237 results about "Resource planning" patented technology

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 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 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 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 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.

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.

The invention provides a collaborative management system and method for an EDA all-in-one machine, the collaborative management system comprises a business layer, an application layer, a platform layer and a hardware layer, the hardware layer is used for providing hardware resources and deploying container instances; the platform layer is used for performing instance management, resource quota allocation and instance life cycle control of container instances on the hardware resources provided by the hardware layer; the application layer is used for running the EDA tool of the corresponding version in the container instance; and the business layer is used for carrying out resource planning, template configuration, task scheduling and result management on the chip design whole process of the EDA all-in-one machine. According to the scheme, the whole process of chip design is integrated through the business layer, scattered project information, resource requirements and result management are incorporated into a unified framework, the tool chain information barrier is broken, and the manual coordination cost is reduced. And meanwhile, the predictability and traceability of task execution are improved, and an efficient and ordered collaborative management scheme is provided for the EDA all-in-one machine.

The invention discloses a land resource planning management method and platform based on space-time big data, and relates to the technical field of land resource planning management, and the method comprises the steps: obtaining multi-source space-time big data, land reserve state variables and land actual supply data in a planning region, and carrying out the space-time weighted fusion of the multi-source space-time big data, constructing a flow space sensing matrix, and extracting a land demand feedforward operator from the flow space sensing matrix; based on the land demand feedforward operator and the land reserve state variable, generating an estimated land demand quantity, and performing difference operation on the estimated land demand quantity and the actual land supply data to generate reserve regulation and control deviation data; the method has the beneficial effects that the land vacancy rate is obviously reduced while the land price is ensured to be stable.

The invention discloses a method for realizing lightweight open world target detection, which comprises the following steps of: generating a prediction frame set for an input image through RPN, and screening out prediction frames of which the intersection-to-union ratio (IoU) meets a predetermined requirement from the prediction frame set to form a potential unknown target candidate set; adopting a density clustering algorithm to screen out a spatially dense candidate region cluster from the center coordinates of the prediction frame in the potential unknown target candidate set, and filtering sparsely distributed background noise to obtain a filtered candidate set; and calculating an IoU matrix of the filtered candidate set and the bounding box set, dividing unknown target sample types according to the IoU matrix, and determining unknown target positive samples, negative samples and neglected samples. The method has the resource adaptability and full-autonomous and high-reliability open world perception capability of the embedded edge device, and provides a practical and efficient open world target detection solution for large-scale landing of scenes such as intelligent cities, industrial monitoring and intelligent traffic.

The application provides a natural resource planning text generation method and system and a storage medium, relates to the technical field of data processing, and comprises the following steps: establishing a grid partition unit in a feature space, performing spatial clustering according to feature vector distribution density; mapping the feature vector to a corresponding partition unit to generate a dynamic material pool containing spatial correlation attributes; generating an outline structure space reference outline based on the spatial correlation attributes; positioning the material pool content by adopting a segmented writing strategy according to the spatial reference outline, generating a text paragraph based on the feature weight of the partition unit; performing structured processing on the text paragraph to obtain a structured processed text; and performing dynamic checking on the structured processed text to output a final planning text. The spatial clustering mapping reduces the processing amount of redundant data and reduces the computing resources.

The invention relates to a resource planning method and apparatus, an electronic device, a storage medium and a program product. The method comprises the steps of obtaining a model portrait, a hardware portrait and a scene portrait; generating at least two resource planning candidate schemes based on the hardware portrait; based on the model portrait, the hardware portrait and the scene portrait, calculating a video memory margin of each resource planning candidate scheme; and selecting a target resource planning scheme from the at least two resource planning candidate schemes based on the calculated video memory margin of each resource planning candidate scheme. Therefore, by taking the hardware portrait, the model portrait and the scene portrait as unified input, feasibility judgment and robust sorting of the resource planning scheme can be formed before resource deployment, so that the problems of long iteration period and high cost of the resource planning scheme caused by only relying on pressure measurement before online or trial and error during operation are avoided; therefore, the interpretability, the recomputability and the mobility of resource planning can be improved.

This application provides a resource planning method, apparatus, electronic device, medium, and program product, which can be applied to the fields of cloud computing technology and artificial intelligence technology. The method includes: acquiring resource observation information and knowledge representation information of a target sub-institution, wherein the knowledge representation information is information generated or provided by multiple teacher models constructed by multiple sub-institutions based on local resource usage history information, and the knowledge representation information includes model parameters and / or soft labels; determining teacher knowledge suitable for the target sub-institution based on the resource observation information and knowledge representation information; training or updating the student model of the target sub-institution based on the teacher knowledge to obtain a resource prediction model; obtaining resource usage prediction results based on the resource observation information and resource prediction model; and generating resource planning suggestions based on the resource usage prediction results.

The invention provides a demand prediction method and device and electronic equipment, and relates to the field of core networks, and the main technical characteristics comprise the following steps: obtaining first demand data of a first organization and second demand data of a second organization; wherein the first organization and the second organization have a competitive relationship of resource allocation; and performing prediction processing on the first demand data and the second demand data to obtain a target demand corresponding to the first organization, and performing prediction processing by combining the demand data of both parties in the resource allocation competition relationship, so that the prediction process fully reflects the actual competition relationship, the accuracy of demand prediction of the first organization is improved, and the user experience is improved. And a reliable basis is provided for resource planning.

The application relates to a bandwidth resource planning method and device, computer equipment and a readable storage medium. The method comprises the following steps: obtaining target resource information and benchmark bandwidth information of a bandwidth resource operator for a target node type, wherein the target resource information comprises regional resource information of multiple regions corresponding to the bandwidth resource operator; generating an operator bandwidth resource planning result of the bandwidth resource operator for the target node type according to the target resource information and the benchmark bandwidth information; generating a regional bandwidth resource planning result of each region of the bandwidth resource operator for the target node type according to the operator bandwidth resource planning result; and determining nodes of the multiple regions under the target node type, and generating a node bandwidth resource planning result of the nodes of each region according to the regional bandwidth resource planning result of each region for the target node type. The method can improve the accuracy of bandwidth resource planning.

The application 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 area, which comprises the following steps: obtaining branch flow calculation results according to topological structure data and real-time load data of a power grid, calculating a current overload coefficient of a branch, and determining a heavy-load branch; determining potential weak points according to voltage amplitude deviations of load nodes of the heavy-load branch, aggregating, obtaining a weak area, and calculating a voltage stability margin of the weak area; obtaining risk indexes of the potential weak points, calculating risk levels of the potential weak points according to the voltage stability margin and the risk indexes, and screening out a first weak point; calculating a resource matching degree between a regulation and control resource and the first weak point; establishing a resource planning model according to the risk levels and the resource matching degree, and obtaining a resource planning scheme. Through multi-dimensional risk identification, accurate resource matching and multi-scenario strategy optimization, the application improves the accuracy and dynamic adaptability of resource planning.

The invention relates to the technical field of artificial intelligence, in particular to a data service system construction method taking artificial intelligence computing power as a core, which comprises the following steps of computing power resource planning and deployment, data acquisition and preprocessing, data storage and management, artificial intelligence model development and training, data service interface design and implementation and data service monitoring. By adopting a virtualization technology, physical computing power resources are abstracted into a virtual resource pool, and computing power is dynamically allocated according to service requirements, so that idle and waste of hardware resources in a traditional mode are avoided.

The present application relates to a kind of multi-airport flight time slot resource allocation method, belong to air traffic control automation technical field, the present application is aimed at the characteristics of complex constraint, strong coupling of multi-airport time slot allocation problem, constructs multi-airport flight time slot optimization model, under the premise of guaranteeing all airport take-off, landing capacity constraint, airport passing time constraint and flight time constraint between take-off and landing airport, make full use of airport time slot resource, generate feasible full track multi-airport flight time slot allocation scheme, while taking into account the fairness between flights, minimize maximum flight time slot adjustment amount;The method of the present application can simultaneously process the flight time slot allocation problem of multi-airport, full season, create value for multiple key subjects of air transportation system, realize the maximization of overall social benefit.Rational time slot allocation improves the punctuality of passengers, improves travel experience;Help airport to realize collaborative resource planning and efficient operation, enhance hub competitiveness.