136 results about "Resource integration" patented technology

The invention provides a lightweight digital human lesson preparation system based on an intelligent agent, and belongs to the field of intelligent teaching. Through collaborative operation of four core modules of knowledge graph construction and reasoning, multi-modal cognitive agent, lightweight digital human generation and intelligent teaching plan assistance, the problems of low efficiency of resource integration, teaching content homogenization, insufficient digital human interaction experience and the like in traditional lesson preparation are solved. The knowledge graph construction and reasoning module is used for constructing a structured knowledge graph and realizing knowledge point association mining and teaching logic reasoning; the multi-modal cognitive agent module is used for generating personalized explanation content according with a teaching target by fusing multi-modal courseware analysis, semantic understanding and lecture style dynamic adaptation functions; the lightweight digital human generation module is combined with model pruning and emotion modeling technologies to synchronously output natural voice and a high-simulation digital human image; the intelligent teaching plan auxiliary module helps the teacher to intelligently generate a teaching plan and a teaching outline according to the courseware content based on the knowledge graph.

The invention discloses a hyper-converged server multi-resource integration system and a scheduling method, belongs to the technical field of computer resource management, and aims at solving the problems that a traditional hyper-converged server is dispersed in resource scheduling, low in storage efficiency, difficult to identify abnormities and the like. Multi-source data is obtained by means of distributed acquisition nodes, a distributed time sequence database architecture is adopted for storage, a standard data set containing a multi-dimensional index is constructed, and efficient storage and rapid retrieval of the data are achieved. Positioning target data to construct a graph model, building a layered and partitioned distributed graph database, and capturing change events in real time to realize dynamic updating. And mining a resource causal relationship by using a graph neural network, and screening an effective causal chain. Meanwhile, according to a graph model, monitoring weights of nodes and connecting edges are calculated, differential monitoring is implemented, potential abnormal points are accurately identified, and a resource scheduling strategy is generated in combination with a causal relationship. Real-time feedback adjustment and database updating are performed during execution, multi-resource deep integration and intelligent scheduling are achieved, and the resource utilization rate and stability of the system are remarkably improved.

The invention provides a classical music cross-modal high-reliability experimental data set construction method for full-scene teaching application, and the method comprises the steps: constructing an original data set through collecting audio, video, music score and other multi-modal data, carrying out the standardization of each modal data through a preprocessing technology, and obtaining a multi-modal data set in a unified format; according to the semantic feature vectors, a knowledge graph is constructed, nodes represent semantic relations between music elements, edges represent semantic relations between the elements, a graph convolutional network is adopted to encode the knowledge graph, and semantic representation of the teaching content is obtained; a virtual reality rendering technology is adopted, multi-modal teaching resources are integrated into an immersive teaching scene, a content sequence is presented in real time, and dynamic teaching experience is obtained; and continuously collecting interaction data of the learner, updating the knowledge graph and the semantic feature vector, and optimizing the cross-modal fusion model by adopting an incremental learning algorithm to obtain a self-adaptive teaching data set.

The invention discloses a case resource integration data system based on big data analysis, and belongs to the technical field of medical data. The method comprises the following steps: acquiring hospital case data and corresponding disease type data to construct a resource integration range, acquiring a personal case information set provided by medical consultation of a patient, performing sensitive data extraction on the personal case information set to obtain a dynamic case parameter set, and sending the dynamic case parameter set to a data risk analysis module; the multi-source data acquisition module processes the personal case information set as follows; according to the method, a data integration-risk analysis-clinical intervention closed-loop system is constructed, preorder data standardization integration guarantees analysis reliability, accurate risk analysis provides a direction for intervention, multi-level alarm and pre-plan matching is achieved through linkage of the preorder data standardization integration and the accurate risk analysis, prediction diagnosis reports and intervention suggestions are automatically generated, invalid operations are reduced, the clinical decision-making efficiency is improved, and the system is suitable for large-scale popularization and application. And meanwhile, through dynamic threshold updating and system self-iteration optimization, the adaptability and practicability of the system are continuously enhanced.

The invention discloses an intelligent database switching method and device, computer equipment and a storage medium, belongs to the technical field of big data, and is applied to a financial database downtime processing scene. According to the method, the node state of the database is monitored in real time, multi-dimensional load evaluation is combined, the optimal main and standby database combination is automatically selected, second-level fault sensing and rapid switching are achieved, and a minute-level time window needed by traditional database recovery is remarkably shortened. The database identifier is embedded in the service main key, so that the request can accurately position the corresponding database node, complex cross-database query and routing overhead are avoided, and the system response efficiency is improved. In addition, through data seamless transmission and automatic abnormal switching between the main database and the standby database, the risk of service interruption caused by database faults is reduced. According to the scheme, the high availability and the fault recovery speed of the database are improved, repeated construction of a multi-product-line database cluster is reduced through resource integration, and a large amount of hardware and operation and maintenance cost are saved.

The invention provides a scientific and technological intelligence resource integration method based on a real-time data stream, which comprises the following steps: monitoring the task queue depth of each node through a load migration trigger condition, and if the queue depth of a certain node exceeds a preset upper limit value, starting a task migration process, selecting a target node with the shortest response time as a migration object in combination with the node response time statistical data, and obtaining a load redistribution instruction; and according to the load redistribution instruction, a processing result synchronization mechanism is adopted to coordinate the data processing progress among the nodes, if a source node completes task migration, a global task state table is updated, if a target node successfully receives a task, a confirmation signal is sent, and a closed-loop control mechanism for cooperative processing among the nodes is formed.

The invention is suitable for the technical field of system fault monitoring, and provides a system fault monitoring method based on log real-time synchronization and active scanning, and the method comprises the steps: capturing a log increment in real time through a file system event monitoring trigger, analyzing a newly added log content, and synchronizing the log increment and the newly added log content to message middleware; a distributed active scanning unit is configured, the scanning period is dynamically adjusted, and node resources are expanded and shrunk based on the containerization technology; integrating the abnormal data of the user side, the interface state data of the server side and the log stream data, and constructing a multi-modal data set; a normalized error report increment is calculated based on a 30-minute sliding window, and a pre-processing instruction is generated in combination with a random forest algorithm and Bayesian optimization; executing corresponding operation according to the pre-processing instruction; according to the method, a traditional passive mode is broken through, the fault monitoring confidence coefficient is remarkably higher than that of an industry standard, sub-health perception and active blocking of a high-concurrency scene are supported, and a real-time and accurate intelligent operation and maintenance solution is provided for a complex system.

The invention belongs to the technical field of auxiliary interactive learning, and particularly relates to a battery-grade lithium carbonate industrial chain monitoring and cognitive deduction system based on industrial intelligence, which comprises a resource management and control database, a knowledge graph modeling library, a situation deduction processing module, a service collaborative application module and a safety protection module, and the resource management and control database is used for storing cross-domain multidisciplinary chain type ubiquitous resource data. According to the battery-grade lithium carbonate industrial chain monitoring and cognitive deduction system based on industrial intelligence, through the resource management and control database and the multi-task distributed scheduling engine, the resource integration and dynamic scheduling capability, the cloud side computing power adaptive scheduling and multi-task parallel processing mechanism are significantly improved; the computing power distribution of the edge end lightweight chip and the cloud high-performance cluster is optimized, the problems of low resource access efficiency and delayed real-time data analysis are reduced, the collaborative management and control of large-scale enterprise resources are realized, and the real-time dynamic monitoring requirement of a complex industrial chain is met.

The invention discloses an intelligent patrol control method, and relates to the technical field of intelligent patrol, and the intelligent patrol control method mainly comprises the steps: obtaining a resource data storage model according to patrol resources; according to the resource data storage model, a quick response police force task is obtained based on shortest path calculation and a task matching method; a major alarm condition or a safety event is confirmed, and emergency response processing is performed according to the quick response police force task; and performing multi-device cooperative scheduling according to the scheduling task and the patrol resources. By implementing the intelligent patrol management and control method provided by the invention, the police scheduling efficiency and response capability can be improved, the accuracy and collaboration of police resource integration can be improved, the system construction and operation cost can be reduced, the safety and reliability of the system can be enhanced, and the handling capability of emergency events can be improved.

The invention discloses an optimal scheduling method for a regional integrated energy system, and relates to the technical field of energy optimization. Comprising the following steps: step 1, multi-source data hierarchical acquisition and preprocessing; step 2, multi-time scale uncertainty scene modeling is carried out; step 3, constructing a multi-objective optimization model; 4, intelligent algorithm solving and dynamic scheduling are carried out; 5, carrying out constraint processing and resource integration; and 6, carrying out closed-loop correction and strategy optimization. According to the regional integrated energy system optimization scheduling method, a dynamic relaxation factor mu (t) is introduced to adjust equipment climbing constraint in real time, and a virtual energy storage equivalent model is combined to integrate a transferable load, so that when the load of the system suddenly increases, the equipment power adjustment fluctuation is controlled within 10% of rated power, frequent out-of-limit of energy storage is avoided, and meanwhile, the optimal scheduling of the regional integrated energy system is realized. The grading pricing mechanism excitation system of the stepped carbon transaction model preferentially consumes new energy, and the carbon emission is greatly reduced compared with that of a traditional fixed carbon price model.

The invention discloses a data interaction management and control system and method for judicial and administrative business resource integration. The system comprises a presentation layer, an application layer, a service layer, a data access layer and an infrastructure layer, wherein the presentation layer is used for providing a visual interface of a judicial administration data collaborative sharing portal and an interactive management and control portal, and supporting data collaborative sharing and management and control operation of a plurality of business scenes; the application layer is used for combining functions of the service layer, realizing a business scenarized application module and recording an operation behavior log in real time; the service layer is used for packaging preset business logic and providing an atomization service interface; the infrastructure layer comprises network resource load balancing, an API gateway, a hardware security device, a computing resource server cluster, a message queue, a log storage, a relational database, a distributed file storage and a block chain platform node. The block chain is utilized to realize on-chain data access, behavior data is prevented from being tampered, traceability and truth restoration are facilitated, and a person in charge is positioned.

The invention relates to the field of computer vision, in particular to an intelligent parking system based on a computer vision algorithm, which is provided with a data acquisition module, a resource integration module, an anomaly analysis module, a parking matching module and a parking guide module, determines a vehicle running value and an actual parking effective value, calculates a parking permission characterization value, and displays the parking permission characterization value. Determining whether a parking position for a target vehicle exists in the parking area; determining a target parking area; dividing the target parking area into a plurality of sub-areas; analyzing data information corresponding to the target parking area; and parking guidance is carried out according to the judgment result of the preset condition, and intelligent parking is completed. According to the invention, parking guidance is carried out by determining the target parking area, analyzing the corresponding abnormal data and comparing with the parking related area of the target vehicle, so that the smoothness of the parking path and the parking efficiency are improved.

The invention provides a flexible load hierarchical polymerization optimization regulation and control method. Flexible load operation data is acquired and divided into adjustable and non-adjustable loads, and a classification foundation is laid for subsequent hierarchical aggregation; three-level aggregation of an equipment layer, a region layer and a system layer is executed on two types of loads, and scattered resources are integrated into a hierarchical structure, so that the complexity of directly regulating and controlling massive scattered loads is remarkably reduced; a regulation and control model is established based on a hierarchical aggregation result, and an optimization algorithm is adopted to solve a strategy, so that collaborative optimization from local equipment to a global system is realized. According to the method, the contradiction between resource dispersity and regulation and control real-time performance is solved through a layering mechanism, the transmission efficiency and execution precision of regulation and control instructions are improved, meanwhile, the communication burden and the calculation cost are reduced through an aggregation model, and an efficient framework is provided for flexible dispatching of power grid flexible loads.

The invention provides an integrated recommendation method and device for educational resources, computer equipment and a medium, and belongs to the field of educational resource recommendations. The method comprises the steps of obtaining multi-source heterogeneous educational resource text data; sentence embedding is constructed by aggregating context-related high-dimensional vector representation of each word in the sentences; aggregating the sentence embedding to generate document embedding; obtaining candidate keyword embedding by utilizing a regular expression; calculating document correlation scores, cross correlation scores and importance scores of the candidate keywords, and determining the keywords; comprehensive scores of the sentences are determined based on the keywords, and target sentences are screened out to form a current abstract; setting a dynamic fusion weight parameter alpha, and performing weighted fusion on the current abstract and the historical abstract based on alpha to obtain a dynamic abstract; and performing data matching on the dynamic abstract and historical behaviors of the learner, and performing recommendation based on a matching result. Therefore, not only are the coherence and fluency of the text enhanced, but also the generation capability of the dynamic knowledge abstract is improved.

The invention discloses a power system flexibility resource cooperative scheduling method and device, a terminal and a medium, and relates to the technical field of new energy scheduling. According to the scheme provided by the invention, firstly, energy network structures and flexibility resource parameters in a region are collected, and then clustering is carried out according to resource types and technical parameters; a plurality of resource sets capable of being scheduled in a unified mode are formed, the resource utilization rate is improved through resource integration, distributed energy resources and demand response plans are integrated by constructing a double-layer optimization model, the system flexibility is maximized through an upper-layer model, the operation cost is optimized through a lower-layer model, and various resources are coordinated in combination with an aggregation processing mechanism. And finally, iteratively solving the double-layer model, and finally outputting a scheduling scheme considering both flexibility and economy, so that the problem of insufficient resource collaboration in the prior art is effectively solved, the flexibility and stability of the system are improved, and the adjustment potential of the system is maximized on the premise of ensuring the operation safety.

The invention belongs to the field of load response and regulation, and particularly relates to an adjustable load aggregation and demand response control system of a virtual power plant, which is characterized in that dispersed adjustable load resources are aggregated into a load aggregation cluster containing shared resources through a clustering module; utilizing a screening module to construct an overlapping cluster hypergraph taking the shared resources as hyperedges, and screening a high-value candidate set; an iteration module is adopted to calculate a Shapley value revenue share of each load aggregation cluster through a cooperative game, a joint demand response strategy is generated by taking alliance total revenue maximization and Gini coefficient minimization as targets, and a system state is dynamically updated; and finally, integrating all strategies through a strategy generation module to generate an executable control instruction. According to the method, the problems of low resource integration efficiency, response delay and large execution deviation in the prior art are solved, optimal scheduling and cooperative control of load resources are realized, and the economical efficiency and the stability of the virtual power plant participating in the power grid demand response are remarkably improved.

The invention relates to the technical field of education and teaching, in particular to a teaching digital model electronic secretary construction method based on group intelligence and a large model. The method comprises the following steps: S1, constructing a centerless and boundless mesh network; s2, electronic secretary arrangement and resource integration; s3, teaching resource sharing and cooperation; s4, performing personalized learning path planning; and S5, intelligent teaching assistance. The system integrates group intelligence and large model functions, endows the teaching digital model electronic secretary with strong intelligence ability, can provide auxiliary functions such as intelligent questions and answers, learning suggestions and the like, reduces the teaching burden of teachers, and improves the learning efficiency; the sharing of teaching resources is realized through a centerless and boundless mesh network, so that students and teachers can communicate and cooperate conveniently, and the propagation and common progress of knowledge are promoted; by arranging the intelligent assistant for each user, personalized learning support can be provided according to individual differences of students, and learning requirements of different students are met.

The invention relates to the technical field of renewable energy utilization, and discloses a biomass energy circular economy system based on a multi-stage partner system and an operation method, and the system comprises a resource integration module which is used for obtaining a biomass raw material from an upstream partner; the energy and product conversion module is used for converting the biomass raw material into at least one energy product and at least one substance byproduct; the value circulation module is used for processing the substance byproducts into value-added products; and the multi-stage partner collaborative management module is used for carrying out whole-process resource collaboration, benefit distribution and cooperative relationship management on upstream partners, downstream partners and system operation related parties. According to the invention, a stock right binding mechanism based on a contribution value evaluation model is constructed, the cooperation value of the upstream raw material supplier can be accurately quantified, the long-term income right is given to the upstream raw material supplier, and the design realizes the relationship transformation from short-term buying and selling to long-term symbiosis with the raw material supplier.

The invention discloses a hospital logistics service resource integration and optimization method, and particularly relates to the technical field of resource integration and optimization. The method comprises the following steps: acquiring service request data from a mobile terminal service platform, carrying out standardization processing on the service request data, establishing a multi-source service request feature analysis model, and carrying out differential analysis on data granularity, an emergency level marking mode and information completeness of service requests to construct a service request fusion model; the service request emergency level is re-evaluated in a unified manner through the fusion model, the service request and the service resource are dynamically matched in combination with the real-time load state of the service resource and the scheduling constraint condition, and the maintenance work order is automatically generated and distributed, so that the intelligent, real-time and refined optimization scheduling of the hospital logistics service resource is realized.

The application provides a resource scheduling method and an electronic device, which can be applied to the technical field of computers. The resource scheduling method comprises the following steps: in response to a resource request for a target task, obtaining resource utilization scores of nodes and hardware accelerators in a cluster according to resource usage amounts of the hardware accelerators, resource allocation amounts of the nodes, and a resource demand amount carried by the resource request; determining a target node in the nodes and a target hardware accelerator of the target node according to the resource utilization scores and a scheduling strategy carried by the resource request, the scheduling strategy indicating a scheduling rule matched with resource integration requirements and / or load balancing requirements of the target task; virtually dividing the target hardware accelerator according to the resource demand amount to obtain a virtual hardware accelerator; and binding the virtual hardware accelerator to a container of the target node to execute the target task by using the container.

The invention relates to a red tourist route intelligent planning method based on multi-source data fusion, relates to the technical field of crossing of smart tourism and artificial intelligence, and realizes semantic unification and streaming incremental updating of multi-source heterogeneous red cultural resources by constructing a domain knowledge graph. Dynamic modeling is carried out on the regional road network, the environmental factors and the tourist portraits by utilizing a space-time digital twinning technology; a multi-target reinforcement learning algorithm is adopted, multi-dimensional constraints such as cultural value, safety risk and time satisfaction are comprehensively considered, and a personalized tourist route is generated online; and continuous self-optimization of the strategy is realized through a closed-loop feedback mechanism in the travel. According to the technical scheme, the problems of resource integration and real-time dynamic decision making in a large-scale desert area are effectively solved, and the intelligent level of route planning, the cultural experience depth and the travel safety guarantee are remarkably improved.

The invention discloses an AIGC-driven traditional cultural life education interaction system, which belongs to the field of cultural education and comprises a resource integration module, a situation creation module, a comprehensive evaluation module and a life criterion landing module. The resource integration module is used for collecting traditional cultural life education resources, performing intelligent processing on the resources through an AIGC technology, and constructing a structured digital resource library; the situation creation module generates immersive experience interaction content through AIGC based on the digital resource library, and realizes dynamic interaction between the learner and the traditional cultural life education content; by means of the AIGC technology, traditional cultural life education resources are converted into diversified forms such as 3D scenes, virtual interaction content and role playing games, a traditional single character and static teaching mode is replaced, the interestingness and immersion of learning are enhanced, cognitive lassitude is effectively relieved, and the intention of active participation of learners is stimulated.

The invention discloses a MinIO distributed storage optimization system and method based on an AI cloud desktop, belongs to the technical field of distributed storage, and aims to solve the technical problems of multi-cluster synchronization, enterprise-level security management and heterogeneous resource integration faced by MinIO in the AI desktop. Comprising a unified management platform which provides user authority management through an RBAC model and provides multi-level approval and audit logs through an operation approval process; the non-intrusive monitor is deployed in a source MinIO cluster and used for monitoring a data change event, and the non-intrusive monitor is matched with a synchronization engine in a target cluster to realize multi-cluster data synchronization; the storage abstraction layer is used for managing heterogeneous cloud storage resources, receiving a storage request initiated by a user through the API gateway, and routing the storage request to a corresponding storage service; and the metadata management library provides a data storage service.

The invention provides a 400 call operation control method and system based on task assignment. The method belongs to the technical field of 400 calling. The method comprises the following steps: acquiring basic data of each communication node of a 400 call center, and carrying out in-depth call topological structure analysis on the acquired basic data of the 400 call nodes; the method comprises the following steps: constructing a topological structure model of a 400 call network, and integrating and optimizing a communication architecture of a 400 call center according to the constructed 400 network topological structure model; and node traffic feature extraction is carried out on the 400 call node basic data. Through dynamic task assignment and intelligent resource integration, customer service resources and network bandwidth can be reasonably allocated according to real-time conditions, idle or overload of the resources is avoided, and the utilization efficiency of the resources is improved.

The invention discloses a three-dimensional teaching resource integration system for environmental art design, and the system comprises a data receiving module which collects line features, color features and material features in an environmental art design picture; the style disassembling module is disassembled into a straight line gene unit, a curve gene unit, a dominant hue gene unit, a saturation gene unit and a luminosity gene unit; the style matching module is used for calculating the style identification entropy value of each gene unit and matching a preset style matrix; the learning path recommendation module is used for generating a curriculum schedule according to the matched preset style matrix; and the visualization module is used for visually displaying the preset style label corresponding to the matched preset style matrix and the curriculum schedule. Therefore, the accuracy of environmental art design style recognition can be improved, the curriculum schedule is recommended based on the style matching result, so that the learning path better conforms to the learning requirements of students for specific design styles, and the students can conveniently and visually obtain information through the visual association style label and the curriculum schedule.

The present application relates to the technical field of federal learning, in particular to a multi-agent enterprise asset collaborative management method and system based on federal learning, comprising the following steps: collecting multi-enterprise asset returns, risk volatility and asset liquidity, and performing normalization and feature dimension reduction to generate an asset feature set, calculating gradient differences based on the feature set and dynamically adjusting weights to generate a collaborative weight set, combining upload stability, return volatility ratio and participation frequency for comprehensive analysis to generate a credit factor, calculating the gradient angle to judge the direction deviation adjustment contribution ratio, and finally generating a collaborative return result set by joint weighting, in the present application, feature dimension reduction is optimized through data processing, local model training efficiency is improved, enterprise weight and collaborative direction are adjusted, enterprise contribution imbalance is avoided, collaboration stability is enhanced, gradient upload accuracy is improved, asset collaborative management is finally optimized, resource integration and utilization efficiency are improved, and data security and privacy protection are ensured.

The application relates to the technical field of computing power resource management, and discloses a computing power operation management method based on artificial intelligence, which comprises the following steps: S1, data acquisition and prediction; S2, intelligent scheduling and distribution of computing power resources; S3, comprehensive index optimization of computing power resources; S4, cross-regional multi-service resource integration; and S5, security management and monitoring of computing power resources. The application uniformly schedules and distributes multiple computing power resources, thereby greatly shortening the task execution time and improving the work efficiency; the optimal distribution strategy under different comprehensive indexes of computing power resources is analyzed, various comprehensive indexes are optimized and balanced, thereby realizing low-delay and high-response task scheduling; a unified computing power resource management platform is constructed, a sharing mechanism and cross-service integration are established, computing power resources in different regions, services and departments can be integrated, efficient utilization of the computing power resources and flexible deployment across regions are realized.

The invention provides a model parameter training data batch method and device based on a large model. Monitoring resource data of computing nodes in the cloud environment in real time, wherein the resource data comprises a CPU utilization rate, a GPU utilization rate, a memory occupancy rate, storage I / O performance and network bandwidth; processing the collected resource data to form a resource use snapshot; a scheduling strategy is initialized based on the resource snapshots, task requirements and node resource states are combined, and data batch tasks are dynamically distributed through an intelligent scheduling algorithm; the distributed tasks are received, an execution environment is prepared at a target node, data are preprocessed, the tasks are executed through parallelization and distributed calculation, and resources are released after the tasks are executed; training results of all the nodes are integrated and verified, and final model parameters are stored and published after verification. The scheme of the invention can adapt to the dynamic resource change of the cloud environment and meet the efficient processing requirement of large-scale data.

The invention provides a heterogeneous resource integration method and device. The method comprises the following steps: pre-configuring and storing heterogeneous resource attribute information; and initiating a resource query request or a resource operation request, querying a corresponding resource based on query condition configuration information in the heterogeneous resource attribute information, selecting a resource provider route based on basic configuration information in the heterogeneous resource attribute information, calling a resource provider interface based on interface configuration information in the heterogeneous resource attribute information, and sending the resource provider route to the resource provider interface. Obtaining resource data; processing the resource data based on resource operation configuration information and resource batch operation configuration information in the heterogeneous resource attribute information to obtain processed resource data; and filling the processed resource data into a preset structure, and rendering and displaying the page according to the filled data. According to the method, various heterogeneous resources can be dynamically integrated as required, and the problems of repeated workload and bloated codes in heterogeneous resource integration in the prior art are solved.

The invention discloses a grid code-driven power grid data resource multi-dimensional management method and system, and relates to the technical field related to power grid data management, and the method comprises the steps: carrying out the partition planning of grid division granularity for a preset power grid region, and generating a grid code coding result; obtaining multi-dimensional power grid data type generation features in a preset power grid region; performing hierarchical relationship analysis on a time dimension, a space dimension and an equipment dimension, and establishing a multi-dimensional data hierarchical relationship; constructing a large multi-dimensional hierarchical index model in combination with a grid code coding result and a multi-dimensional data hierarchical relationship; and executing storage and calling management of the power grid data resources of the preset power grid region by using the multi-dimensional hierarchical index large model. The technical problems that in the prior art, power grid data multi-dimensional characteristics are difficult to manage efficiently, time, space and equipment dimension data are processed dispersedly, and query management efficiency and accuracy are low are solved, and the technical effect of improving the accuracy and efficiency of power grid data resource integration management is achieved.