Product
Patsnap Eureka
For R&D, Patsnap Eureka makes reading and utilizing patents & technical documents easy.
Patsnap Eureka AIR
Designed for self-driven R&D workflows. Generate viable solutions, solve complex R&D challenges, empower your innovation with AI.
Patsnap Eureka Materials
Designed for material experts only. Revolutionize your material R&D, from search, analyze, to developing new materials.
Feature
TechResearch
Generate reliable direction feasibility study reports for your R&D in just a few steps.
TechSeek
Discover and master advanced knowledge NOW. Basics, ideas, possibilities, all at once.
TechMind
As an expert in R&D Theories, TechMind can generates customized viable solutions instantly.
TechRisk
Analyze your overall solution with one click, know your potential R&D risks in advance.
TechMonitor
Get weekly tech updates, stay abreast of the latest tech innovations and key insights.
Patents
Literature
Hiro is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
32 results about "Workload prediction" patented technology
Efficacy Topic
Property
Owner
Technical Advancement
Application Domain
Technology Topic
Technology Field Word
Patent Country/Region
Patent Type
Patent Status
Application Year
Inventor
GPU workload prediction and management
ActiveUS20130106881A1Easy to processDigital data processing detailsDigital computer detailsGraphicsComputerized system
The disclosed embodiments provide a system that configures a graphics-processing unit (GPU) in a computer system. During operation, the system predicts an incoming workload to the GPU. Next, the system identifies an operational floor for the GPU based on the incoming workload. Finally, the system uses the operational floor to configure the subsequent execution of the GPU, wherein the operational floor facilitates processing of the incoming workload by the GPU.
Owner:APPLE INC
Optimizing emergency department resource management
InactiveUS20190180868A1Good care and efficiencyReduce bottlenecksMedical data miningHealth-index calculationSystems analysisResource management
The disclosed embodiments disclose techniques for optimizing emergency department resource management. During operation, a system receives a set of parameters that is associated with a patient entering an emergency department. The system analyzes the set of parameters in a machine learning module to determine (1) a calculated acuity score that indicates an estimated severity of illness for the patient and (2) a set of workload predictions that predict a set of resources that will be needed to treat the patient in the emergency department. The system then uses the acuity score and the workload predictions to assign a set of predicted tasks that are associated with treating the patient into the work queues of the emergency department.
Owner:UBQ INC
Method for predicting software workload of newly-added software project
InactiveCN102147727AImproving the Capabilities of Workload Prediction ModelsImprove abilitiesSpecific program execution arrangementsMissing dataPredictive methods
The invention discloses a method for predicting the software workload of a newly-added software project, belonging to the technical field of development of computer software. The method comprises the following steps of: discretizing the workload of history projects and dividing the history projects into project classes in designated number; calculating the condition probability and the priori probability of each project attribution in the classes of the project workload by using workload attribute data of the history projects; establishing a Bayes classification model and predicting the workload class of the newly-added project; adding the newly-added project subjected to workload classification prediction into history project data, repairing missing date, recalculating the condition probability of the project attribute on the project workload class and recalculating the priori probability of the project workload class, and repeatedly iterating until all probability distributions are converged; and finally predicting the workload of the newly-added software project by using the converged posterior probability distribution. Compared with the prior art, by using the method, the capability of a mode for predicting the workload of the software project is greatly improved.
Owner:INST OF SOFTWARE - CHINESE ACAD OF SCI
Controller workload prediction method and system based on multiple regression model
Owner:CHENGDU CIVIL AVIATION AIR TRAFFIC CONTROL SCI & TECH +1
HEVC-based DVFS control method and system, processor and storage device
ActiveCN107465929AImproved ability to capture mutations in decoding complexitySave energyPower supply for data processingDigital video signal modificationComputer moduleComputer science
The invention relates to an HEVC-based DVFS control method and system, a processor and a storage device. The method comprises the following steps: performing decoding in cooperation with HEVC based on a CPU and a GPU in the manner of a streamline, performing entropy decoding on a binary bit stream input by a decoder, assigning decoder parameters, estimating the number of TU of a video frame i, analyzing an inverse transformation coefficient matrix, and inputting the inverse transformation coefficient matrix to a workload prediction module; on each synchronous node of the CPU and the GPU, predicting the working frequency of the CPU and the working frequency of the GPU by the workload prediction module based on the number of TU of the video frame i estimated by the entropy decoding, and inputting the working frequency of the CPU and the working frequency of the GPU to a frequency manner; adjusting the set working frequency of the CPU and the set working frequency of the GPU according to the number of the decoded video frames in a frame cache, and inputting the set working frequency of the CPU and the set working frequency of the GPU to a regulator; and setting the frequency of the CPU according to the final working frequency of the CPU by the regulator, and setting the frequency of the GPU according to the final working frequency of the GPU, and continuing to decode by the CPU and the GPU at the set frequency.
Owner:SHANDONG UNIV
Genetic algorithm back propagation (GABP) neural network based controller workload prediction method and system
InactiveCN105160201AOptimize workloadGuaranteed reliabilityBiological neural network modelsSpecial data processing applicationsStructure of Management InformationControlled airspace
The invention discloses a controller workload prediction method and system. The method comprises a first step of determining a back propagation (BP) neutral network topology structure, collecting sector traffic flow state index samples of different times, and building a sample set; a second step of optimizing a weight and a threshold of a BP neutral network by use of a genetic algorithm, performing network training, and outputting an optimized BP neutral network; a third step of predicting the index of the controller workload through the optimized BP neutral network of the second step according to the input real-time data of the sector traffic flow state index; and a fourth step of responding to a warning if a result of predicted index of the controller workload meets a preset condition. Through adoption of the method, the reliability of the prediction result of the controller workload can be improved, actual demands of an air traffic control unit on real-time prediction and warning for the controller workload can be met, and the method can provide data support for improvement of operation management level and optimization of the controlled airspace structure.
Owner:CHENGDU CIVIL AVIATION AIR TRAFFIC CONTROL SCI & TECH +1
Method for service capacity expansion and shrinkage and related equipment
ActiveCN112000459ATimely and accurate determinationMeet the requirements of the SLAResource allocationMachine learningState predictionCurrent cycle
The invention provides a method for service capacity expansion and shrinkage and related equipment. The method comprises the following steps: obtaining index information and a workload of a current period service, and obtaining a workload prediction value of a next period according to the workload by utilizing a workload prediction model; predicting the state of the service in the next period by using a state prediction model according to the workload prediction value and the index information; and according to the predicted state, determining an instance expansion and shrinkage strategy corresponding to the service and expanding and shrinking the instance corresponding to the service. According to the method, the instance expansion and shrinkage strategy can be determined in time, and theaccuracy of the determined instance expansion and shrinkage strategy is improved.
Owner:HUAWEI CLOUD COMPUTING TECH CO LTD
Driver Workload Prediction and Path Routing
InactiveCN107036608ALow costInstruments for road network navigationSemi-structured data mapping/conversionDriver/operatorAutopilot
Methods, systems, and apparatuses for automated driving or for assisting a driver and for determining driver workload prediction as a function of a driving route or path are disclosed. A system includes a predicted workload component, a route component, and a notification component. The predicted workload component is configured to determine that at least one section of a current route comprises a high driver workload. The route component is configured to modify the current route to generate an alternate route, wherein the alternate route avoids the at least one section that comprises a high driver workload. The notification component is configured to provide the alternate route to a driver or automated driving system.
Owner:FORD GLOBAL TECH LLC
GPU workload prediction and management
Owner:APPLE INC
Line production line workload prediction method based on delivery date
PendingCN111784035AAchieve job scheduling and other control purposesForecastingOffice automationOperation schedulingWork in process
The invention provides a line production line workload prediction method based on a delivery date. The method comprises the steps of forming an in-process product list information set; forming a virtual workpiece list information set; calculating a production periodic table according to the historical workpiece information; integrating the in-process product list information and the virtual workpiece list information as a set of elements to obtain a simulated workpiece list information set; obtaining a complete technological process of each simulation workpiece; calculating estimated shipmenttime according to the time of arriving at the current station, the operation time, the waiting time and the stagnation time, and calculating a coefficient according to the estimated shipment time; calculating estimated arrival time, estimated start time and estimated end time of each step according to the coefficients; and obtaining a prediction set of prediction information for each simulated workpiece. According to the method, the coefficient value is used for correcting the production period, the future workload prediction of the whole production line is quickly realized, the data are analyzed, and the regulation and control purposes of production regulation and control, productivity early warning, operation scheduling and the like are achieved in combination with the condition of the actual production line.
Owner:SHANGHAI HUALI INTEGRATED CIRCUTE MFG CO LTD
Bank outlet workload prediction method and device, electronic equipment and storage medium
The invention provides a bank outlet workload prediction method and device, electronic equipment and a storage medium. The method comprises the steps: obtaining workload flow data and auxiliary data of a bank outlet; performing feature extraction according to the workload flow data and the auxiliary data to obtain feature data; respectively inputting the feature data into a plurality of pre-trained monomer prediction models to obtain corresponding prediction values; adopting a non-dominated sorting genetic algorithm with an elitist strategy to process the prediction value of each monomer prediction model to obtain a bank outlet workload prediction result, wherein the monomer prediction model is a holt-works model, a multi-layer perceptron model, a decision tree model, a gradient boosting tree regression model and a long short-term memory network model. According to the invention, the method can efficiently and accurately predict the workload of each post of the outlet every day in a future period of time, and guide the bank outlet to determine the number of workers required by each post.
Owner:INDUSTRIAL AND COMMERCIAL BANK OF CHINA
Work platform task workload prediction method based on feedback correction
PendingCN111652403AAvoid impacting workload forecastsHigh precisionForecastingResourcesOriginal dataWork task
The invention discloses a work platform task workload prediction method based on feedback correction. The method comprises the following steps that a work task is received, the packet sender publishesa work task demand to the work platform; the working platform receives various tasks, the work tasks are screened out, primary screening is carried out on the work tasks, some tasks which cannot be completed are rejected, task classification is carried out, statistics on all the tasks is carried out, then the tasks are classified, the tasks are preliminarily divided into three categories of universality, industrial design and software development; work contents are evaluated, the work content of the received tasks is evaluated, and wherein evaluation and classification is content evaluation.According to the invention, detailed information in the work task can be fully understood, the detail information is accurately evaluated; the construction period evaluation error value of the actualconstruction period and the evaluation construction period is obtained through the stored data, the artificial neural network and analogy arrangement, feedback correction can be performed according to comparison of the collected information and the original data, and the task workload is re-evaluated.
Owner:武汉空心科技有限公司
Knowledge-driven function change propagation path and workload prediction method
ActiveCN111126706AImprove R&D ProgramGuarantee the progress of research and developmentForecastingResourcesDesign planTheoretical computer science
The invention provides a knowledge-driven function change propagation path and workload prediction method. The method comprises the following steps: firstly, from the perspectives of functions and knowledge, establishing a function change propagation prediction information model, determining a change propagation potential path, finding out all potential affected functions, and comparing the similarity between change knowledge of the change functions and knowledge of the affected functions one by one; secondly, respectively establishing a change propagation tree from the function decompositionmodel and a change propagation tree from the function interface model, and calculating change propagation possibility and extra complexity and workload brought by change propagation; and finally, making a specific design plan according to the extra complexity and workload, and effectively managing the product research and development progress. The method is combined with a BZT complexity evaluation method, helps engineers to improve an existing research and development plan, guarantees the research and development progress, and improves the problems of huge workload, high input subjectivity and low universality of a product component-based engineering change propagation prediction method.
Owner:ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
GPU workload prediction and management
ActiveUS20160042489A1Power supply for data processingProcessor architectures/configurationGraphicsComputerized system
The disclosed embodiments provide a system that configures a graphics-processing unit (GPU) in a computer system. During operation, the system predicts an incoming workload to the GPU. Next, the system identifies an operational floor for the GPU based on the incoming workload. Finally, the system uses the operational floor to configure the subsequent execution of the GPU, wherein the operational floor facilitates processing of the incoming workload by the GPU.
Owner:APPLE INC
Line production line workload prediction method based on production cycle
PendingCN111784036AAchieve productivityTo achieve the purpose of production capacity early warning and other regulationForecastingOffice automationProduction lineStatistical Report
The invention provides a line production line workload prediction method based on a production cycle. The method comprises the steps of forming an in-process product list information set; forming a virtual workpiece list information set; calculating a production periodic table according to the historical workpiece information; integrating the in-process product list information and the virtual workpiece list information as a set of elements to obtain a simulated workpiece list information set; obtaining a complete process flow corresponding to each piece of simulated workpiece list informationaccording to the production periodic table; calculating the estimated arrival time, the estimated start time and the estimated end time of each step of each simulation workpiece; and obtaining a prediction set of the prediction information of each simulation workpiece to form a statistical report. According to the characteristics and known data of the flow production line, the prediction method is utilized to quickly realize workload prediction of the whole production line in a period of time in the future, detailed prediction data are reserved, analysis of each dimension is performed on thedata, and the regulation and control purposes of production regulation and control, productivity early warning and the like are achieved in combination with the condition of the actual production line.
Owner:SHANGHAI HUALI INTEGRATED CIRCUTE MFG CO LTD
Cloud platform resource dynamic scheduling method based on deep learning
PendingCN113806094ARealize automatic schedulingAccurately predict workloadResource allocationNeural architecturesData setEngineering
A cloud platform resource dynamic scheduling method based on deep learning comprises the following steps: obtaining time series data from monitoring information and log files of a system, and taking the time series data as a training data set of a model; constructing a workload prediction model; constructing a response time prediction model; and constructing a scheduling strategy model according to the constructed workload prediction model and the response time prediction model: performing joint debugging on the scheduling strategy model, optimizing model parameters, forming a stable cloud platform resource dynamic scheduling model based on deep learning, and realizing scheduling of cloud platform resources. Based on server-oriented scheduling, the deep learning model is used for continuous training, a stable and effective model is finally obtained, automatic scheduling of cloud platform resources can be achieved, a large number of cloud resources can be saved in actual production, and good economic benefits are achieved.
Owner:WUHAN IRON & STEEL ENG TECH GROUP
Working platform workload prediction method and system based on load prediction
InactiveCN111915102AImprove work efficiencyConvenient pricingForecastingResourcesData setNeural network learning
The invention discloses a working platform workload prediction method and system based on load prediction, and relates to the technical field of computer software. The method comprises the following steps: initializing a known data source calculation topology data set to train an optimized SOM network; obtaining to-be-allocated tasks in the task pool and grouping the to-be-allocated tasks according to the workload; enabling the SOM network model to acquire a training neural network model of each cluster by using the workload characteristics of each cluster in the neural network learning task pool; when a user publishes a new task, enabling the SOM network model to firstly obtain an initial workload of the user and determine a cluster to which the user belongs according to the initial workload of the user; and predicting the workload of the new task by utilizing the training neural network model of the cluster to which the new task belongs. The SOM network is trained and optimized through the known data, the training neural network model of each cluster is obtained according to the load characteristics of the clusters to complete prediction of the workload of the new task, pricing of the task is facilitated, the task is reasonably allocated, and the working efficiency of employees is improved.
Owner:武汉空心科技有限公司
New-drilled well workload prediction method based on ensemble learning
InactiveCN112330064AHigh precisionReduce the effect of noisy dataEnsemble learningForecastingGeneralization errorWell drilling
The invention discloses a new-drilled well workload prediction method based on ensemble learning. The method is characterized in that a new-drilled well workload prediction model based on a random forest is built, a key hyper-parameter combination of the new-drilled well workload prediction model is optimized through a particle swarm optimization method, and a weighted voting mechanism is added ina decision stage; by adjusting the weight value of the weak classification decision tree, the generalization error of new-drilled well workload prediction is reduced, and the precision of the new-drilled well workload prediction model is improved. The key hyper-parameter combination in the new-drilled well workload prediction model is optimized by adopting the particle swarm optimization method,the influence of noise data in oil reservoir development historical data is reduced, and the stability and the operation speed of the random forest method are improved; a weighted voting mechanism isadded in the decision stage, wherein the weight proportion of a high-score decision tree is increased and meawhile the negative influence of a low-score decision tree on a prediction result is reduced, and thereby the precision of a new-drilled well workload prediction model is improved.
Owner:CHINA UNIV OF PETROLEUM (EAST CHINA)
Working platform task workload assessment method based on big data
PendingCN113128916AImprove work efficiencyEasy to useCharacter and pattern recognitionNeural architecturesOriginal dataEngineering
The invention discloses a working platform task workload assessment method based on big data, and relates to the technical field of working platform management. According to the method, a working platform decomposes a task, searches a matched packet receiving party from a talent pool of the working platform according to skill requirements of each sub-task, and evaluates the working content of the task received by the packet receiving party, and the evaluation is classified into content evaluation, complexity evaluation and similarity evaluation. The method is convenient to operate and use and simple to operate, the working efficiency of working platform management can be effectively improved, the original data stored in the working platform is compared with the evaluated working content, the working content influenced by various factors is calculated through comparison, and the workload is reanalyzed. The accuracy of workload evaluation of a working platform is improved, the work task is decomposed into a plurality of sub-tasks, it is avoided that due to too little classification, task classification is inaccurate, and later workload prediction is affected, and the prediction accuracy is improved.
Owner:武汉空心科技有限公司
Data Center Workload Prediction Method Based on Wavelet Neural Network and Linear Regression
InactiveCN108197083BImprove usabilityImprove securityForecastingNeural architecturesData centerEngineering
The invention discloses a short-term workload prediction method based on the fusion of linear regression and wavelet neural network, which includes: first step, establishing a historical workload database through data center system log files; For the relative stability of the workload at the same time, linear regression is used to predict the short-term workload; in the third step, due to the relative volatility and local stability of the workload in each period of the day in the data center, this patent uses error feedback propagation wavelet neural network technology to predict Workload; the fourth step is to integrate linear regression and wavelet neural network prediction technology to predict the short-term workload of the data center; the fifth step is to update the historical workload information database with the actual workload of the data center in the current period, and execute the second, third and fourth cycles step-by-step forecast workload. Compared with the prior art, the invention has the advantages of high precision and can provide strong technical support for data center resource management and energy consumption control.
Owner:HUNAN AGRICULTURAL UNIV
Oil and gas field asset evaluation method and device
PendingCN113496301AOptimize prediction speedPrediction is accurateForecastingResourcesEvaluation resultThermodynamics
The invention provides an oil and gas field asset evaluation method and device, and relates to the technical field of oil and gas exploration and development, and the method comprises the steps: obtaining oil and gas field basic parameters and bid invitation contract evaluation parameters; according to the recovery ratio data, the recovery degree data, the storage and production ratio data and the geological reserve data, determining a peak period yield and peak period stable yield period prediction model; determining a yield and workload prediction model according to the storage and production ratio data, the oil production speed data, the decline stage yield data, the yield increase yield data and the single well productivity data; determining a well group optimization prediction model according to the recovery ratio data, the reservoir effective thickness data, the well spacing data and the permeability data; and generating an oil and gas field asset evaluation result according to the peak period yield and peak period stable production period prediction model, the yield and workload prediction model and the well group optimization prediction model. According to the method, the prediction speed is optimized, and a more accurate prediction result is obtained based on limited parameters.
Owner:PETROCHINA CO LTD
Wrist workload prediction method and device based on multi-modal physiological data acquisition
ActiveCN112274163AAccurate Workload MetricsPrevention of impact on wrist movementDiagnostic signal processingSensorsSimulationData acquisition
The invention discloses a wrist workload prediction method based on multi-modal physiological data acquisition. The method comprises the steps: obtaining a first electromyographic signal, a second electromyographic signal, a third electromyographic signal and a fourth electromyographic signal according to electrodes; obtaining first input information, second input information, third input information and fourth input information according to the first electromyographic signal, the second electromyographic signal, the third electromyographic signal and the fourth electromyographic signal respectively; inputting the first input information, the second input information, the third input information and the fourth input information into a motion prediction model to obtain a first motion index;obtaining a predetermined wrist load threshold; judging whether the first motion index is within a preset wrist load threshold value or not; and if the first motion index is not within the predetermined wrist load threshold, obtaining first early warning information. The technical problem of traumatic diseases caused by long-term overload work of the wrist is solved.
Owner:北京中科心研科技有限公司
Hevc-based dvfs control method, system, processor and storage device
ActiveCN107465929BImproved ability to capture mutations in decoding complexityReduce dynamic energy consumptionDigital data processing detailsDigital video signal modificationComputer moduleComputer science
The invention relates to an HEVC-based DVFS control method and system, a processor and a storage device. The method comprises the following steps: performing decoding in cooperation with HEVC based on a CPU and a GPU in the manner of a streamline, performing entropy decoding on a binary bit stream input by a decoder, assigning decoder parameters, estimating the number of TU of a video frame i, analyzing an inverse transformation coefficient matrix, and inputting the inverse transformation coefficient matrix to a workload prediction module; on each synchronous node of the CPU and the GPU, predicting the working frequency of the CPU and the working frequency of the GPU by the workload prediction module based on the number of TU of the video frame i estimated by the entropy decoding, and inputting the working frequency of the CPU and the working frequency of the GPU to a frequency manner; adjusting the set working frequency of the CPU and the set working frequency of the GPU according to the number of the decoded video frames in a frame cache, and inputting the set working frequency of the CPU and the set working frequency of the GPU to a regulator; and setting the frequency of the CPU according to the final working frequency of the CPU by the regulator, and setting the frequency of the GPU according to the final working frequency of the GPU, and continuing to decode by the CPU and the GPU at the set frequency.
Owner:SHANDONG UNIV
Inspection robot work adjustment method based on regression analysis algorithm
PendingCN113625712AExtended service lifeAvoid damagePosition/course control in two dimensionsPrediction algorithmsElectrical battery
The invention discloses an inspection robot work adjustment method based on a regression analysis algorithm, and the method comprises the following steps: S1, obtaining an inspection route of an inspection robot, and recording the positions of charging piles and robot shutdown rooms distributed on the inspection route; S2, establishing a battery loss degree mapping table according to charging of the charging pile and charging of the shutdown room; s3, obtaining the working state of the inspection robot and the historical data of the corresponding battery power use characteristics, and establishing a workload prediction model by adopting a regression analysis prediction algorithm; s4, acquiring a current inspection task of the inspection robot, judging whether the battery capacity of the inspection robot meets the requirement of the current inspection task or not according to the workload prediction model, if so, entering step S6, and otherwise, entering step S5. According to the inspection robot, in the inspection process, the electric quantity can fully meet the inspection requirement, meanwhile, charging is reasonably conducted, the battery is protected, damage to the battery is reduced, and the service life of the inspection robot is prolonged.
Owner:STATE GRID ZHEJIANG ELECTRIC POWER CO LTD JIAXING POWER SUPPLY CO +1
Method for predicting software workload of newly-added software project
InactiveCN102147727BImproving the Capabilities of Workload Prediction ModelsImprove abilitiesSpecific program execution arrangementsMissing dataPredictive methods
Owner:INST OF SOFTWARE - CHINESE ACAD OF SCI
Staged workload prediction method and system based on composite variable characteristics
PendingCN114238102AAccurate predictionForecastingSoftware testing/debuggingLinear regressionData mining
The invention relates to a staged workload prediction method and system based on composite variable characteristics, and the method comprises the steps: building a test workload prediction model and a staged workload prediction model, and firstly, obtaining the total prediction workload through the test workload prediction model; the total prediction workload is used as input, a staged workload prediction model is used for calculation to obtain the predicted staged workload, and aiming at the problem of how to evaluate the project test workload, on the basis of fully considering various influence factors, a linear regression algorithm and a variational self-encoding VAE algorithm are used for evaluating the project test workload. And predicting the overall test workload of the project and the workload of each stage of the project test work so as to achieve the purpose of accurately predicting the project test workload.
Owner:CHINA CITIC BANK
Execution object quantity processing system and method
PendingCN112836906AConvenient self-managementImprove practicalityOffice automationResourcesData managementComputer science
The embodiment of the invention provides an execution object quantity processing system and method. The system comprises a data management system, a quantity determination system for preset execution objects and a display system. The quantity determination system for the preset specified objects comprises a prediction model generation subsystem which is used for generating a prediction model according to the historical number of first specified execution objects, the historical number of second specified execution objects and historical data of performance; an acquisition subsystem which is used for acquiring workload prediction data and a prediction model under an organization object of a specified level; and a predicted quantity generation subsystem which is used for generating the predicted quantity of the second specified execution object according to the workload prediction data and the prediction model. According to the embodiment of the invention, reasonable quantitative distribution of the indoor staff of the service department is realized, the problems of resource waste caused by too much indoor staff and reduction of performance of each service department caused by too little indoor staff are solved, and autonomous management of each service department and overall development of a company are promoted.
Owner:TAIKANG LIFE INSURANCE CO LTD +1
Station shunting operation workload prediction method
PendingCN114368420AEfficiently predict the workload of shunting operationsRailway traffic control systemsComplex mathematical operationsConcurrent computationStation
The invention discloses a method for predicting the workload of shunting operation of a station. The method comprises the following steps: firstly, constructing time sequence data for measuring the workload of the shunting operation through wireless shunting locomotive signals and STP data of a monitoring system; distributing sub-sequence data of the constructed time sequence data to Hadoop cluster nodes, dividing a DTW distance matrix into a plurality of sub-matrixes, and obtaining a time sequence DTW distance by adopting a parallel computing method; and searching a scene with the highest similarity with the current data in the data over the years according to the obtained DTW distance, and predicting the workload of the shunting operation of the station on the basis of the scene. The method can effectively predict the shunting operation workload of the station in a specific time period, carries out the proper allocation of transportation resources according to the workload, and provides a judgment basis for the related departments of the railway in transportation scheduling command.
Owner:SIGNAL & COMM RES INST OF CHINA ACAD OF RAILWAY SCI +3
A knowledge-driven functional change propagation path and workload prediction method
ActiveCN111126706BThe number of functional requirements is lowReduce workloadForecastingResourcesDesign planAlgorithm
Owner:ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
SNN workload prediction method and system
InactiveCN114090261APredictive mapping resultsAccurately predict mapping resultsResource allocationNeural architecturesLoad modelSimulation
The invention discloses an SNN workload prediction method and system. The method comprises the steps: the following steps: S1, building an SNN workload model based on an NEST simulator, and the SNN workload model comprises a memory load model, a calculation load model and a communication load model; s2, collecting parameters of the SNN workload model, wherein the parameters comprise memory parameters, time parameters and network parameters; and S3, constructing a load calculation function according to the SNN workload model, processing the obtained parameters, and predicting the workload of the SNN target network under a plurality of nodes. According to the SNN workload prediction method and system, the problem of reasonable matching between the SNN workload and the calculation platform can be solved, the mapping result of the SNN network on the calculation platform can be accurately predicted, mapping guidance is provided for the calculation platform on the basis, and high-performance operation of the platform is ensured in a mode of reasonably distributing the calculation nodes.
Owner:JIANGNAN UNIV
Who we serve
- R&D Engineer
- R&D Manager
- IP Professional
Why Patsnap Eureka
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com