467 results about "Optimal matching" patented technology

The invention provides an intelligent short message scheduling method and device based on multi-dimensional dynamic optimization, and the method comprises the steps: obtaining the performance data of a plurality of short message channels, and calculating a channel health score based on a weight dynamic adjustment model; determining a scheduling strategy according to the priority identifier of the to-be-sent message, and performing channel screening and optimal matching; executing message sending and monitoring a sending state; terminal state detection is carried out on the failure message through operator base station signaling, and a decision tree model is applied to determine a retry strategy; and performing Huffman coding compression processing on the P2-level marketing messages which fail in retry, and performing batch sending in an idle window. According to the method, a comprehensive performance evaluation index and reward function model is also constructed, and parameter optimization is performed by applying a reinforcement learning algorithm. According to the invention, multi-dimensional dynamic channel scoring, intelligent retry decision making based on terminal state perception, batch processing with balanced cost-time efficiency and a closed-loop self-optimization system are realized, the short message delivery rate is obviously improved, and the invalid retry rate and the sending cost are reduced.

The invention discloses a cost optimization method for resource scheduling management of a cloud data center, and relates to the technical field of cloud computing, and the method comprises the following steps: S1, collecting and modeling a multi-dimensional resource state of the cloud data center, and generating a resource change trend based on a sliding time window and a prediction model; and S2, constructing a multi-target game scheduling model taking calculation, storage, bandwidth and energy consumption as participants, outputting a scheduling game solution in combination with task modal adaptability parameters, and forming task-resource optimal matching. According to the method, through multi-dimensional resource state collection, a sliding time window and an advanced prediction model, resource dynamic changes and future trends can be captured more accurately, more reliable input is provided for scheduling decisions, resource waste or performance bottlenecks caused by information lag are avoided, calculation, storage, bandwidth and energy consumption are modeled as multi-party game participants, and the game efficiency is improved. Nash equilibrium is solved in combination with task modal adaptability parameters, and an optimal scheduling scheme giving consideration to resource utilization rate, performance and cost can be found.

The invention discloses an order-driven cross-factory collaborative production system, and relates to the technical field of intelligent manufacturing and supply chain collaboration, and the system obtains the productivity data, logistics cost and tax policies of a plurality of production bases such as Ningbo, Thailand and America in real time, and carries out the intelligent splitting and distribution of orders through a multi-base productivity game algorithm. And dynamic optimal matching of the order and the productivity is realized. Meanwhile, the system integrates WMS inventory data and third-party logistics real-time quotation, a transportation scheme with the lowest total cost is generated by adopting a genetic algorithm, and cross-border logistics and tax expenditure are remarkably reduced. The system overcomes the problems of information isolated island, response lag, extensive cost control and the like in traditional multi-factory production, realizes global productivity collaborative optimization and supply chain integrated intelligent decision, and improves the enterprise order performance efficiency and the overall resource utilization rate.

The invention relates to the technical field of smart community data management, in particular to a smart community metadata interaction method and system based on an edge computing framework, and the method comprises the steps: carrying out the dynamic storage of multi-dimensional data through a hierarchical rolling cache; generating a uniform resource vector through sequential progressive mapping; community semantic tags are added for the uniform resource vectors through extensible tag slots, connection is established according to association rules, and a resource semantic graph oriented to community services is constructed; receiving a resident task request, constructing a multi-factor weighted scoring engine based on the resource semantic graph, and generating a task data packet with a priority label; a two-channel hybrid scheduling network is constructed, a first channel outputs resource availability and node association degree, and a second channel outputs time sequence characteristics and load prediction parameters; and fusing dual-channel output to obtain task-node correlation and generate an optimal matching strategy. According to the method, task collaboration and resource adaptive management are realized through semantic resource modeling and hybrid scheduling.

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention discloses a network construction type energy storage capacity optimal matching method based on a frequency response model, and belongs to the technical field of gas turbine power plant financial supervision and artificial intelligence, and the method comprises the steps: S1, quantifying the power vacancy of a typical station under an extreme short-circuit fault, determining a virtual inertia constant and a virtual droop coefficient of network construction type energy storage, and carrying out the calculation of the virtual inertia constant and the virtual droop coefficient; setting an initial output coefficient and adjusting a step length; s2, constructing and integrating a system frequency response model based on the inertia and frequency modulation characteristics of the conventional unit, the wind turbine generator and the network construction type energy storage; s3, applying power disturbance to the system, gradually increasing an initial output coefficient and performing simulation, and recording the maximum frequency deviation, the maximum frequency change rate and the quasi-steady-state frequency deviation; and S4, when the maximum frequency deviation, the maximum frequency change rate and the quasi-steady-state frequency deviation are respectively smaller than or equal to set threshold values, the energy storage capacity proportion corresponding to the current initial output coefficient is the optimal proportion. According to the method, the problem of accurately measuring the optimal configuration proportion of the network construction type energy storage in the new energy station is solved.

The invention discloses a task allocation method, device and equipment, a storage medium and a computer program product, and relates to the technical field of computers.The method comprises the steps that reasoning task information in a first preset time period and computing power information of all computing cards in a heterogeneous computing system are obtained; predicting a predicted number of the reasoning requests received in a second preset time period based on the reasoning task information; and according to the reasoning task information, the computing power information of each computing card and the predicted number, allocating a pre-filling task or a decoding task to each computing card, and determining the number of tasks allocated to each computing card, so that the time for the heterogeneous computing system to respond to the predicted number of reasoning requests is shortest. According to the method, the optimal matching of the heterogeneous resources and the reasoning tasks is realized, the utilization rate of the heterogeneous resources is improved, and the execution efficiency of the reasoning tasks is improved.

The invention relates to the technical field of intelligent recommendation, and discloses an AI intelligent matching method based on a knowledge graph, and the method comprises the steps: constructing a quaternary knowledge graph containing a time dimension; identifying legal entities and semantic relationships by adopting an entity identification and relationship extraction technology; multi-level semantic features are extracted through a two-layer progressive semantic matching algorithm of a grammar layer, a semantic layer and a reasoning layer; constructing lawyer ability portraits based on a heterogeneous graph neural network and a time sequence perception graph convolution technology; progressive matching calculation is adopted, the optimal matching weight is learned through a multi-layer attention mechanism, and dynamically optimized intelligent matching is achieved. The technical problems of cold start, insufficient semantic understanding ability and poor timeliness processing ability in the existing legal consultation matching system can be solved, and the matching precision and the user satisfaction are improved.

The invention relates to the technical field of electric digital data processing, and discloses a multi-dimensional data logic processing method based on an artificial intelligence algorithm, which comprises the following steps: receiving a target discrete data packet, extracting metadata, and calculating to generate a dimension entropy feature vector representing data logic complexity; inputting the vector into a preset topological mapping model, and outputting an initial adjacent matrix; calling a feedback suppression mask matrix generated based on a historical operator utility state, and executing bitwise logic AND operation with the initial adjacent matrix to generate a corrected effective topological matrix; the matrix is analyzed, a logic operator function pointer is dynamically indexed in an instruction cache, and a directed acyclic execution linked list is constructed; according to the method, redundant logic nodes in AI prediction are definitely eliminated through a bit operation mask mechanism based on historical feedback, and deterministic convergence of processing delay and optimal matching of computing power resources are achieved.

The invention provides an elevator multi-mode crowd feature perception and intelligent advertisement putting method and system, and relates to the technical field of intelligent advertisement putting, and the method comprises the steps: collecting multi-mode perception data in an elevator through an edge computing terminal; performing feature decoupling on the data, performing cross-modal semantic alignment, establishing a directed association relationship between modals, and constructing a scene feature map; calculating the topology importance degree of map nodes, screening feature nodes, and extracting context information for semantic coding; mapping the scene semantic code and the advertisement audience semantic code to a two-dimensional coordinate system to construct a semantic matching graph, and extracting an optimal matching path to form a candidate set; predicting a scene evolution trend based on the scene characteristic spectrum evolution trajectory, and calculating an advertisement adaptive score to generate a playing sequence; putting and collecting user interaction data feedback according to the sequence to update the graph structure. According to the invention, accurate crowd feature recognition and advertisement dynamic matching are realized, and the advertisement putting efficiency and the user experience are improved.

The invention discloses a battery capacity prediction and state evaluation method and system based on multi-model collaborative learning, and belongs to the technical field of battery management. The method comprises the following steps: constructing a database containing multiple lithium ion battery long-term cycle data, and classifying according to a capacity attenuation trend; cleaning and preprocessing short-term cycle data of the to-be-tested battery; matching the to-be-tested data with the long-term attenuation trend in the database by using a clustering algorithm, and determining an optimal matching trend; distributing weights for the data in the matching trend by adopting a correlation algorithm, and generating initial capacity attenuation prediction; performing sequence correction on the preliminary prediction in combination with meta-learning and a related model, and generating a smooth future attenuation trend conforming to a physical law; and outputting a capacity prediction and health state evaluation result, and evaluating the prediction precision through a root-mean-square error and an average absolute percentage error. The method significantly improves the precision and generalization ability of long-term capacity prediction, and is suitable for various scenes such as electric vehicles, energy storage systems, consumer electronics and the like.

The invention discloses a dynamic learning path planning method and system based on a learner portrait. The method comprises the steps of collecting learning behavior data, learning result data and background attribute data of a target scholar based on a plurality of data sources, and obtaining knowledge point mastery degree vector representation, learning style preference and cognitive ability level to construct a multi-dimensional dynamic portrait of the target scholar; generating an initial personalized learning path of the target scholar based on a preset learning target and the multi-dimensional dynamic portrait of the target scholar; learning process data of a target scholar for related learning resources is monitored in real time, a learning effect evaluation report is generated according to the learning process data, a multi-dimensional dynamic portrait is adjusted to trigger a path replanning mechanism to generate a brand new personalized learning path, and the brand new personalized learning path is pushed. The learning path and the current state of the learner are kept optimally matched all the time, and personalized static planning is upgraded to dynamic syndrome guidance.

The invention relates to the technical field of computer vision, and discloses a vehicle paint make-up color matching method based on computer vision, and the method comprises the steps: collecting a vehicle paint image in real time through an industrial camera, constructing a multi-dimensional color feature extraction system after preprocessing, and building a color feature database through a color space conversion technology; and positioning a target color card by combining a feature matching algorithm. Meanwhile, a historical paint make-up case information base is constructed, a multi-factor weight distribution model is established, an optimal matching scheme is solved by adopting an improved particle swarm optimization algorithm, and finally a result is output through an interactive verification platform and tracked and fed back. According to the method, multiple factors are comprehensively considered, the precision and efficiency of color matching are improved, and the method is suitable for paint make-up color matching of vehicles of different vehicle types, years and paint surface types and has high practicability and popularization value.

A system and method for matching multimodal user-query input at an information handling system includes storing capabilities associated with a plurality of AI productivity tool-enablable software applications in a hierarchical capabilities decision tree with each node including natural language textual and non-textual modality descriptions of a capability and multimodal capability intent values generated from the same. Executing code instruction to receive a multimodal user-query input in any of text, audio, or image and generate a multimodal query input intent value for matching to a best match capability for a responsive action to be taken by one of the plurality of AI productivity tool-enablable software applications executing on the information handling system via a semantic similarity search comparing the multimodal query input intent value to the multimodal capability intent values in the hierarchical capabilities decision tree based on a highest cosine semantic similarity search score.

The invention discloses a defect segmentation loss evaluation method based on space consistency optimization, and relates to the field of computer vision and defect segmentation. The problem that an existing loss calculation method is difficult to give consideration to defect space consistency and fair modeling of defects of different scales, and consequently loss evaluation accuracy is poor is solved. According to the method, a prediction result and a defect area in a real label are subjected to instantiation modeling, a prediction and real defect set is constructed, and one-to-one optimal matching of the prediction result and the real defect set is realized through a Hungary matching algorithm; in order to enhance the modeling capability for defects of different sizes, a Wasserstein distance is introduced to measure the spatial similarity of matched defect pairs, defect segmentation loss is calculated for successfully matched defect pairs based on the distance, a fixed loss value is directly applied to predicted defects which are not successfully matched, and the sum of the defect segmentation loss and the fixed loss value is used as a total loss value of a sample. The method is mainly applied to an industrial product surface defect segmentation task.

The invention relates to the technical field of three-dimensional image data processing, in particular to a priori constraint-based tower point cloud and model multi-scale registration method and system. The method comprises the following steps: S1, performing standardization processing on multi-source data to enable to-be-registered point cloud data to be matched with a point cloud representation standard of a prior knowledge base of a candidate model; s2, preliminary screening; s3, performing coarse registration, and performing candidate model rapid screening and key point priority sampling strategies; s4, fine registration is carried out, soft constraint optimization is adopted, priori knowledge is quantized into weights, and weighted ICP and nonlinear least square are combined for solving; and S5, performing matching scoring according to the matching parameters, and obtaining an optimal matching result of the to-be-registered point cloud data and the candidate model of coarse registration. A self-learning priori knowledge base is constructed, a high-confidence matching result is fed back to update the priori base, a dynamic weight adjustment strategy and a candidate screening rule are realized, and the adaptability of the system to different pole tower models is improved.

The invention provides a coal ash composite material goaf closed filling parameter intelligent matching method, and belongs to the technical field of deep learning and mining engineering crossing. According to the method, dynamic optimization and accurate matching of the filling parameters are realized through combination of data driving and an intelligent algorithm. The method comprises four core links: multi-source data perception and fusion, a material performance prediction model, key parameter identification and boundary constraint, and intelligent matching and optimization decision. According to the method, the modeling capability of the model for the complex coupling relationship is improved through the attention mechanism and the feature cross network; the performance evolution trend of the material under different proportions and process conditions is accurately predicted, key regulation and control parameters are automatically identified, and efficient search and optimal matching of a parameter space are achieved through a strategy gradient method. According to the method, multi-source data can be fused, key parameters can be dynamically identified, and the intelligent matching and optimizing capability is achieved.

The invention provides a multi-merchant hardware equipment supply chain demand intelligent matching method, and relates to the technical field of supply chain management, and the method comprises the steps: obtaining a demand order and supply capability data through a supply chain platform, and enabling the supply capability data to obtain production line test data in real time through an Internet of Things interface; performing preliminary screening based on equipment types and necessary authentication standards; calculating the comprehensive reliability of the optical fiber sensing system by adopting a comprehensive evaluation model fused with nonlinear transformation; calculating the multi-dimensional demand integrating degree of the supplier and the demander through multi-dimensional difference analysis and normalization processing; intelligent sorting is carried out based on weighted scores of configurable weight factors; implementing global productivity monitoring and conflict resolution optimization, and outputting an optimal matching pair; and continuously iteratively optimizing the evaluation model parameters according to the performance feedback data by adopting an incremental learning mechanism. According to the method, accurate matching and dynamic optimization under multi-target constraints are realized, and the supply chain resource configuration efficiency and the system autonomy capability are effectively improved.

The invention relates to the technical field of data management, and discloses a labor market supply and demand prediction and intelligent matching system driven by real-time big data. The system comprises a feature separation module, a spatial-temporal feature matrix acquisition module, a supply and demand gap prediction module, a dynamic knowledge graph generation module, an optimal matching path acquisition module and a labor resource allocation module, and is characterized in that dynamic features of original data streams are extracted and separated to obtain a standardized feature vector set; performing space-time alignment on the standardized feature vector set to obtain a space-time feature matrix; capturing time dependence, aggregating spatial neighborhood information to obtain a regional supply and demand gap predicted value to construct a basic graph skeleton, and mapping entity dynamic attributes in the original data stream to the basic graph skeleton to obtain a dynamic knowledge graph; generating an optimal matching path set; performing desensitization processing on the optimal matching path set to obtain a labor resource allocation scheme; according to the invention, the rationality of labor resource allocation can be improved.

The invention relates to the technical field of server resource scheduling, in particular to a server virtualization integration system and method. The method comprises the following steps: executing a multi-dimensional reference pressure test on each NUMA node in a physical server cluster to generate physical node performance data; monitoring a virtual machine cache event of a tenant virtual machine in real time by utilizing the virtualization management platform, and determining tenant load snapshot data; evaluating the matching degree between the remote memory access demand of the virtual machine and the node localization supply capability according to the tenant load snapshot data, determining an optimal host migration target, and starting real-time migration of the virtual machine by the virtualization management platform to obtain a server migration integration process; and monitoring an abnormal state after migration in real time according to a server migration integration process so as to realize secondary forced scheduling integration request processing. According to the method, the optimal matching between the virtual machine and the physical node in the server is realized, the resource utilization efficiency is improved, and the cross-NUMA access overhead is reduced.

The invention belongs to the technical field of industrial data processing, and provides an intelligent material matching method and system fusing multi-modal features and fuzzy matching, and the technical scheme is that data in obtained electronic component list data is analyzed, key characters in character strings are identified, and a constructed mapping table is called to carry out mapping replacement on the key characters, so that the matching accuracy of the electronic component list data is improved. Obtaining each field parameter of the mapped electronic component; performing matching based on the constructed manufacturer alias knowledge graph and the mapped manufacturer field parameters to obtain a manufacturer matching result; screening the mapped material number parameters to obtain a material number candidate set, performing semantic similarity calculation based on the material number candidate set and a constructed special word vector model in the field of electronic components, when the similarity is greater than a set threshold, performing accurate matching, otherwise, triggering fuzzy matching, calculating a service score according to a matching result, and obtaining a service result; and performing multi-objective optimization based on a service score result to obtain an optimal matching scheme. And the matching accuracy and the purchasing decision-making efficiency are obviously improved.

The invention provides a zero-code biological information analysis method and device based on multi-agent collaboration, and the method comprises the steps: setting a large language model as a plurality of agents through cue words, carrying out the scoring and rearrangement of tools in a tool library through the plurality of agents, and finding an optimal matching tool through discussion; exploring type assembly is carried out by utilizing intelligent agents of a plurality of explorers according to input and output parameters of different tools, fusion is carried out after multiple rounds of tests are successful, a workflow is established, the workflow is written into codes, and the codes are executed to obtain an analysis result; checking and screening the analysis result to obtain screening information; presenting the screening information in a graphic mode to obtain graphic information; and obtaining candidate answers of the to-be-analyzed task according to the screening information and the graphic information.

The invention relates to the technical field of audio and video generation, in particular to a real-time audio and video synchronous generation method and system based on semantic parse, and the system and method respectively decompose texts, audios and videos into minimum semantic units, and combine with a pre-training model to ensure that each modal unit is complete in semantics and accurate in granularity. And meanwhile, a cross-modal context consistency coefficient is introduced to filter pseudo associations with similar semantics but irrelevant scenes, and a matching uniqueness punishment mechanism is matched to avoid generation of conflicts, so that the problems that semantic associations are fuzzy and contents deviate from requirements in traditional video and audio generation are effectively solved. Video and audio synthesis based on an optimal text-audio and video semantic unit matching combination output by game optimization and core strategy parameters is a key advantage of guaranteeing generation quality. The accurate corresponding relation between the text and the audio and video unit is defined through the optimal matching combination; the core strategy parameters provide a dynamic adaptation basis for the synthesis process, and audio and video generation modules can be guided to adjust quality parameters and resource allocation according to scene requirements.

The invention relates to a GPU scheduling optimization method for task and node bidirectional modeling, which comprises the following steps: step 1, task modeling and classification, step 2, node feature modeling and scoring mechanism, step 3, priority scheduling of strong dependency tasks, and step 4, optimal matching scheduling of weak dependency tasks. And constructing an adaptive scoring matrix between the task and the node, and realizing optimal matching between the task and the node by means of a graph theory matching model. The method comprises the following steps: constructing a multi-dimensional feature modeling system, and abstractly expressing resource demand features of a computing task and computing power features of a computing node; and then, based on a multi-index comprehensive weighting mechanism, evaluating a matching relationship between the task and the node, fusing factors such as a task dependence structure and a task emergency degree, and executing fine distribution by adopting a hierarchical scheduling mechanism, so that efficient utilization of GPU resources and interpretable optimization of a scheduling result are realized.

The invention relates to a federated learning method supporting heterogeneous model architecture search and zero sample knowledge fusion. The method comprises the following steps: providing a federated learning system to be subjected to federated learning, and when the federated learning system is configured to perform federated learning and any client executes teacher model generation processing, searching and generating a neural network local teacher model optimally matched with the client based on local private data in the client, and sending the generated neural network local teacher model to a connected server, after the server executes student model generation processing, at least generating a global shared student model, when the global shared student model is generated, training the constructed basic student model by using the pseudo-supervised training data set, and generating the global shared student model by using the pseudo-supervised training data set. And after the basic student model is subjected to distillation training, a global shared student model is generated. According to the method, the heterogeneous model of the client can be effectively supported, the personalized capability and privacy protection are improved, the communication cost is reduced, and the model generalization is excellent.

The invention relates to the technical field of intelligent medical dispatching, in particular to a multi-terminal collaborative nursing worker resource intelligent allocation method, which comprises the following steps of: firstly, acquiring positioning, road, nursing worker physiology and old people demand data and generating multi-modal standardized data; constructing a three-dimensional digital twin potential field, predicting a corrected potential field by using photons, constructing a matching model by using the corrected potential field, a nursing worker capability vector and an old man demand vector, and performing annealing optimization to obtain initial matching; constructing a nursing worker-old person-time period tripartite graph based on a matching result, and obtaining optimal matching by adopting tension diffusion and gradient projection iteration; double digital signatures are executed on each piece of matching, and a non-homogeneous commitment is cast in the block chain, so that credible performance is realized; the wearing end spiking neural network continuously outputs fatigue probabilities, the fatigue probabilities are mapped into potential energy increments and written back to the potential field, and sub-potential field resolution and zero-knowledge post replacement are triggered and closed-loop updating is carried out. According to the method, the scheduling real-time performance and fairness are improved, the fatigue risk is reduced, and the whole service process is traceable.

The invention relates to the technical field of visual position recognition, and particularly discloses a multi-modal visual position recognition reordering method and system based on guidance, and the method comprises the steps: obtaining a query image, and retrieving a plurality of candidate images based on a pre-trained visual basic model and the query image; constructing a composite multi-modal prompt object, wherein the composite multi-modal prompt object comprises an image pair formed by the query image and the current candidate image, and an instruction text used for guiding a multi-modal large language model to perform visual comparison; outputting a structured similarity judgment result, wherein the result comprises a quantitative similarity score; and sorting based on the similarity scores corresponding to all the candidate images, and determining the candidate image with the highest score as an optimal matching result. Through combination of guiding type prompt engineering and structured output, an intermediate text generation link is avoided fundamentally, and the calculation efficiency is improved while the fidelity of all original visual information is reserved.

The invention discloses a multi-camera target tracking method and device based on feature recognition, and relates to the technical field of computer vision. The method comprises the following steps: collecting a video stream, and carrying out target tracking to obtain a plurality of targets; extracting the multi-modal features of each target, fusing the multi-modal features to obtain a multi-modal fusion feature vector, and generating a global trajectory fragment; when any target to be matched leaves the view field of the current camera or enters the view fields of other cameras, triggering a cross-camera matching event, and screening out a candidate target set from other cameras; executing a cross-camera matching event, and matching an optimal matching target with the highest similarity with the to-be-matched target in candidate target sets of other cameras; and associating the to-be-matched target with the optimal matching target, and splicing to form a continuous global motion track of the to-be-matched target. The problems of poor target tracking effect, insufficient track continuity and low calculation efficiency in the prior art are solved.

The invention relates to a smart power grid space-time scheduling method based on a self-adaptive dynamic graph neural network, which comprises the following steps of: collecting time sequence data from distributed data nodes of a smart power grid in real time, and preprocessing the data; self-generating an adaptive adjacency matrix, and adjusting an edge weight in the adjacency matrix according to the change of real-time data by a self-adaptive algorithm; performing spatio-temporal feature extraction on the generated adaptive adjacency matrix through a spatio-temporal convolutional network STCN, capturing a complex spatio-temporal dependency relationship between power grid nodes, obtaining a feature graph containing a spatial and temporal dependency relationship, and representing the operating states and mutual dependency conditions of different time nodes of the power grid by the features; real-time scheduling optimization: inputting the extracted spatial-temporal characteristics into a scheduling optimization module, and calculating the optimal matching between the load demand and the power generation capacity; the node state is continuously monitored in the scheduling process, and the scheduling strategy is dynamically adjusted. Distribution of electric power resources is dynamically adjusted, and it is ensured that electric power supply of the load center meets requirements.

The invention discloses a method and a device for calculating the matching capacity of network following equipment and network constructing equipment. The method comprises the following steps: analyzing advantages and disadvantages of a configuration address of networking equipment according to a constructed wind power plant grid-connected model to obtain an access address of the networking equipment; constructing a target function and a constraint condition of the matching capacity of the network construction equipment and the network following equipment; according to the access address and the constraint condition of the network construction equipment, an improved non-dominated sorting genetic algorithm is adopted to solve the target function, and the optimal matching capacity of the network construction equipment and the network following equipment is obtained.