model parameters are corrected on line based on measured data. The system comprises a multi-source data acquisition module, a preprocessing module, a key factor extraction module, a space-time attention fusion module, a dynamic prediction module and an adaptive correction module. According to the method, the prediction accuracy and stability are remarkably improved, and algal bloom early warning and ecological regulation are effectively supported.

The embodiment of the invention provides a hail identification and prediction method based on multi-source meteorological data fusion and an attention mechanism. The method is applied to the technical field of meteorology and artificial intelligence processing, and comprises the following steps: collecting dual-polarization radar data, satellite remote sensing data and ground meteorological observation data, and carrying out time sequence alignment, data standardization and feature splicing processing; extracting physical mechanism features and statistical texture features of hail clouds from the dual-polarization radar data, the satellite remote sensing data and the ground meteorological observation data; extracting multiple spatio-temporal features of the hail cloud by using a spatio-temporal feature extraction network; and inputting real-time observation data into the trained spatial-temporal feature extraction network, and outputting a hail occurrence probability, a nuclear region position and intensity grade distribution. In this way, the technical problems that in the prior art, multi-modal data noise, inconsistency and insufficient time-space feature capture are caused, and the precision and real-time performance of hail recognition are limited can be solved.

The invention discloses a security reinforcement learning-driven large language model security decision agent, and the decision agent comprises a high-level semantic planner which is used for receiving a target and constraint instruction in a text form, receiving a language or visual observation signal of an environment at the same time, and outputting text formatted security risk information and suggested action planning; the low-layer action actuator is used for receiving low-dimensional observation and semantic codes of the environment, and the semantic codes are output by the high-layer semantic planner after text embedding conversion; the strategy network of the low-layer action actuator outputs a final safety action; the training alignment module is used for optimizing the strategy network and the value network; a high-level semantic planner is fed back and prompted through reward and cost signals collected through environment interaction, and parameters of a strategy network and a value network are trained through a security reinforcement learning algorithm. According to the method, the decision cannot violate the given text security constraint while the decision of the given text target is completed.

The invention belongs to the technical field of production monitoring systems, and discloses a numerical control machine tool dynamic fault diagnosis method and system. The method comprises the following steps: generating a global time reference signal through a main shaft encoder and a clock synchronization protocol; the method comprises the following steps: collecting vibration data of a main shaft bearing in each unit time, current data of an electric cabinet and process parameters, and generating a preprocessed data sequence through transmission delay compensation and multi-rate frequency raising processing; inputting the vibration data and the current data into a preset mechanical-electrical transfer function model, and calculating a time delay parameter; performing phase alignment on the preprocessed data sequence based on the time delay parameter to generate an aligned data sequence; inputting the aligned data sequence into a time sequence neural network, and outputting a fusion feature vector; calculating a cross correlation coefficient of the fusion feature vector, and generating a fault diagnosis result based on a preset cross correlation threshold value; the problem of failure of fault feature extraction caused by data asynchronization in the prior art is solved.

The embodiment of the invention provides a dynamic flexible workshop scheduling method and related equipment, and belongs to the technical field of industrial intelligent manufacturing and production scheduling. The method comprises the steps of obtaining current state information of a workshop in response to a scheduling event; inputting the state information into a pre-trained scheduling decision model for processing; the model outputs state feature embedding through a two-stage feature extraction network: in the first stage, feature extraction is performed on a heterogeneous disjunction graph by using a graph attention network, and in the second stage, expert output is dynamically fused through a hybrid expert model; and finally, outputting and executing a process-machine pairing decision by the actor network. Wherein the model is trained by adopting a near-end strategy optimization algorithm based on multiple commentators; and a meta-learning framework is integrated during training, so that the model obtains strong generalization ability. According to the method, the defects of an existing scheduling method in the aspects of state characterization, multi-target tradeoff and environmental adaptability are effectively overcome, and the efficiency, quality and robustness of dynamic flexible workshop scheduling are remarkably improved.

The invention discloses a nondestructive testing method for composite material defects, and relates to the technical field of nondestructive testing, and the method comprises the following steps: firstly, collecting a scanning waveform of a test piece A, determining a potential defect area based on an echo amplitude and a time difference, and outputting a sequence containing space coordinates, an original waveform and focusing parameters; an effective time window is intercepted after preprocessing, sub-bands are generated through wavelet packet decomposition, and total energy is calculated and normalized to obtain an energy vector; secondly, based on a known sample, evaluating the separability of sub-bands by using a Fisher criterion, sorting the sub-bands, determining an optimal energy dimension through cross validation, combining sub-band energy features with phase and time difference features into composite vectors, inputting the composite vectors into a dual-channel lightweight deep network, outputting defect categories and confidence coefficients, and mapping the defect categories and confidence coefficients to a C scanning frame image; and finally, backtracking the three-dimensional coordinates, calculating the defect volume and the residual wall thickness, and comparing with a material performance database to output a conclusion. The problem of misjudgment caused by echo waveform similarity is solved, and detection closed-loop optimization is achieved.

The invention discloses a goaf collapse risk assessment data fusion system based on big data processing, and particularly relates to the technical field of geological disaster assessment, and the system comprises three core modules: a multi-source data adaptive weighted fusion module which establishes a unified space-time coordinate system, converts non-raster data into a continuous field through Kriging interpolation, and performs data fusion on the continuous field; combining the information entropy and the correlation coefficient to dynamically distribute weights, and generating an enhanced feature field through self-supervised pre-training; the physical-space-time neural network dynamic prediction module is integrated with elastic-plastic mechanical constraint loss and multi-task learning, and outputs a future multi-time step risk probability field and a deformation prediction field through a space-time convolution-memory network; and the risk field three-dimensional subdivision and emergency response module is used for clustering three-dimensional voxels in a high-risk area, automatically calculating risk body parameters, generating an emergency scheme in combination with DEM data and an A * algorithm, and improving evaluation accuracy and emergency scheme practical operability through digital twinborn deduction evaluation.

The invention discloses a power grid dynamic scheduling decision-making method and device based on multi-modal prediction, electronic equipment and a storage medium, and belongs to the field of power system regulation and control operation, and the method comprises the steps: obtaining internal state data and external working condition data of each target power grid device, and a future load change curve of a related power transmission and distribution line, and an equipment feature matrix is constructed through space-time alignment. And inputting the feature matrix into a multi-modal neural network, and outputting the health index, the remaining service life and the fault probability. When the equipment health index is lower than a threshold value, a multi-objective optimization model is constructed, a preventive scheduling strategy is generated, and scheduling is executed; and when the equipment fault probability exceeds a set threshold value, updating the power grid line weight based on load prediction, generating a topology reconstruction scheme of the minimum power failure range, and scheduling according to the topology reconstruction scheme. By implementing the method and the device, the problem that the long-term degradation trend and the short-term sudden risk of the equipment cannot be accurately predicted due to single data dimension in the prior art can be solved.