301 results about "Resource assessment" patented technology

The invention provides a Dores front-end node-based service proxy method and system, and relates to the technical field of database access request processing, and the method comprises the steps: collecting front-end node performance indexes, extracting node performance characteristics through employing multi-head recursive attention and causal convolution solution, extracting inter-node correlation characteristics through combining graph attention convolution, and obtaining a database access request; the method comprises the following steps: constructing a node state vector, optimizing a multi-layer connection pool parameter by utilizing an online learning algorithm, establishing a node incidence matrix based on comparative learning, constructing a node performance probability graph evolution model through variational reasoning and Monte Carlo sampling, predicting a node performance change trend, and receiving a database access request; and generating a query plan by adopting an edge attention message passing mechanism, carrying out resource evaluation in combination with performance trend prediction, carrying out scheduling decision making by utilizing multi-agent reinforcement learning and Monte Carlo tree search, carrying out fault detection and migration according to node performance deviation, and updating a probability graph evolution model.

The invention relates to the technical field of power system dispatching, in particular to an emerging load power supply potential improvement method based on dynamic prediction, which comprises the following steps of: acquiring emerging load power consumption, meteorological environment and power grid schedulable resource data in a target area through an Internet of Things sensing terminal, and performing two-channel modeling to obtain a new load power supply potential improvement model; a deep space-time network is used to predict a load curve, a model is established to quantify resource regulation potential, a scheduling priority list and a capacity allocation strategy are generated by means of a matching rule base according to load fluctuation and resource evaluation results, an actual scheduling effect is fed back to the prediction model, parameters are corrected through error back propagation, a closed-loop optimization link is formed, and the scheduling efficiency is improved. The method improves load prediction accuracy and resource scheduling adaptability, is suitable for emerging load power supply optimization in a novel power system, and guarantees stable and efficient operation of a power grid.

The invention relates to the technical field of computers, provides a heterogeneous computing cluster deployment method and a collaborative scheduling system, realizes full-process automation and intelligent management from resource evaluation, task scheduling to dynamic optimization by accurately sensing the performance and task characteristics of computing units and the running state of a heterogeneous computing cluster, and improves the scheduling efficiency compared with a traditional scheduling scheme. According to the scheduling system, the heterogeneous computing cluster resource utilization rate, the task execution efficiency and the system stability are remarkably improved, the energy consumption cost is effectively reduced, the scheduling system adapts to the cluster environment with diversified task loads and dynamic changes, and an efficient and reliable collaborative scheduling solution is provided for a large-scale heterogeneous computing scene.

The invention relates to a task scheduling optimization method and device based on reinforcement learning, equipment and a medium. The method comprises the steps that firstly, system resource state data are collected in real time, dynamic environment characteristics are determined through preprocessing and time sequence analysis, task characteristic data are analyzed at the same time, and a task priority sequence and a resource demand vector are generated through a priority ranking algorithm and a resource evaluation model; and then a state space and an action space are constructed by adopting a reinforcement learning algorithm, an optimal task allocation scheme is generated through strategy iteration and reward function optimization, and if the scheme meets a resource balance threshold, scheduling is executed, and performance indexes are collected. And finally, fusing real-time indexes with historical data, and updating parameters of the reinforcement learning model through experience playback and gradient descent to form a closed-loop optimized improved scheduling strategy. By adopting the method, the accurate mapping of the resource state and the task requirement can be realized, and the problem of insufficient adaptability of the traditional static scheduling to a complex scene is solved.

The invention discloses a garden landscape plant health assessment method, and belongs to the technical field of resource assessment. Comprising the following steps: step 1, constructing a time sequence database of environmental factors and plant growth states; step 2, according to different plant species and growth stages thereof, dynamically calculating and generating a health reference value adapted to the current environmental condition; step 3, in the optimal evaluation time window, performing preliminary evaluation on the plant health condition, extracting original health indexes, and performing correction in combination with an environment disturbance compensation factor; step 4, performing double comparative analysis on the corrected health assessment result, eliminating false anomalies caused by environmental fluctuation, and calculating an anomaly confirmation credibility index; step 5, grading the plant health conditions, visually displaying the plant health conditions in a thermodynamic diagram form, and marking the environmental interference degree; and step 6, based on an evaluation result, automatically generating a targeted maintenance suggestion, and designing a corresponding maintenance effect verification scheme.

The invention discloses a wind energy resource prediction method, system and device and a storage medium, and relates to the field of renewable energy sources, and the method comprises the steps: obtaining the three-dimensional wind field observation data of a target region; performing space-time alignment processing operation on the three-dimensional wind field observation data to generate a space-time sequence; performing multi-source data fusion on the space-time sequence, and constructing a multi-dimensional wind field matrix; establishing a wind energy potential prediction model based on the multi-dimensional wind field matrix; and predicting to-be-predicted wind energy data based on the wind energy potential prediction model to obtain a wind energy resource prediction result. And efficient, accurate and reliable resource evaluation is provided for wind energy development of various terrain areas by constructing a machine learning model.

The invention relates to the technical field of fishery management, in particular to an intelligent fishery supervision method and system based on multi-source data fusion, and the method comprises the steps: obtaining the multi-source trajectory data of a fishing boat, and carrying out the data fusion, and obtaining the precise trajectory data of the fishing boat; performing trajectory feature analysis, trajectory behavior pattern analysis and high-sensitivity point identification on the accurate trajectory data to obtain trajectory distribution of the fishing boat; obtaining multi-source fishery resource data and performing data fusion to obtain a fishery resource evaluation vector; the fishery resource prediction model processes the fishery resource evaluation vector to obtain fishery resource distribution; performing spatial coincidence analysis, time coincidence analysis and intensity coincidence analysis on the trajectory distribution of the fishing boats and the distribution of fishery resources to obtain analysis results; and an analysis result and management data are obtained and processed, and if abnormal behaviors exist, early warning is carried out.

The invention relates to the technical field of wind power generation, and discloses a cross-scale wind power plant wind resource evaluation method based on a numerical mode, comprising the following steps: step 1, acquiring multi-source observation data of a target area; step 2, screening a WRF optimal configuration scheme adaptive to the regional terrain; step 3, through a WRF power downscaling method, generating annual scale wind resource data as initial wind resource data; 4, performing partition correction on the initial wind resource data to obtain wind measurement data; step 5, constructing a WRF-WFP model coupling the WRF mode and the wind power plant parameterized model; step 6, optimizing WRF lower boundary conditions; 7, simulating the atmospheric flow of the target area, and quantifying the wake flow evolution characteristics of the wind power plant; 8, constructing a wake flow analysis model; and step 9, formulating a collaborative productivity plan of the wind power plant group. The power generation capacity of the wind power plant to be developed can be accurately predicted, and a reliable basis is provided for scientific decision-making of resource distribution development and planning of the million-kilowatt wind power plant.

The invention discloses a wind resource assessment report generation method based on a large model technology. The method comprises the following steps: processing multi-source data, and constructing a domain knowledge base and a fine tuning database; field adaptive RAG enhancement is carried out, professional literatures, historical cases and industry specifications in a field knowledge base are retrieved by adopting a retrieval enhancement generation technology, and wind resource assessment contents meeting technical standards are generated through a multi-modal fusion word vector technology; performing large model fine tuning, and optimizing the generative large model through a gradient-free optimization algorithm Wind-DFO on the basis of a fine tuning database by adopting a field self-adaptive fine tuning strategy; and automatically generating a report, arranging and matching multiple templates through an AI workflow, embedding a data visualization chart, executing verification of a grammar layer, a logic layer and a compliance layer, and outputting a standardized wind resource assessment report. The accuracy, specialty and efficiency of report generation are remarkably improved, and the problems that a traditional method depends on artificial experience, the data utilization efficiency is low, and standardization is insufficient are solved.

The invention discloses an elastic management and optimal scheduling method and system for cloud computing resources, and belongs to the technical field of cloud computing resource management.The method comprises the steps that a cluster composed of multiple types of intelligent agents is constructed, and the intelligent agents comprise a resource evaluation intelligent agent, an elastic telescopic intelligent agent, a load balancing intelligent agent and a fault recovery intelligent agent; the intelligent agents are distributed and deployed on different nodes of a cloud computing data center, and information interaction is realized through a communication interface based on RESTful API and a distributed message bus constructed by a gRPC protocol; a unified agent management platform is set up and is responsible for registration, monitoring, scheduling and dynamic adjustment of agents, and orderly operation and efficient cooperation of the whole multi-agent system are ensured. According to the method, the functions of efficient elastic allocation, intelligent load balancing, rapid fault recovery, continuous optimization evolution and the like of resources in the cloud computing data center can be realized, and the quality and competitiveness of cloud computing services are effectively improved.

The invention provides a computing power thermal evaluation method based on multi-dimensional data analysis and a decision making system thereof, and relates to the technical field of computing power analysis, and the method comprises the steps: obtaining an original data set, and carrying out the preprocessing of the original data set to generate a standardized data set; performing vectorization processing and heterogeneous fusion on the standardized data set to generate a unified fusion data set; a hierarchical evaluation system is constructed, correlation analysis is performed based on evaluation index features, and a computing power evaluation feature set is formed; different initial index weights are configured for different types of regions; calculating an initial calculation force thermal value of each region; performing iterative optimization on the initial index weight by using an optimization algorithm; and recalculating the regional computing power thermal value based on the optimized index weight, generating a thermal time-space distribution map, and generating a computing power resource allocation scheme according to the thermal gradient difference. According to the method, the systematicness and objectivity of computing power resource assessment are improved, and a reliable decision basis is provided for regional computing power resource allocation.

The invention discloses a resource optimization scheduling method for a high-performance parallel computing data center, and relates to the technical field of resource optimization scheduling, and the method comprises the steps: collecting multi-dimensional parameters of the high-performance parallel computing data center, and carrying out the preprocessing; calculating a comprehensive scheduling target index according to the preprocessed parameters; calculating a comprehensive resource scheduling index according to the comprehensive scheduling target index, setting the comprehensive resource scheduling index as a target function, defining a constraint condition, and constructing a multi-target optimization model; and solving the multi-objective optimization model according to a multi-objective optimization algorithm, generating a resource allocation scheme, and allocating tasks to corresponding resource nodes to complete resource optimization scheduling. The comprehensive resource scheduling index is constructed, and the optimal solution is efficiently searched in the complex high-dimensional solution space by using the multi-objective optimization algorithm, so that the comprehensiveness of resource evaluation, the construction efficiency of the scheduling index and the optimization quality and practicability of the resource allocation scheme are remarkably improved.

The invention provides a first-aid method and system based on 5G communication and an edge device. The method comprises the following steps: determining a communication protocol target adaptation protocol with a 5G base station based on the edge device; obtaining multi-modal data according to the target adaptation protocol based on the edge device, and performing priority classification on the multi-modal data to obtain a priority classification result; evaluating the 5G network resources of the edge device, determining a resource evaluation result, and determining a cloud transmission strategy based on the resource evaluation result and the priority classification result; performing association analysis on the vital sign data, the medical image data and the voice record data to obtain a calculation task and task complexity, processing the calculation task based on the task complexity and a cloud transmission strategy, and determining a data processing result; and data processing results are transmitted to the first-aid smart center and the mobile terminal in a grading manner. According to the method and the device, the compatibility problem caused by non-uniformity of device interfaces is solved, and the edge device processing capability is improved.

The invention discloses a marine fishery resource survey station adaptive optimization method based on a space-time coupling model. According to the method, a space-time continuous species distribution dynamic reference field is constructed by coupling a Bayesian hierarchical species distribution model and a regional ocean model system; performing ecological layering based on time, space and environment dimensions; and when the environment variables change significantly, a habitat suitability index is dynamically reconstructed, and the station layout is optimized again, so that the coverage of a key ecological hot spot area is ensured, and the in-gradient representativeness is maintained. The method has the advantages of fast environmental response, strong spatial representativeness, high adaptability and the like, and is suitable for scenes of resource assessment, ecological protection, biodiversity monitoring and the like.

The invention provides a deep reservoir physical property prediction and oil and gas resource evaluation system based on artificial intelligence. And the problems of physical property prediction and resource evaluation of low-porosity and low-permeability reservoirs in oil-gas exploration are solved. The system fuses earthquake, well drilling and well logging data, and generates a high-quality reservoir database through data cleaning, normalization and interpolation processing. A deep learning model is constructed by adopting a convolutional neural network and a long-short-term memory network, a nonlinear relationship between reservoir physical properties and multi-source data is automatically captured, and high-precision prediction is realized. The system is integrated with a real-time economic evaluation module, and an economic evaluation result is dynamically adjusted through a gradient enhanced tree model in combination with real-time market data. In addition, an automatic feedback mechanism is introduced into the system, and a region with a large error is re-sampled and the model is updated. According to the scheme, the reservoir prediction precision and exploration efficiency are remarkably improved, dynamic economic analysis is supported, and reliable technical guarantee is provided for exploration and development decisions of deep oil and gas resources.

The invention discloses a metal resource assessment method and system based on geological exploration, relates to the technical field of mineral data processing, and effectively solves key problems in geological resource assessment through multi-module collaboration. A data fusion module integrates drilling data Zk, geophysical data Dw and remote sensing data Yg; the confidence Rx is calculated by using an adaptive weighting algorithm and probability filling is carried out, so that the data sparsity error is remarkably reduced; the boundary optimization module is used for calculating a mineralization continuity index Lx and generating a boundary optimization coefficient Ly in combination with the temperature dynamic factor Tx so as to realize intelligent correction of the boundary of the ore body; the depth correction module constructs a depth compensation model through the signal intensity M, the stability V and the noise level N, the deep data credibility Sx is improved, Monte Carlo simulation and dynamic parameter adjustment are matched, the system overcomes the defects of a traditional method in the aspects of data uncertainty, boundary ambiguity and deep precision, and the method has the advantages of being high in accuracy and high in reliability. And full-process automation and high-precision resource evaluation and early warning are realized.

The invention relates to the technical field of terrain wind resource assessment, in particular to a complex terrain wind resource assessment method and system based on CFD enhancement, and the method comprises the steps: processing terrain, weather and vegetation data, and generating a complex terrain feature vector; constructing bidirectional interaction between a mesoscale meteorological mode and computational fluid dynamics, and iteratively optimizing a flow field; fluid dynamic parameters are calculated through reinforcement learning automatic optimization; turbulence simulation is enhanced by combining the generative adversarial network with large eddy simulation; dynamically allocating resolution according to the grid priority of the multi-feature calculation; the lightweight model pre-judges errors, and after the errors reach the standard, evaluation indexes are calculated to complete wind resource evaluation; the system comprises a multi-source data fusion module, a dynamic coupling module, a parameter optimization module, a reinforced turbulence simulation module, an adaptive grid module and an error prediction and evaluation module, and all the modules cooperate to achieve full-process evaluation. The problems that in the prior art, one-way coupling precision is low, manual parameter selection is low in efficiency, turbulence simulation precision and efficiency are difficult to balance, and static grids are wasted are solved.

The invention provides a three-dimensional visualization expression method for a multi-scale geological structure model, including: collecting geological exploration data which includes geophysical information, drilling information, outcrop survey information, seismic information and geological age data; performing interpolation simulation on the geological exploration data to obtain an exploration sample set; preprocessing the exploration sample set to obtain preprocessed data, the preprocessing process including data cleaning, format unification and noise removal; constructing a multi-scale geological structure model according to the preprocessed data based on a three-dimensional modeling technology; performing scale decomposition on the geological structure model using a multi-scale analysis method to form model representations at different scale levels; and converting the model representations into 3D visualization images using a 3D graphics processing technology. The invention can effectively improve the expression ability of geological model and user experience, and has important practical application value for geological exploration and resource assessment.

The invention discloses a collaborative planning method for a regional integrated energy system, and the method comprises the steps: firstly collecting regional historical energy consumption data and various external factor data, and predicting an energy demand through employing a gray prediction and multiple linear regression combined algorithm; exploring existing energy supply facilities in the region and evaluating renewable energy potential; determining a candidate type of the integrated energy system component according to the result, establishing an optimal configuration model, and solving through a particle swarm optimization algorithm; constructing an energy network layout planning model, and determining an optimal topological structure and parameters of the network; establishing a dynamic operation model, and formulating a collaborative operation strategy by using a model prediction control algorithm; and finally, comprehensively evaluating the planning scheme, and analyzing and adjusting the scheme by utilizing sensitivity according to an evaluation result. According to the method, multiple factors are comprehensively considered, and the problems that in existing regional integrated energy system planning, demand prediction is not accurate, resource evaluation is not comprehensive, component configuration and network layout lack optimization collaboration, and the operation strategy is single are effectively solved.

The invention provides an emergency resource intelligent scheduling optimization method and system based on large model analysis, and relates to the technical field of emergency resource scheduling. The method comprises the steps of collecting emergency event data and environment data; extracting emergency event features and environment features, constructing a prediction model based on a long short-term memory network, and obtaining a required emergency resource list; constructing an emergency resource evaluation model based on an analytic hierarchy process, and obtaining priority scores of the required emergency resources; constructing a multi-objective optimization model, and solving the multi-objective optimization model by using a particle swarm algorithm to generate a scheduling scheme; and constructing a decision function, and solving by using a genetic algorithm to obtain a scheduling decision. According to the method, through accurate prediction, scientific evaluation, multi-objective optimization and decision in combination with priority, the accuracy and efficiency of emergency resource scheduling and the resource utilization efficiency can be remarkably improved, powerful support is provided for emergency management work, and the method has important practical significance and application value.

The invention discloses a multi-dimensional fusion medical resource intelligent monitoring and early warning method. According to the method and the device, the integrity and the accuracy of the medical resource data are improved through a multi-dimensional data fusion and dynamic calibration mechanism. Multi-source data of a medical institution system, a regional health platform, wearable equipment and the like are integrated, comprehensive data coverage and timely updating are ensured, and resource monitoring blind areas caused by information islands are reduced. Through the processing flows of noise filtering, missing value filling, abnormal value identification and the like, the influence of data errors on resource evaluation is reduced, quantitative analysis of key indexes such as medical resource supply and patient demands is closer to the actual situation, and the timeliness of medical resource risk early warning and the effectiveness of decision support are enhanced. The hierarchical early warning model constructed based on the multi-modal feature matrix can identify potential risks from multiple dimensions of resource exhaustion, service saturation, regional propagation and the like, and ensures reasonable distribution and efficient utilization of medical resources in different scenes.

The invention provides a power centralized control center data verification and backup method and device, and the method comprises the steps: obtaining hardware resource information corresponding to an operating system, and constructing a thread resource pool; the method comprises the following steps: connecting data sources of an electric power centralized control center, classifying and rating the data sources, and carrying out real-time demand analysis by adopting a parameterized mixed time algorithm to obtain a data task queue with a priority mark; monitoring the load state of an operating system and the length of a task queue, performing resource evaluation by applying an online distributed queue length estimation algorithm, dynamically creating a working thread and allocating resources; based on a thread resource allocation scheme, executing parallel data verification by adopting a sub-exponential algorithm on an independent set; and evaluating a backup demand according to the data verification result set, determining a backup strategy, and executing parallel data backup. According to the method, the characteristics of a domestic operating system are fully utilized, efficient verification and backup of the data of the electric power centralized control center are achieved, and the system resource utilization rate and the data safety are improved.

The invention discloses a CFD (computational fluid dynamics) analogue simulation-based wind power plant wind resource evaluation method and a CFD analogue simulation-based wind power plant wind resource evaluation device. The evaluation method comprises the following steps: collecting and processing landform and meteorological data of a wind power plant site; establishing a three-dimensional CFD simulation model of the wind power plant according to the processed landform and meteorological data of the wind power plant site, performing grid division on a wind power plant area, and setting simulation boundary conditions; performing numerical simulation on the three-dimensional CFD simulation model of the wind power plant, and analyzing wind speed field distribution information of each position in the wind power plant; and according to the wind speed distribution information, calculating and evaluating the wind resource condition of the wind power plant to obtain a wind resource evaluation result. According to the technical scheme, through the three-dimensional simulation-evaluation-optimization closed loop with the CFD as the core, the wind speed and turbulence can be accurately quantified in the design stage, so that unit model selection, tower height, blade length and machine position layout are matched with a real wind field, key parameters can be determined at a time, the design period can be shortened, and the operation and maintenance and grid connection cost can be reduced.

The invention provides a task scheduling method applied to a hybrid cloud environment. The method solves a plurality of problems in the prior art. Firstly, through comprehensive demand analysis and resource evaluation, the target and constraint conditions of task scheduling can be clarified, and blindness and randomness in the scheduling process are avoided. And secondly, through task classification and scheduling strategy formulation, optimal configuration of resources and efficient execution of tasks can be realized, and the stability and performance of the system are improved. In addition, by establishing a task scheduling model and designing a mathematical formula, the task scheduling process can be described more accurately, and the scheduling accuracy and efficiency are improved. And finally, through performance evaluation and optimization steps, problems can be found and solved in time, and the performance and effect of task scheduling are continuously improved.

The invention discloses a soil attribute intelligent prediction method and system based on a multi-dimensional dynamic scale, and relates to the technical field of soil prediction.The method comprises the steps that remote sensing images, terrain factors, meteorological factors and soil actual measurement sample data are collected and stored in a multi-source raster data bin in a unified mode; preprocessing the multi-source data, and constructing raster data in a unified format; combining terrain difference, land utilization and climate fluctuation, dynamically dividing a spatial scale, a time scale, a terrain layering scale and a land utilization pattern scale, and constructing a multi-dimensional dynamic scale feature expression vector; carrying out soil attribute modeling and training based on a multi-task deep neural network model; and applying the model to a non-actually-measured area, and outputting a prediction result and a spatial distribution map layer. The system comprises a multi-source raster data construction and management module, a multi-dimensional dynamic scale feature construction module and a soil attribute modeling and prediction module. The method can be used for forestry management and protection, resource management, ecological monitoring, resource evaluation and other scenes.

The invention discloses a fishery sea fishing resource assessment method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining multi-dimensional data information of a to-be-assessed target sea area; determining a plurality of evaluation factors in the data information of each dimension; constructing a hierarchical structure model of fishery and sea fishing resource evaluation based on the multi-dimensional data information and the corresponding evaluation factors; based on the hierarchical structure model, determining the data information of each dimension and the index weight of the corresponding evaluation factor through an analytic hierarchy process; constructing a fuzzy relation matrix through fuzzy mapping representation based on the evaluation factor in combination with a preset comment set; performing fuzzy synthesis operation according to the index weight and the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result; and determining a final evaluation result of the target sea area based on the fuzzy comprehensive evaluation result. The method can accurately realize fishery sea fishing resource evaluation, and can be widely applied to the technical field of fishery resource evaluation.

The invention discloses a service resource automatic migration method, system and device and a medium, and relates to the technical field of service resource migration, and the method comprises the steps: carrying out the evaluation of service resources of different platforms in a company through a resource evaluation model, determining the use conditions corresponding to different types of service resources, determining whether migration is carried out or not based on the use condition corresponding to any type of service resources in any platform; and when a determination result is yes, determining an optimal migration platform based on the use condition of the same type of service resources of other platforms except the platform, and completing migration processing based on the optimal migration platform. According to the method, all types of service resources in all platforms are evaluated, so that the service resource use condition in a company can be accurately and comprehensively controlled, and the service resources can be distributed more reasonably by carrying out subsequent service resource migration processing on the basis of the service resource use condition.

The invention relates to the field of power, and provides a distributed resource evaluation and aggregation method and device for a virtual power plant and electronic equipment, and the method comprises the steps: dynamically calculating the adjustment capability score and the close degree of each distributed resource according to the adjustment capability index of each distributed resource of the virtual power plant; based on the adjustment capability score and the close degree of each distributed resource, selecting a distributed resource meeting a threshold condition from each distributed resource to participate in aggregation scheduling; and optimizing the runnable boundary of each distributed resource participating in aggregation scheduling based on the power of each distributed resource participating in aggregation scheduling and a pre-established double-layer optimization model. According to the method, efficient aggregation and operation boundary optimization of distributed energy resources by the virtual power plant can be realized through a dynamic scoring mechanism and a hierarchical optimization architecture, and the method has the advantages of high efficiency, flexibility and accuracy.

The invention provides a wind power plant wind power resource evaluation method and system, relates to the technical field of wind power resource evaluation, and aims to realize higher-reliability and more comprehensive wind power resource evaluation. The method comprises the following steps: performing wind power data acquisition at a plurality of time points in the detection time of a wind power plant, obtaining a wind power initial data set; preprocessing the wind power initial data set; performing data analysis on the wind power initial data set to obtain a wind power characteristic data set which comprises regional wind speed probability distribution, regional wind direction probability distribution and wind energy density distribution at a plurality of time points; and performing wind power resource quality evaluation based on the wind power characteristic data set. The method has the advantage that the resource evaluation of the wind power of the wind power plant is more efficient, comprehensive and accurate.

The invention provides a K8S-based satellite image recognition resource dynamic elastic scheduling system. The system comprises a task perception module, a resource evaluation module, an elastic scheduling module, a fault tolerance module and a learning optimization module. The task sensing module collects satellite image recognition task metadata and transfers the metadata to a priority label library; the resource evaluation module monitors node resources, quantifies the health degree and screens healthy nodes into a candidate pool; the elastic scheduling module executes Pod dynamic capacity expansion and contraction and other decisions according to tasks and resources; the fault-tolerant module identifies the fault Pod, reconstructs and distributes a new Pod according to an anti-affinity rule; the learning optimization module optimizes the system parameters according to the historical data. The invention also provides a dynamic elastic scheduling method. Therefore, the resource utilization rate and the system stability of the satellite image recognition task are remarkably improved.