281 results about "Resource planning" patented technology

The invention discloses an interference resource planning method based on a knowledge graph and space-time diagram convolutional neural network. A head entity, a relation and a tail entity gt; constructing an interference pattern decision knowledge graph in a triple form; constructing an interference pattern decision knowledge graph embedding representation model, and generating embedding features representing semantic association; constructing interference pattern decision knowledge fusion based on a multi-layer perceptron, and combining attribute characteristics of interference pattern decision with a historical space-time network interference efficiency evaluation sequence; constructing a space-time diagram convolutional neural network to model embedded features, extracting space correlation characteristics between nodes, capturing a time evolution rule of node attributes, and dynamically predicting interference efficiency; and reasoning an optimal interference pattern in combination with the interference efficiency prediction result and the real-time situation information of the target equipment. The method can effectively predict the interference efficiency evaluation of different interference devices, significantly reduces the prediction error of the interference efficiency evaluation, and improves the prediction precision and model stability.

The invention discloses an automatic resource planning method, system and device for hybrid clouds and a medium, and the method specifically comprises the steps: determining a business change trend through employing a time sequence analysis algorithm, and obtaining a demand prediction model; obtaining dynamic resource demand parameters from the demand prediction model, judging a resource allocation proportion of the private cloud and the public cloud, and determining an initial configuration scheme of a cross-cloud resource pool; based on the initial configuration scheme, a reinforcement learning algorithm is adopted to optimize resource scheduling dynamics, and a real-time scheduling plan is obtained; specific parameters of resource allocation are extracted from the real-time scheduling plan, whether the requirements for safety and flexibility are met or not is judged, and a final scheduling instruction is determined; and based on the final scheduling instruction, generating a configuration script and an automatic deployment file for the cross-cloud environment by adopting a preset template engine. According to the method, the hybrid cloud resource delivery efficiency and the resource utilization rate are improved, and deepening and breakthrough of the hybrid cloud technology in enterprise application are promoted.

The invention relates to the technical field of simulation scheduling combining static scheduling and centralized scheduling, and discloses a simulation scheduling method and system combining static scheduling and centralized scheduling. According to the simulation scheduling method and system combining the static scheduling and the centralized scheduling, pre-allocation and resource planning of tasks are carried out through the static scheduling, and the execution efficiency of periodic tasks is ensured. In combination with centralized scheduling, resource allocation is adjusted in real time in the simulation operation process, the phenomena of resource idleness and overload are reduced to the maximum extent, and dynamic optimization of resources is achieved. The execution sequence of key tasks is locked in advance by static scheduling, and the time constraint of deterministic tasks is ensured. The centralized scheduling can perform global coordination on burst tasks, adjust task priorities in real time and ensure timely response of the burst tasks, so that the real-time response capability of the simulation system is improved.

The invention relates to an improved NSGA-III-based multi-target geographic site selection optimization method and system, belongs to the technical field of optimization in business management and resource planning, and particularly relates to resource allocation and site selection decision making by using a calculation model. The technology aims to solve the problems of slow scheme convergence, non-uniform solution set distribution and low calculation efficiency when multi-target site selection is carried out under limited resources. According to the scheme, geographic space data is preprocessed, and an initial population representing different site selection schemes is generated; carrying out optimization iteration by adopting an improved NSGA-III algorithm, and calculating a plurality of objective functions such as coverage rate, normalized population density and medical service accessibility; and performing individual selection and population updating by utilizing improved reference point generation and vertical distance measurement. The method is mainly used for improving the efficiency and effect of commercial and municipal decisions such as urban planning, medical resource configuration, energy station layout and logistics network optimization.

The present disclosure provides a resilient (radio) access network ((R)AN) slicing framework encompassing a resource planning engine and distributed dynamic slice-aware scheduling modules at one or more network access nodes, edge compute nodes, or cloud computing service. The resilient (R)AN slicing framework includes resource planning and slice-aware scheduling, as well as signaling exchanges for provisioning resilient (R)AN slicing. The intelligent (R)AN slicing framework can realize resource isolation in a more efficient and agile manner than existing network slicing technologies. Other embodiments may be described and / or claimed.

The invention provides a multi-satellite parallel data transmission resource scheduling method based on a genetic algorithm, and belongs to the technical field of ground task management and control of spaceflight earth observation satellites. The method comprises the steps that historical data transmission resource use rules of all satellites are mined, and data transmission resource use rule models of all the satellites are established; in a data transmission resource scheduling process, each satellite generates a corresponding resource demand set according to a data transmission timeliness requirement, a priority degree and a data transmission resource use rule model of an important task, conflict resolution is carried out on a conflicting resource use demand set by utilizing a genetic algorithm, and a data transmission resource scheduling result is obtained through a multi-round parallel resource matching and conflict resolution process. The multi-satellite data transmission resource planning scheme is obtained, large-scale satellite data transmission resource scheduling is achieved, the large-scale remote sensing satellite group resource use efficiency is effectively improved, and the implementation rate of reconnaissance tasks is improved. According to the method, the data transmission resource distribution income among multiple satellites can be comprehensively planned, and the intelligent scheduling capability level of large-scale remote sensing satellite group resources can be effectively improved.

The invention discloses an intelligent optimization system for enterprise resource planning, and relates to the technical field of enterprise resource management, and the system comprises a data collection and preprocessing layer, a knowledge construction and reasoning layer, a risk early warning and decision support layer, and a resource scheduling and execution layer. According to the invention, a knowledge graph covering various resources of an enterprise and association relationships thereof is constructed by using preprocessed data through a knowledge construction and reasoning layer, a causal relationship network among the data is established, and meanwhile, an intervention effect evaluation unit combines an anti-factual reasoning result of a causal reasoning unit to evaluate the intervention effect of the enterprise. Quantitative evaluation is carried out on different resource pre-allocation intervention schemes, and a detailed evaluation report is output, so that an enterprise can perceive and take corresponding measures in advance before a risk occurs, and can select an optimal scheme according to a scientific evaluation result when a resource planning decision is made, thereby effectively ensuring stable operation of enterprise resources, and improving the resource planning efficiency. And the operation risk caused by the resource problem is reduced.

The invention provides an electric vehicle charging resource planning and optimizing method, and aims to solve the problems of static performance, single target optimization, one-sided attraction evaluation, disjunction with traffic jam management and the like in existing planning. The method comprises the following steps: firstly, acquiring regional basic data, and establishing a charging station composite attraction evaluation model which comprehensively considers POI value, plot cost, estimated available charging pile quantity proportion and reputation parameters determined based on historical service data, so as to predict the probability of selecting each charging station by a user; and optimizing charging pile capacity configuration according to the predicted user selection probability and the total charging demand so as to minimize the combined cost of the construction cost and the average queuing waiting time of the users. The optimal charging site is screened and recommended through weighted aggregation of multiple indexes such as composite attraction, construction cost, queuing waiting time and the like. Scientific site selection and reasonable capacity configuration of the charging stations are achieved, urban traffic congestion is effectively relieved, balanced distribution of the charging flow is promoted, and the charging experience of users is improved.

The invention discloses a human resource intelligent planning method and system based on big data analysis, and relates to the technical field of human resource management, and the method comprises the steps: collecting the work performance data and eye movement tracking data of employees in a simulation training environment; skill labels of the employees are extracted by using a natural language processing technology, a personal skill map is constructed, and advantages and shortages of the employees are analyzed; evaluating the ability level of the employee according to the personal skill map and the work performance data; according to the skill label of the employee, combining the ability evaluation result of the employee, intelligently matching a proper post, and planning the occupational path of the employee; a personalized training plan is intelligently generated according to the skill weakness and post requirements of the employees; the comprehensive human resource intelligent planning method is constructed by integrating big data analysis and natural language processing technologies, and many problems in the prior art can be effectively solved.

The invention discloses an adaptability evaluation method and device for ecological restoration of karst plants, and belongs to the technical field of resource planning, and the method comprises the steps: inputting leaf parameters and root system parameters into a data table, carrying out the normality test of all indexes, and selecting a corresponding standardization method for data conversion according to the types of the indexes; based on the standardized data, identifying index correlation through principal component analysis, constructing a judgment matrix, calculating a feature value and a feature vector, and determining a weight coefficient of each evaluation index; calculating a comprehensive evaluation index of the sample plant by using the weight coefficient and the standardized data, dividing grades according to quartile, drawing a spatial distribution diagram and analyzing distribution characteristics; and establishing a multi-constraint integer programming model according to the evaluation grade and the spatial distribution characteristics so as to solve and determine an optimal configuration scheme through an algorithm. Through the scheme of the invention, a multi-dimensional evaluation system of ecological restoration resources can be constructed.

The invention discloses a cloud resource planning method based on time sequence prediction. The method comprises the following steps of collecting cloud resource data and performing storage processing to form time sequence data; preprocessing the time sequence data according to a target to obtain processed data; constructing a training sample set based on the processing data; constructing a time sequence prediction neural network model ScaleMixer, and training the time sequence prediction neural network model ScaleMixer based on the training sample set to obtain a trend prediction model; performing analysis based on a prediction result output by the trend prediction model in combination with actual cloud resource data to obtain an analysis result; and generating a cloud resource planning report based on the analysis result and the historical cloud resource data, and planning the cloud resources. Multi-dimensional accurate prediction of future cloud resource demands is realized, and a self-adaptive cloud resource allocation strategy is generated in combination with demand scene features, so that resource supply and demand fluctuation are effectively balanced.

The invention relates to the technical field of power grid resource planning, and discloses a power grid resource planning method and system based on a voltage weak region, and the method comprises the steps: obtaining a branch load flow calculation result according to the topological structure data and real-time load data of a power grid, calculating the current overload coefficient of a branch, and determining a heavy load branch; according to the voltage amplitude deviation of the load node of the heavy load branch, potential weak points are determined and aggregated to obtain a weak area, and the voltage stability margin of the weak area is calculated; obtaining a risk index of each potential weak point, calculating a risk level of the potential weak point according to the voltage stability margin and the risk index, and screening out a first weak point; calculating a resource matching degree between the regulation and control resource and the first weak point; and according to the risk level and the resource matching degree, establishing a resource planning model to obtain a resource planning scheme. According to the invention, through multi-dimensional risk identification, accurate resource matching and multi-scene strategy optimization, the accuracy and dynamic adaptability of resource planning are improved.

The invention discloses a large-scale complex correlation earth observation task planning method based on reinforcement learning, and relates to the technical field of task resource planning. The method comprises the following steps: acquiring historical data of earth observation task planning, and constructing a sample data set; constructing a graph neural network and a reinforcement learning network; using the sample data set to train the graph neural network and the reinforcement learning network at the same time; acquiring a demand information list, a satellite information list, a data transmission task information list and an observation information list, and processing the information lists into demand feature vectors, satellite feature vectors, data transmission task feature vectors and observation feature vectors; and inputting the four feature vectors into a trained graph neural network to obtain an environment feature vector, inputting the environment feature vector and the demand feature vector into a trained reinforcement learning network, and outputting an earth observation task planning result. The method can solve the scheduling optimization problem under the condition of multiple tasks and limited resources, improves the task planning efficiency, and can be widely applied to the fields of satellite task scheduling, intelligent manufacturing, automatic driving and the like.

The invention provides a natural resource planning text generation method and system and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: building a grid partition unit in a feature space, and executing spatial clustering according to the feature vector distribution density; mapping the feature vectors to corresponding partition units, and generating a dynamic material pool containing spatial correlation attributes; generating a spatial reference outline of an outline structure based on the spatial association attributes; according to the spatial reference outline, positioning the content of the material pool by adopting a segmented writing strategy, and generating text paragraphs based on the feature weights of the partition units; performing structured processing on the text paragraph to obtain a text after structured processing; and executing dynamic verification on the text after the structured processing so as to output a final planning text. According to the spatial clustering mapping, the processing amount of redundant data is reduced, and computing resources are reduced.

The invention discloses an electric power system flexible resource planning method, system and device considering multi-time scale extreme weather and a storage medium, and relates to the technical field of electric power system planning, and the method comprises the steps: generating a multi-time scale extreme weather scene based on a conditional diffusion model; constructing a flexible resource planning model of the mixed time scale power system; and calling a Gurobi solver by using a Yalmip toolkit of Matlab to solve the flexible resource planning model of the mixed time scale power system, and outputting an optimal planning scheme of flexible resources of the power system. According to the method, the multi-day extreme weather scene is generated based on the conditional diffusion model, spatial-temporal characteristics of multi-day extreme weather such as typhoon and high temperature can be accurately described, and compared with other methods, a diversified scene set with more statistical coverage can be reconstructed under limited historical extreme data; and a multi-time-scale operation scene basis with typicality and extreme property is provided for power system planning.

The invention discloses a resource management method and related equipment for an air-space-ground integrated Internet of Things system, and the method comprises the steps: making a resource planning decision at a first time scale, and generating a long-term scheme; and performing a resource allocation decision based on the long-term scheme at a second time scale far less than the first time scale to generate a short-term scheme. Then, a system overall user experience quality model is constructed according to the long-term and short-term schemes, and the model fuses user experience quality models of calculation-intensive and data transmission tasks; then, with the purpose of maximizing the overall user experience quality, a resource management optimization problem containing long-term and short-term scheme variables is constructed; and finally, solving and outputting an optimal scheme. The invention aims to solve the problems of low efficiency and poor adaptability of current resource management, especially the situation that in the prior art, when diversified requirements and complex constraints are dealt with, calculation bottlenecks exist, and resources are difficult to distribute finely.

The present disclosure provides a resilient (radio) access network ((R)AN) slicing framework encompassing a resource planning engine and distributed dynamic slice-aware scheduling modules at one or more network access nodes, edge compute nodes, or cloud computing service. The resilient (R)AN slicing framework includes resource planning and slice-aware scheduling, as well as signaling exchanges for provisioning resilient (R)AN slicing. The intelligent (R)AN slicing framework can realize resource isolation in a more efficient and agile manner than existing network slicing technologies. Other embodiments may be described.

The invention belongs to the technical field of resource planning, and particularly relates to a power distribution network distributed resource robust planning method and device based on cluster division, and the method comprises the steps: building a source-storage robust locating and sizing model as an upper model based on cluster division, and setting a target function and constraint conditions thereof; based on cluster planning, constructing an intra-cluster reliability index calculation model as a lower-layer model, and setting a target function; combining the upper-layer model and the lower-layer model to obtain a double-layer joint planning model; aiming at an island operation scene under a cluster division condition, designing a reliability merging optimization method integrated with island operation characteristics, and optimizing the double-layer joint planning model to obtain an analytical expression of a reliability index; and obtaining a joint planning scheme of the distributed resources of the power distribution network by solving the optimized double-layer joint planning model. According to the method, the source load uncertainty and the reliability index are effectively considered, and the economical efficiency, the flexibility and the reliability of power distribution network planning are remarkably improved.

The invention provides an urban water resource planning method and system, and the method comprises the steps: constructing an interval fuzzy multi-layer planning model, and setting an objective function of each layer in the multi-layer planning model and a corresponding constraint condition; setting the minimum satisfaction of each layer, calculating the maximum and minimum objective function values based on the constraint of the decision variable of each layer, and substituting the maximum and minimum objective function values into the corresponding membership function to obtain the satisfaction of each layer; the satisfaction degree of each layer is obtained; respectively calculating the satisfaction stability of each layer according to the satisfaction of each layer; and if the satisfaction stability of each layer is within a preset interval, obtaining a decision variable value corresponding to the maximum total satisfaction as a water resource planning value of each layer. By means of the scheme, urban water resource allocation can be optimized, and reasonability and reliability of water resource allocation are guaranteed.

The invention relates to the technical field of territorial resource planning, in particular to a territorial space resource planning system based on big data, which comprises a space-time diagram modeling module for summarizing land utilization data, extracting a land utilization type of each geographic unit, embedding type data into space-time diagram nodes, and calculating a space-time dependency relationship among the nodes; and generating a space-time diagram adjacency matrix. According to the method, space-time dependency between nodes is modeled through space-time diagram processing, complexity of space and time changes is accurately captured, and the system can effectively recognize short-term and long-term change features by combining differentiated time windows and small and large window convolution kernels, so that prediction of land utilization changes is more flexible, and the prediction accuracy is improved. The spatial adjacency matrix is further adjusted through multi-scale spatial heterogeneity modeling, the mutual relation between the areas is accurately described, the precision of spatial dependency calculation is improved, data trends of different time steps are subjected to weighted fusion, land utilization changes are effectively predicted, and more accurate reference is provided for decision making.

The invention provides an integrated intelligent management system for energy storage equipment production management. The system integrates a pre-incoming process management module based on an ERP system and a post-incoming production execution module based on an MES system. The system realizes efficient production management through a plurality of interaction modules, and comprises a purchase module, a production scheduling system, an SN management system and a production process adjustment module. The ERP system and the MES system are fused, bidirectional synchronization of information and data is kept, consistency of production execution and resource planning is fully guaranteed, and traceability and visualization of the whole process of products, materials and equipment are achieved; through the function modules, the whole production process is orderly carried out in a planned and filing manner under reasonable monitoring, the production efficiency, the material management precision and the product quality are improved, and the production cost and risk are reduced.

The invention provides a resource information determination method and device, equipment and a storage medium, relates to the field of human resources, and is used for determining reserve resource information of a main body object of an institution and providing reliable data support for the institution to formulate long-term and accurate resource planning. The specific scheme is as follows; the method comprises the following steps: quantifying historical business processing data of a subject object to obtain an efficiency index, and fitting based on the quantified efficiency index to obtain a first growth rate of efficiency index growth of the subject object; a target senescence factor coefficient is introduced to correct the first growth rate to obtain a second growth rate which reflects the law of efficiency growth slowing of the subject object after the target time window; combining the first growth rate, the second growth rate and the target time window to generate an efficiency growth curve, and representing the change of the efficiency of the main object along with time; and finally, determining resource information of the mechanism in the target time window based on the efficiency growth curve of each main body object.

The invention belongs to the technical field of power system operation control, and discloses a power distribution network flexibility resource dynamic aggregation and finite time hierarchical optimization control method, which comprises the following steps: constructing a power distribution network distributed resource cluster model, clustering clusters based on different clustering indexes, analyzing the adjustable potential of each cluster, and establishing a distributed resource cluster model; calculating and quantifying scale parameters of the cluster; performing dynamic aggregation according to complementarity of different resource clusters by applying a panoramic theory; performing hierarchical optimization on the cluster according to an aggregation result, constructing an objective function and a constraint condition, and solving a hierarchical optimization model by adopting a particle swarm algorithm; a performance algorithm is preset through finite time, dynamic parameters are introduced into a particle swarm algorithm, dynamic constraint is carried out on system state errors, decision time is further shortened, and control performance is improved. According to the method, accurate clustering of different distributed resources can be realized, the cluster aggregation optimization operation efficiency of the power distribution network is effectively improved, and flexible resource planning is realized.

The invention discloses a coastal port shoreline resource planning and utilization method based on a rigid-elastic-tough idea, and the method comprises the steps: collecting and preprocessing the resource endowment data of a port shoreline, and building a shoreline space attribute database; data analysis is performed through multi-source spatial data integration and a GIS spatial analysis technology, and rigid constraint management and control are performed; elastic adaptation planning is performed in combination with the port-approaching industrial demand and the urban life coordination degree; carrying out toughness optimization on the key transportation goods; and constructing an intelligent decision-making and full-period management and control platform, carrying out port shoreline planning full-process digital and refined management, and obtaining a final coastal port shoreline resource planning result. According to the method, the limitation of traditional specifications is broken through, the problem of collaborative optimization is solved, full-period management and control are realized, and the resource utilization efficiency is improved.

The invention provides an integrated energy system resource robust planning method considering the low-carbon benefit of a new energy automobile, and the method comprises the steps: constructing an integrated energy system frame on the basis of analyzing the electricity-heat-cold-hydrogen energy conversion, flow and equipment operation characteristics; the new energy automobile is researched to replace a fuel automobile to run, the energy charging requirement of the new energy automobile is simulated, and a new energy automobile carbon-green certificate quota model is constructed; the uncertainty of new energy output and multi-energy load requirements is considered, and a min-max-min two-stage robust planning model is established by taking system planning economy optimization as a target. According to the comprehensive energy system resource robust planning method considering the low-carbon benefit of the new energy automobile provided by the invention, the IES resource robust planning is realized while the system planning-operation economy is optimized.

The invention discloses a test sequence optimization method and device, equipment and a storage medium. Comprising the steps of obtaining function point related data, and generating a function point test sequence based on an upper confidence bound algorithm; defining a global resource occupancy matrix, and according to the function point test sequence, sequentially importing the function points, traversing and querying the global resource occupancy matrix, and generating a parallel scheduling task; and executing the parallel scheduling task, and monitoring resource conflict conditions and test failure information in real time. By comprehensively considering related data of the function points, intelligent sorting of the function points is achieved, and the problems that in a traditional sorting method, exploration and utilization are difficult to balance and global dynamic optimization is lacked are solved. By reasonably distributing parallel tasks, resource conflicts are avoided, the utilization rate of limited test resources is improved, and efficiency reduction caused by unreasonable resource planning is reduced. Resource conflicts are found and processed in time through real-time monitoring, the conflicts are prevented from influencing the test process, test failures can be quickly responded, and the test efficiency is improved.

The present invention relates to a system (1) which enables network resource planning by estimating the potential mobility of network users, the persons with whom they communicate or establish relationships and the risky areas by performing location risk analysis, relationship forecasting, and user / subscriber mobility analysis in order to ensure the continuity and efficiency of the communication infrastructure in cases such as disinformation, disaster.

The invention discloses a novel power system multi-element flexibility resource planning method in extreme weather, and the method comprises the steps: building a flexibility resource model of a novel power system in conventional weather, and the flexibility resource model comprises a power side flexibility resource model, a load side flexibility resource model and an energy storage side flexibility resource model; on the basis, establishing a flexibility resource mathematical model of the novel power system in extreme weather; extreme weather is divided into environment type and disaster type, the standby thermal power generating unit is added to the environment type extreme weather, and the mobile energy storage vehicle is added to the disaster type extreme weather; the method comprises the following steps: establishing a flexibility resource planning model in extreme weather by taking total cost minimization as a target function, and setting investment constraint conditions and operation constraint conditions, and performing linearization processing and solving on the flexibility resource planning model in the extreme weather to obtain a multi-element flexibility resource planning result of the novel power system in the extreme weather. According to the invention, the flexibility and economy of the novel power system in extreme weather can be effectively improved.

A dynamic AI-based resource planning system and calculator for optimizing fieldwork in renewable energy construction projects, the system comprising: (a) a data collection module configured to collect real-time and historical data from sources such as worker attendance records, weather data, equipment usage logs and project management software; b) a predictive analytics module configured to process the data using machine learning algorithms to predict work needs, potential project delays, and task prioritization; (c) a resource allocation module configured to dynamically allocate on-site workers to tasks based on forecast results, worker availability, skill levels and project schedules; (d) an interactive planning interface suitable for the visualisation, modification and simulation of personnel plans and project activities in real time; (e) a Smart Operational Resource Dashboard (SORD) configured to monitor and display key performance indicators, detect anomalies, and issue automatic alerts; and f) a reporting module configured to generate work, productivity and compliance reports, The system continuously adjusts labor deployment in response to changing on-site conditions to improve work efficiency and project results.

The invention discloses a carbon footprint full-link acquisition and trusted accounting system based on artificial intelligence, which relates to the technical field of carbon emission data processing, and comprises the steps of collecting original records from multi-source data such as resource planning, manufacturing execution, energy consumption metering, logistics and computing power operation, cleaning and normalizing, uniformly generating an accounting event, and merging the accounting event into an additional event account book; the evidence file enters a content addressing evidence library, and a consistency score write-back event is calculated based on cross-source constraint; solidifying a rule-factor-algorithm-parameter combined snapshot number, calculating a risk score, arranging, checking and sampling, carrying out closed loop on a minimum evidence list and a problem, and completing snapshot accounting; and constructing a full-amount commitment root, generating a verifiable minimum disclosure evidence packet according to sampling, and performing additional correction and versioning updating according to check feedback. Therefore, results can be traced, recalculated and checked, the evidence obtaining disclosure range is reduced, and mismatching and tampering risks are reduced.