86 results about "Cost prediction" patented technology

A computer-implemented process of developing a person-level cost model for forecasting future costs attributable to claims from members of a book of business, where person-level data are available for a substantial portion of the members of the book of business for an actual underwriting period, and the forecast of interest is for a policy period is disclosed. The process uses development universe data comprising person-level enrollment data, historical base period health care claims data and historical next period claim amount data for a statistically meaningful number of individuals. The process also provides at least one claim-based risk factor for each historical base period claim based on the claim code associated with the health care claim and provides at least one enrollment-based risk factor based on the enrollment data. The process also develops a cost forecasting model by capturing the predictive ability of the main effects and interactions of claim based risk factors and enrollment-based risk factors, with the development universe data through the application of an interaction capturing technique to the development universe data.

The invention discloses a fast and efficient video coding intra mode determining method. The fast and efficient video coding intra mode determining method includes the steps of prediction manner configuration and prediction mode selection. According to the prediction mode selection, the correlations between brightness intra prediction modes of a current CU layer and brightness intra prediction modes of CU layers above the current CU layer, the correlation between CBF and TU segmentation depth, and the correlations between chroma intra prediction modes of adjacent CU layers are utilized, and redundant intra mode prediction can be skipped, and therefore, coding computation complexity can be decreased. According to the correlation between a cost function and RDcost and the correlations between chroma intra prediction modes and brightness intra prediction modes, a minimum-cost prediction mode and an optimal brightness prediction mode in RMD are added in a brightness and chroma intra prediction process, and therefore, video coding efficiency and video quality can be ensured. Compared with high efficiency video coding (HEVC) standards in the prior art, the fast and efficient video coding intra mode determining method of the invention can greatly decrease coding computation complexity under the premise that bit rate and video quality are almost unchanged.

Methods and systems are described for predicting the likely causes of service outages using only time information, and for predicting and the likely costs of service outages. The likely causes are found by defining a narrow likely cause window around an outage based on service quality and / or service usage data, and correlating service events to the likely cause window in the time domain to find a probability distribution for the events. The likely costs are found by measuring usage loss and duration for a given point during an outage and using cost component functions of the time and usage to extrapolate over the outage. These cause and cost predictions supply service administrators with tools for making more informed decisions about allocation of resources in preventing and correcting service outages.

An energy cost prediction unit predicts an energy cost for the next travel. A lifetime prediction unit uses a charge amount—lifetime map to predict the lifetime of a power storage device according to an amount of charge to the power storage device. A usage cost prediction unit predicts the cost of using the power storage device according to the amount of charge to the power storage device, based on the lifetime of the power storage device predicted by the lifetime prediction unit. The predicted cost associated with the amount of charge to the power storage device is indicated on a display device. In accordance with an instruction from a user, the power storage device is charged from a charging station.

The invention provides a server integration method oriented to minimum energy consumption. The server integration method oriented to minimum energy consumption includes that resource states and performance data of servers and virtual machines on the servers are periodically obtained, and meanwhile, energy consumption of the servers is periodically measured by an external-connected wattmeter on a physical server to be stored; resource state data of the servers, resource state data of the virtual machines, performance data of the servers and the performance data of the virtual machines are periodically collected, and data pre-processing is performed; a server energy consumption model is established; a virtual machine transfer cost prediction model is established; transfer cost prediction value of each virtual machine is obtained; virtual machine comprehensive assessment is performed by means of an improved analytic hierarchy process; service stability index of the servers is calculated; a server integration scheme is determined; server integration is performed. According to the server integration method oriented to minimum energy consumption, the virtual machines are transferred to proper servers by means of a dynamic packing algorithm according to virtual machine resource required quantity and server resource surplus, and the number of starting servers is the minimum under the condition of stable service operation.

An energy cost prediction unit predicts an energy cost for the next travel. A lifetime prediction unit uses a charge amount—lifetime map to predict the lifetime of a power storage device according to an amount of charge to the power storage device. A usage cost prediction unit predicts the cost of using the power storage device according to the amount of charge to the power storage device, based on the lifetime of the power storage device predicted by the lifetime prediction unit. The predicted cost associated with the amount of charge to the power storage device is indicated on a display device. In accordance with an instruction from a user, the power storage device is charged from a charging station.

An exemplary system for analyzing and modeling electric generators for a customer is provided. The system may include a database component and processor. The database operable to maintain data identifying a generator, a generator fuel cost projection, operating parameters, a customer load profile, and an electricity cost projection for a period of time. The processor operable to calculate a projected generation cost, and to compare the projected generation cost with the electricity cost projections to provide electricity to the customer through an electric grid. A method for modeling distributed generation is also provided. The method includes determining a projected customer electric cost to provide electricity to a customer from the electric grid and determining a projected generation cost for a generator to generate electricity for the customer for the period of time to meet the customer's projected load or electric load profile.

A method for determining the geographical location of a network-connected mobile device comprising a hybrid approach that further comprises both the utilization of strength or time delay signals and a database of fingerprints taken throughout the network area.

The discovery cost forecasting system uses incomplete information to generate forecasts of discovery expenses. Models are generated with different levels of granularity depending upon the available facts and user preferences. An expert provides real-time information to improve the accuracy of the predictions. A scenario analysis is generated that evaluates how changes to the parameters of the matter could affect the forecasted cost. An estimate is generated of the degree of advancement of the matter in its lifecycle as part of the cost forecast. The cost associated with each data source and custodian is tracked and reported. A configurable and extensible cost equation is defined. A dashboard user interface is generated to monitor the entire portfolio to extract easy to understand facts, trends, and early warning signs related to discovery cost management.

The present invention is dynamic cost controlling method for metallurgical production process under network environment and by means of concentrated and distributed control technology, dynamic cost management technology, intelligent nerve network technology, etc. The system structure consists of hardware system and software system, the hardware system includes exchanger, server, user terminals, dynamic data acquisition system, control network and enterprise network, and the software system includes application platform software, data base, information collection and interface software. The key algorithm in dynamic cost control software package utilizes nerve network in dynamic cost prediction and analysis. The present invention can lower production cost of production enterprise.

The invention provides a method and system for controlling construction cost based on a BIM technology. The method includes the steps that 101, a BIM three-dimensional model including rod piece information is constructed; 201, according to list valuation standards and budget quota, the BIM three-dimensional building model is modified, and a BIM model meeting computation amount standards is created; 301, the standard BIM model is pushed to a BIM cloud end and establishes data docking with all construction organizations; 401, before engineering construction, the construction organizations utilize data pushed to the BIM cloud end to perform cost prediction and planning; 501, in engineering construction, according to the data pushed to the BIM cloud end, the construction cost is checked and analyzed; 601, after engineering construction, according to the data pushed to the BIM cloud end, the construction cost is assessed. With the method and system, the construction cost can be saved, construction progress is accelerated, and construction quality is promoted.

Methods and systems are described for predicting the likely causes of service outages using only time information, and for predicting and the likely costs of service outages. The likely causes are found by defining a narrow likely cause window around an outage based on service quality and / or service usage data, and correlating service events to the likely cause window in the time domain to find a probability distribution for the events. The likely costs are found by measuring usage loss and duration for a given point during an outage and using cost component functions of the time and usage to extrapolate over the outage. These cause and cost predictions supply service administrators with tools for making more informed decisions about allocation of resources in preventing and correcting service outages.

The discovery cost forecasting system uses incomplete information to generate forecasts of discovery expenses. Models are generated with different levels of granularity depending upon the available facts and user preferences. An expert provides real-time information to improve the accuracy of the predictions. A scenario analysis is generated that evaluates how changes to the parameters of the matter could affect the forecasted cost. An estimate is generated of the degree of advancement of the matter in its lifecycle as part of the cost forecast. The cost associated with each data source and custodian is tracked and reported. A configurable and extensible cost equation is defined. A dashboard user interface is generated to monitor the entire portfolio to extract easy to understand facts, trends, and early warning signs related to discovery cost management.

The embodiment of the invention discloses a cost prediction method based on a prediction model, DEVICE, SERVER AND STORAGE MEDIUM, The method comprises the following steps: receiving a cost predictionrequest sent by a terminal, wherein the cost prediction request carries patient information, the patient information comprises patient basic information and diagnosis and treatment information, the diagnosis and treatment information comprises disease name and treatment plan information corresponding to the disease name, and the patient basic information comprises region information and age information; Obtaining the first target information from the patient information according to an input item of the cost prediction model; inputting The first target information into a cost forecasting model for processing to obtain the forecasted cost information, and training the cost forecasting model according to the history forecasted cost information and the history first target information obtained from the history patient information; sending The predicted cost information to the terminal. By adopting the embodiment of the invention, the predicted cost information corresponding to the treatment scheme can be quickly determined, so as to provide a reference for judging whether the diagnosis and treatment cost is reasonable or not.

The invention discloses an energy saving expert system based on energy consumption device models and a control decision method. The system comprises a model simulation module, a cost forecasting module and an expert suggestion determining module, wherein the model simulation module is used for simulating a plurality of energy consumption device models according to real-time measured data and preset constraint conditions of a current energy consumption device, and obtaining a plurality of simulation results; the cost forecasting module is used for obtaining a forecast cost of each simulation result according to an objective function; and the expert suggestion determining module is used for determining expert suggestions on the current energy consumption device according to the forecast cost. According to the energy saving expert system, expert suggestions on a concrete energy consumption device are determined in combination with the real-time measured data and the existing energy consumption device models, so that the energy consumption device saves energy better and is safer; a control device in an energy management system is simple and easy to deploy and maintain, and plenty of costs are reduced while the reliability of the device is guaranteed.

The invention discloses an operation and maintenance detection cost prediction method for physical assets of a power grid based on big data, and the method comprises a multisource data collection platform, a data warehouse, a MapReduce model, and a data visualization display platform. An operation and maintenance detection cost prediction system for physical assets of the power grid employs the big data analysis technology, the ETL technology, the data warehouse technology and the data visualization technology, takes the whole-life-cycle of assets as the guiding thought, takes the asset unit information data in a PMS system and an ERP system and the operation and maintenance cost as the objects, builds a statistical relation of mass unstructured data through the analysis methods of clustering analysis, classification analysis and correlation analysis, and achieves the middle-term and long-term analysis prediction of the operation and maintenance cost scale and development trend of the physical assets of the power grid. The method achieves the high-efficiency integration of data of various types of business systems in the operation and maintenance management of the physical assets of the power grid, and improves the prediction accuracy of the operation and maintenance cost.

The invention belongs to the field of power transformation engineering cost prediction, and particularly relates to an improved firefly algorithm-based power transformation engineering cost predictionmethod for SVM optimization. For improving optimization performance of an FA to optimize parameters of an SVM prediction model, the invention provides the improved firefly algorithm-based power transformation engineering cost prediction method for the SVM optimization. The method mainly comprises three parts including data processing, parameter determination and cost prediction; specially, in theparameter determination part, a position updating formula of the FA is improved by adopting a Gauss disturbance technology based on a conventional FA to search for optimal parameters; and the methodenhances the capability of fireflies in escaping from local optimum and improves the optimization performance of the FA so as to optimize the parameters of the SVM prediction model. Through Schaffer function testing, the proposed Gauss disturbance FA has the advantages of high convergence speed, good search capability and the like, and can realize high-precision prediction of power transformationengineering cost level.

Determining if access should be granted to a data source. A method includes determining resource usage cost of performing an operation on a data source. The method further includes determining if the resource usage cost exceeds a predetermined threshold. When the resource usage cost exceeds a predetermined threshold, the operation is rejected.

Embodiments of the invention disclose an engineering project cost prediction method, apparatus and device, and a readable storage medium. The method comprises the steps of obtaining a BIM 5D management platform for calculating an engineering amount of an engineering project; inputting unit prices of labors, materials and machineries of a current market to a pre-built cost prediction model, therebyobtaining unit prices of the labors, the materials and the machineries when the engineering project is in different implementation stages, wherein the cost prediction model is obtained by learning and training sample data of multiple historical engineering projects by utilizing a rote learning algorithm; and according to the engineering amount and the unit prices of the labors, the materials andthe machineries in the corresponding implementation stages, calculating the cost of the engineering project, thereby realizing dynamic prediction of the cost of the engineering project. According to the technical scheme provided by the method, when the cost of the engineering project is calculated, the changes of the unit prices of the labors, the materials and the machineries along with the timeare taken into account; the deficiency of existing manufacturing cost prediction is made up for; the cost prediction accuracy and reliability of the engineering project are improved; and accurate costcontrol of the whole engineering project can be realized.

The invention relates to an engineering project cost prediction method based on a Markov grey model. The method comprises the following steps: acquiring an original data column X(0); predicting the gray scale of the original data column X(0); obtaining an absolute value data column epsilon(0) of the residual error; performing gray scale prediction on the residual absolute value data column epsilon(0); constructing a new equation in combination with gray prediction of the original data column X(0) and a gray prediction equation of the residual absolute value data column epsilon(0), and solvingto obtain a residual symbol function sgn(k) of gray prediction; and combining and constructing a final prediction equation, and solving the final prediction equation to obtain a prediction value of the original data column X(0). According to the invention, prediction of a set cost index can be realized; and the Markov model-based residual error correction model introduced into the model can well correct the prediction deviation of the original GM(1, 1) grey prediction model, higher prediction precision is realized, and the future cost of the project is effectively estimated, so that an effective reference basis is provided for the cost management decision in the construction process of the engineering project.

The invention discloses a power grid basic construction project cost prediction method based on a multi-core support vector regression. The method comprises the following steps: carrying out attribute pretreatment on descriptive data of a transmission and distribution project, and using a basic kernel function to calculate a multi-core matrix, wherein the attribute pretreatment comprises the steps of standardization of continuous variables and type conversion of enumerative variables; constructing a prediction model of the multi-core support vector regression, and optimizing a regression parameter of the prediction model and the weighing of the multi-core matrix; and utilizing the prediction model to carry out cost prediction by taking data obtained from the attribute pretreatment as a sample to be tested. According to the application of the method, the power grid basic construction project cost can be reasonably predicted so as to facilitate construction management personnel to improve the budget estimate making precision; and therefore, the basic construction investment is efficiently controlled.

The invention discloses a BIM project material and project cost statistical method. The method comprises the following steps: adding corresponding material attributes to each component of a three-dimensional model; uploading a historical engineering cost list to a material center; collecting engineering cost information; giving a weight value to each construction cost index in the construction cost information, and storing the weight values into a database; processing each engineering component in the database and then constructing a cost model; inputting each component attribute into a cost model to obtain a cost prediction value; and superposing the construction cost prediction values of the components to obtain the final project construction cost. According to the method, the cost modelis constructed in the historical engineering cost list of the material center, the similar cost model is identified by using the fuzzy identification technology and the three-dimensional model is input to obtain the specific cost of the three-dimensional model, the building scheme is modified at any time, the engineering quantity and the cost are output, the advantages and disadvantages of the schemes are compared, and the investment is saved; and the most beneficial and most economical scheme is selected.

The invention relates to a cost prediction method and apparatus for medical projects, computer equipment and a storage medium. The cost prediction method for medical projects includes the steps: obtaining a medical project to be predicted input by a user, determining a cost type of the medical project, and then inputting the medical project to be predicted into a cost prediction model matching thecost type as input data, wherein the cost prediction model is a deep learning model for performing machine learning on the cost information corresponding to medical projects of different cost types,and the cost information output by the cost prediction model can be obtained, and finally the cost of the medical project can be obtained according to the cost information. The cost prediction methodand apparatus for medical projects can simplify the steps for predicting the project cost of the medical project, can improve the efficiency of cost prediction, and at the same time can avoid the problem that the data is prone to error when the cost of the medical project is manually predicted, thus ensuring the reliability of the cost prediction.

The invention relates to a power grid technical renovation overhaul engineering cost management system and a control method thereof and solves a disadvantage of difficulty in effectively managing engineering cost in the prior art. The method comprises steps that projection division of a power grid technical renovation overhaul engineering project is determined; engineering work load list cost dataof different stages of one same historical engineering and of different engineerings at one same stage is analyzed; similarity matrix construction and similarity calculation are carried out; quantitative calculation and prediction are carried out; prediction cost is adjusted according to actual conditions till the prediction result is consistent with preset conditions of the power grid technicalrenovation overhaul engineering. The method is advantaged in that an engineering work load list valuation mode can be taken as a penetration point for cost prediction, rapid record of relevant data ofcost management of the power grid technical renovation overhaul engineering is realized, and work efficiency and accuracy of cost prediction are improved.

The invention discloses an improved substation engineering cost prediction method belonging to the technical field of substation engineering cost prediction. The method includes the steps of 1, selecting input index of substation engineering cost prediction and data preprocessing; 2, establishing a prediction model of optimized LSSVM base on that fireworks algorithm; 3, inputting the input index data set obtained in step 1 into the prediction model obtained in step 2 to obtain the prediction result. The invention optimizes the coefficient of the LSSVM model through the fireworks algorithm, andthe prediction result obtained is higher in prediction precision and stronger in robustness than the basic LSSVM model and the support vector machine model. The model provided by the invention can beapplied to the cost level of substation engineering projects of different regions, different voltage grades and different scales, and can obtain ideal prediction effect, and the model constructed bythe invention has strong adaptability and stability.

The invention discloses a power grid operation and maintenance cost prediction method and prediction system. The method comprises the following steps: acquiring basic data related to the power grid operation and maintenance cost, wherein the basic data comprises the electricity consumption of the whole society, the GDP, the power consumption structure, the power grid valid asset net value, power transformation capacity, the line length, and the operation and maintenance cost; inputting the basic data acquired in the above step into a pre-established power grid operation and maintenance cost prediction model to compute, and outputting the expected power grid operation and maintenance cost. The power grid operation and maintenance cost prediction method and prediction system are used for predicting the operation and maintenance cost of the regional power grid, and can provide scientific data support for the power transmission and distribution cost checking, the power grid project feasibility research and post project evaluation and like works.

The invention discloses KPCA-LA-RBM-based transmission and transformation project cost prediction method, and belongs to the electric digital data processing technology field. The method includes suchsteps as selecting and preprocessing data of each sample and obtaining key influencing factor set; the training set is composed of several groups of pre-processed sample data, and the training set isused to train KPCA-LA-RBM combination model; using the remaining data as the test set, according to the trained combination model, using the test set to predict the trained model, the final prediction results are obtained. The invention selects the first four principal components as input vectors of the combination model according to the principal component accumulative variance contribution rate, which improves the calculation efficiency of the model under the condition of guaranteeing the prediction accuracy. The combined model proposed by the invention can effectively reduce the error caused by a single model, improve the prediction accuracy, improve the generalization ability and robustness, and is suitable for the cost prediction of transmission and transformation projects.

A computer-implemented method for benchmarking, accounting, analysis, and cost forecasting is provided. In exemplary embodiments, the method may comprise, at a server having one or more processors and memory storing one or more programs for execution by the one or more processors: harvesting raw data from a plurality of sources; indexing the harvested data according to predetermined data categories relating to municipalities; organizing the data based on the indexed data and the geographic location associated with the data; storing the organized data in a database; and generating and displaying a profile page for a selected region based on the organized data stored in the database.

The embodiment of the invention discloses a task allocation method and device, electronic equipment and a computer readable storage medium. the task method comprises the steps: obtaining task-relatedtraining data, and carrying out the training according to the task-related training data, and obtaining a task execution cost prediction model; Obtaining related data of a to-be-distributed task, andinputting the related data into the task execution cost prediction model to obtain a predicted execution cost of the to-be-distributed task; And according to the predicted execution cost of the to-be-allocated task, allocating the to-be-allocated task. According to the technical scheme, the input of manpower can be greatly reduced, the working efficiency can be improved, the optimization of task distribution can be effectively ensured, the distribution risk can be reduced to the maximum extent, and meanwhile, the machine learning method is applicable to many scenes due to the fact that the training data is complete and the self-adaptive capacity is high.