161 results about "Resource planning" patented technology

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 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 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 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 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 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.

The embodiment of the invention provides a network resource planning method and device and a related product. The method comprises the following steps: acquiring a first value range of a first parameter and a second value range of a second parameter; the first parameter is used for deploying a first network resource, and the second parameter is used for deploying a second network resource; based on a benefit function, determining a first target parameter value of a first parameter in the first value range, and determining a second target parameter value of a second parameter in the second value range; the benefit function is determined based on the network resource deployment cost and the network resource deployment income; the benefit function values corresponding to the first target parameter value and the second target parameter value are maximum; and performing network resource deployment processing on a target area based on the first target parameter value and the second target parameter value. Through the embodiment of the invention, the deployment of the network resources is more reasonable and balanced, and the quality of accessing the network resources by the users in the region is improved.

The invention relates to the technical field of resource planning, and discloses a smart city resource flow simulation and planning system based on a city metabolism model, and the system comprises a parameter collection module which determines a time boundary parameter and a scale parameter; the scale parameterization module is used for calculating metabolic integral scale elasticity; the effective budget generation module is used for calculating to obtain an elastic weighted effective budget; the resource allocation solving module is used for solving the allocation amount of each type of source; the scheduling curve mapping module is used for calculating to obtain a scheduling curve; and the planning result output module outputs the allocation list. According to the method, the robust budget is constructed through the scale parameterization model and the metabolic integral scale elasticity, and the planning deviation under different statistical granularities is reduced. The objective function is fused with the energy cost, the source cost and the scale robust item, the balance of cost control and scale adaptability is realized, and the execution deviation caused by purely pursuing the optimal cost is avoided.

The application discloses a resource distribution method of a storage system and an electronic device, relates to the technical field of electric digital data processing, and can perform leaf node resource retrieval in a resource model tree according to a retrieval order determined according to the structure of the resource model tree of the storage system, to determine a target leaf node. After the resource distribution retrieval of any user is completed, the target leaf node is occupied first, so that other users jump over the occupied target leaf node when performing retrieval, and continue to perform retrieval according to the retrieval order, until the resource distribution request responses of all users are completed. The technical problem that, in the related art, when multiple allocation requests are parallel, the retrieval efficiency is relatively low, resource occupation waiting exists, and only the current demand is considered when allocating resources, and the overall resource planning cannot provide a reference is solved, the effect of avoiding full resource scanning, optimizing a retrieval path, improving retrieval efficiency and concurrent processing capability, and facilitating overall resource planning is achieved.

The invention provides an unmanned aerial vehicle cluster load resource planning method based on multi-attribute coding and parallel variable neighborhood search, which belongs to the technical field of unmanned aerial vehicle resource planning, and comprises the following steps: firstly, determining target characteristics and task requirements, and determining unmanned platforms and available resource attributes; calling a load configuration algorithm to generate a configuration scheme, adopting multi-attribute coding to represent the configuration scheme, preferentially constructing an initial scheme based on task requirements, then adopting a self-adaptive variable neighborhood multi-population parallel evolution method to solve, generating a task configuration scheme meeting an optimization target and constraint conditions, and uploading the task configuration scheme; determining whether to approve to use the scheme or not by using expert experience or a decision support system according to the feasibility and efficiency of the scheme and the integrating degree of the scheme and a target; starting from the rationality and economy of the load resource allocation method for the unmanned aerial vehicle cluster, a higher cost-effectiveness ratio is realized on the premise of ensuring the task efficiency.

The utility model discloses a ground marking device for resource planning, and belongs to the technical field of ground marking devices. Comprising an electric trolley and a rotating disc, the rotating disc is arranged outside the electric trolley, a supporting plate is installed at the top end of the rotating disc, a stirring barrel is installed at the top end of the supporting plate, a Y-axis moving support is installed at the top end of the supporting plate on one side of the stirring barrel, and a Z-axis moving support is arranged outside the Y-axis moving support; and a control panel is mounted at the top end of the electric trolley. According to the utility model, the ground marking device can conveniently move and adjust the position for marking at multiple positions, conveniently rotate the angle for marking, enlarge the marking range of the ground marking device, conveniently mark on a slope surface, improve the marking convenience of the rotation angle of the ground marking device, and improve the working efficiency of the ground marking device. And the raw material mixing uniformity of the ground marking device is improved.

This application discloses a method for predicting OTA upgrade completion rate and cycle time based on multimodal data fusion. The method first collects multimodal data on vehicles, users, environmental networks, and upgrade packages, and generates feature vectors reflecting multidimensional influencing factors through preprocessing and feature engineering. Subsequently, the feature vectors are input into a pre-trained multi-task learning model, which outputs in parallel the upgrade completion probability and predicted upgrade cycle time for the target vehicle. Finally, based on these prediction results, a global completion rate curve and optimization strategy are generated. This application overcomes the limitations of traditional methods that rely on single historical data by fusing multi-source heterogeneous data, achieving a shift from "post-event statistics" to "pre-event prediction." It can accurately predict key indicators of OTA upgrade activities and provide quantitative basis for proactive intervention and resource planning, significantly improving upgrade completion rate and operational efficiency.

The application discloses a kind of based on two-stage ecological constraint's metallogenic favorable area type correction method, system and electronic equipment, method includes: obtaining the evaluation unit of metallogenic favorable area of study area, ecological protection red line data, ecological function importance data and ecological sensitivity data;Based on ecological function importance data and ecological sensitivity data, generate the ecological constraint index Eeco of each metallogenic favorable area evaluation unit;First stage rigid screening and second stage elastic correction;Output each metallogenic favorable area evaluation unit after correction Classification attribute and / or space layer containing classification attribute.The application establishes the ecological constraint processing mechanism of rigid and elastic combination, by independent ecological priority marking system, on the premise of not changing type level, to the key development area needing to strengthen ecological control has been marked, provides more abundant information level for subsequent planning management, improves the reusability and update efficiency of mineral resources planning results.

The invention relates to the technical field of resource planning, in particular to an intelligent logistics tracking system driven by the Internet of Things, which comprises a positioning time sequence module, an aging verification module, a path resource module, a path evaluation module and a path optimization module. According to the method, the cargo displacement distance and the transportation duration are synchronously associated to form a tracking state capable of reflecting the advancing rhythm change, and the remaining time is continuously calculated around the delivery time limit, so that the aging pressure sensing in the transportation process is realized; on this basis, unified verification is carried out on storage capacity loading and unloading waiting and passing time consumption of multiple connectable paths in combination with the current position, comparative analysis is carried out on path time occupation and remaining time, and meanwhile continuous cycle time consumption fluctuation evaluation is introduced, so that limited risks are dominated in advance, and the accuracy of the system is improved. Therefore, weight distinguishing and sorting optimization of feasible paths are supported, and the probability of scheduling mismatch and resource waste of adjacent delivery nodes is reduced.

The invention relates to the technical field of power system optimization, in particular to a multi-time-scale-considered main-distribution cooperative integrated optimization planning method and system, and the method comprises the steps: building a main and distribution network resource planning framework, considering a multi-time-scale operation simulation method, analyzing the power generation capability of new energy in different time periods, and calculating the power generation capability of the new energy in different time periods. Multi-time scale output prediction is carried out; based on multi-time-scale output prediction, in combination with power grid load, system constraint and equipment capability, setting an optimization objective function and constraint conditions, and constructing a main and distribution network collaborative planning model; and solving the main and distribution network collaborative planning model by using a particle swarm algorithm to obtain the optimal capacity configuration, and determining the optimal installation position and capacity of the new energy. According to the method, the output characteristics of the renewable energy sources and the load change of the power grid in different time periods are accurately analyzed, and coordinated optimization of multiple types of resources in the main and distribution networks is realized, so that the stability and the economical efficiency of a power system are effectively improved, and the calculation efficiency of an algorithm is improved.

The application provides a method and system for realizing resource use and concurrent reasoning of a large model all-in-one machine, which comprises collecting model basic information in combination with the use scene of the large model all-in-one machine; formulating a differentiated resource allocation strategy according to an evaluation result, planning isolated resources and shared resources; evaluating and classifying data storage; optimizing a model reasoning engine; defining core monitoring indexes; constructing a multi-dimensional index evaluation system; and completing dynamic optimization of model resources and parameters by referring to the evaluation result of the model indexes. Through systematic resource planning and management design, the application constructs an efficient intelligent resource scheduling system, realizes intelligent perception and precise deployment of resources of the large model all-in-one machine, and effectively supports accurate evaluation of the rationality of resource use, effectively responds to complex and changeable application scene requirements, significantly improves the resource utilization rate and operation efficiency of the large model all-in-one machine, and brings an innovative solution and significant application value to the field of the large model all-in-one machine.

This invention discloses a distributed resource planning method for a data center integrated energy system under a diversified market environment, belonging to the field of power system planning. The method constructs a two-layer optimization framework. The upper layer uses a multi-objective robust Bayesian optimization algorithm under input noise to optimize distributed resource capacity. The lower layer establishes a simulation model of the data center integrated energy system operation based on the diversified market benefits of energy storage. It incorporates Gaussian distributed noise to model uncertainty factors, trains a robust Gaussian process surrogate model, and selects sampling points for iterative optimization through a robust expectation hypervolume improvement function. Finally, it outputs a robust optimal configuration scheme through Pareto sorting. This invention solves the problems of poor robustness, imbalance in multi-objective optimization, single energy storage value assessment, and algorithm inefficiency in existing methods, achieving stable configuration schemes, balanced objectives, and accurate benefits, thereby improving system economy and renewable energy absorption rate.

Embodiments of the present application provide a CSI-RS resource allocation method, device, apparatus and storage medium, the method comprising: determining, by a network device, an edge weight value between any two of a plurality of TRPs according to a plurality of acquired sampling point data; wherein any one of the sampling point data contains a plurality of measurement results of SRSs transmitted by the plurality of TRPs to a same terminal at a same time; constructing a first TRP relationship network graph according to a pair of TRPs in the plurality of TRPs whose edge weight value is greater than or equal to an edge weight value threshold; determining a second TRP relationship network graph according to the first TRP relationship network graph and a maximum CSI-RS resource number supported by the network device for allocation, and allocating CSI-RS resources according to the second TRP relationship network graph. Thus, the calculation process of obtaining a resource allocation scheme is simplified, and efficient matching of TRP topology and CSI-RS resource planning is completed.

The invention relates to the technical field of network construction, and particularly provides a resource planning method and device, a medium and a program product, and the method comprises the steps: obtaining the traffic use information of a plurality of users in an administrative region to which a target region belongs, dividing the administrative region into a plurality of reference regions, determining a related flow type of each reference area in the plurality of flow types based on the usage amount of the user for each flow type in each reference area, and determining a resource planning result based on the related flow type of the target area and a predetermined resource planning strategy; according to the invention, the rationality and scientificity of the determined resource planning result can be improved.