48 results about "History of computing" patented technology

A method of generating trading strategies, comprising the steps of: providing a system for generating trading strategies for dividend-based stocks, loading a first database containing basic information of stock and a second database containing financial information of stock into the system, computing maximum trading days of the stock, mapping of the first database and second database of the stock to a trading day and an alternative trading day, computing maximum number of trading pairs based on the maximum trading days of the stock, if trading long, computing historical returns for all trading pairs, computing buy / sell differences and actual trading dates and price of all trading pairs, if trading short, computing historical returns for all trading pairs, compute short / cover price differences and actual trading dates and price of all trading pairs, ranking a list of trading pairs based on one or more corresponding ranking criteria for trading long and trading short.

The present invention provides a parking lot available parking space prediction method and system. The method comprises: obtaining the historical parking data of a target parking lot at each sampling moment in a sampling period; calculating a correlation coefficient between any two parking data samples in the historical parking data; classifying the historical parking data according to the presetting correlation threshold value and the correlation coefficient, and obtaining at least one parking data sample subset; performing smoothing processing of each parking data sample subset, and obtaining the average parking number of vehicles of different parking data samples of each parking data sample subset in each sampling moment in one day; establishing a stationary Poisson process model between all adjacent sample points according to the average parking number of the vehicles and the collection moment information of the historical parking data, and forming a non-stationary Poisson process model corresponding to each parking data sample subset; and estimating the variable parking spaces of the target parking lot at the moment to be predicated according to the non-stationary Poisson process model corresponding to each parking data sample subset.

The invention relates to a modeling method for a parallel smart case recommendation model. The method comprises the following steps of obtaining existing patient cases from an electronic case database, carrying out denoising, clustering and word segmentation on the patient cases, and establishing a patient case corpus database; defining that TFIDFi, j shows the importance degree of a word or an expression in a case of the patient case corpus database, establishing an LSI vector space model according to the TFIDFi, j, and moreover, establishing a BOW word bag model according to all words and expressions in the patient case corpus database; calculating history case vectors and to-be-processed case vectors in the patient case corpus database through utilization of the LSI vector space model and the BOW word bag model; calculating cosine similarity among the history patient cases and storing the cosine similarity; and calculating the cosine similarity between the to-be-processed case vectors and the history patient case vectors, and searching similar cases of to-be-processed cases according to the cosine similarity. The model established through adoption of the method provided by the invention is high in accuracy and low in error. A recommendation result is high in quality.

A system for a vehicle navigation accurately calculates travel times between locations and serves a vehicle driver request for the shortest time route. A navigation server compiles data of common road segments between two locations that are contained in route requests and subsequently calculates travel time between the two locations. When subsequent route requests contain further information pertaining to the same locations, the navigation server updates the travel time. Consequently, the navigation server can calculate the travel times of different routes from the same source vehicle location and destination location. When a vehicle driver requests directions from the vehicle's current location to a destination, the navigation server then provides directions for the shortest time route to the vehicle driver.

A method of valuing real estate properties includes the steps of calculating a premium or discount to rental parity for a historically stable time period and the current time period. Another method of timing a real estate market is also disclosed. Another method of searching for relevant property based on the personal income and / or expense of the buyer is also disclosed.

The invention discloses an environment monitoring platform based on mobile internet and a monitoring method. The environment monitoring platform and the monitoring method improve accuracy, reliability and instantaneity of environment monitoring by fully taking advantages of the mobile internet technology and have the advantages of 1) utilizing the latest mobile internet technology, connecting a lower computer with a server and connecting the server with a monitoring terminal through mobile internet, simplifying deployment topology, saving cost, simultaneously being convenient for user monitoring, supporting any smart phones installed with browsers, and solving the problem that instructions cannot be issued when the internet protocol (IP) address of the lower computer is flexible; 2) combining monitoring modes of real-time remote inquiry, server routing inspection, lower computer routing inspection and real-time report in complementary mode, ensuring instantaneity and stability of the monitoring and avoiding the phenomenon of monitoring failure caused by one mode; 3) concluding attributes of a group of monitoring objects, being capable of fast introducing a new monitoring object through the attribute definition, building a data structure through the monitoring objects, being capable of obtaining comparison results of unstable data and historical data in real time without recomputing the historical data during the comparison, and ensuring instantaneity of the response; 4) adopting the server pushing technology between the server and a user terminal, keeping long-time connection between the server and the client side, enabling the client side to display the latest monitoring data once the monitoring data are updated, and having instantaneity compared with the conventional monitoring system monitoring through a real-time polling data base; and 5) enabling the platform to be capable of adopting an Saas mode, having generality and supporting environment monitoring of different industries.

The invention provides a computer cluster performance index detection method, electronic equipment and a storage medium. The computer cluster performance index detection method includes the steps of extracting performance time sequence data with periodic forms in a certain period of time from a historical database; conducting modelling on the performance time sequence data, and determining a timesequence model; calculating a fitting error of a preset initial step length of historical data according to the time sequence model; predicting a threshold interval of a preset future step length according to the fitting error and preset reliability; detecting whether or not an actual value corresponding to the preset future step length of a computer cluster performance index is located in the threshold interval, determining that the index is normal if yes, and determining that the index is abnormal if not. According to the computer cluster performance index detection method, the electronic equipment and the storage medium, the fitting error of the corresponding step length of the historical data is automatically calculated according to the predicted step length, then the prediction interval is determined according to the error, a more reasonable threshold range is conveniently designed for the prediction interval, and the missing alarm rate or false alarm rate of abnormality detectionis reduced.

The invention discloses a whole-network load prediction method based on local load predicted value comprehensive evaluation. The whole-network load prediction method is characterized in that historical data in a recent sample period are obtained and used as a historical data sample space, then the average proportionality coefficient of each region at a time point t in the historical data sample space is calculated, the proportionality coefficient of each region at the same time point t on a to-be-predicted day is predicted, a multi-index evaluation system of the time point t is built, a comprehensive evaluation index of the time point t is built according to the multi-index evaluation system, q regions with higher priorities at the time point t are selected by means of the comprehensive evaluation index, the selected q regions are used for predicting whole-network system loads at the time point t respectively, the optimal comprehensive models of q different predicted values at the time point t are built, final predicted results of the whole-network system loads are obtained by conducting solving, the optimal comprehensive models are built for whole-day T time points of the to-be-predicted day respectively, and a whole-day load prediction sequence is obtained. The whole-network load prediction method can improve the accuracy of short-term load prediction of a power system.

Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.

The invention discloses a photovoltaic power generation power prediction method based on support vector machine regression, and the method comprises the steps: firstly, obtaining the historical outputdata and numerical weather forecast data of a target station; Screening out meteorological factors with high correlation from the meteorological factors; Secondly, preprocessing the historical data set, selecting appropriate input parameters, and performing data normalization to construct an input vector of a support vector machine; Calculating correlation degrees between the historical data setand four typical days day by day by using a grey correlation coefficient method; Clustering correlation degree calculation results so as to divide the historical data into four training sets accordingto weather types; Carrying out training modeling on the classified historical samples by adopting a support vector machine regression algorithm to obtain a prediction model; Determining the weather type of the to-be-predicted day through correlation calculation, and calling a corresponding prediction model; And finally, prediction day value weather forecast parameters are input, and a power prediction result is obtained based on a support vector machine regression algorithm and a prediction model.

The invention provides a context routing processing method, network element equipment and a network server. The process context routing method includes: receiving context access information reported by context routing devices in a local area by the network server; computing frequency of historic context access according to context addressing in the context access information, and adjusting a context routing list according to the frequency; and issuing the context routing table adjusted to all context routing devices in the local area. By the context routing processing method, conflict between context flow transmission efficiency and cache hit ratio can be eliminated, so that higher integral transmission performance can be realize by least cost and cache hit ratio is increased.

The invention relates to a method for monitoring faults in a smelting process of a multimode magnesia electrical smelting furnace. According to the method, a historical normal data set of different working modes in the smelting process of the multimode magnesia electrical smelting furnace is obtained; a subspace separation model based on a mass nucleus locally linear embedding method is created; T2 statistic control limit of global public subspace of historical normal data and SPE (squared prediction error) statistic control limit of local special subspace of each of different working modes are calculated; a new data set in a current working mode is acquired in real time; T2 statistic of global public subspace of new data and SPE statistic of corresponding local special subspace in the current working mode are calculated; and if the T2 statistic of the global public subspace of the new data exceeds the T2 statistic control limit of the global public subspace of the historical normal data, or the SPE statistic of the corresponding local special subspace of the new data exceeds the SPE statistic control limit of the local special subspace of the historical normal data in the working mode, the current working mode in the smelting process of the multimode magnesia electrical smelting furnace has possibility of fault occurrence.

The invention discloses an information recommendation method and device, relates to the fields of computer technology and artificial intelligence and improves the accuracy for recommending information to users. The method includes calculating the user similarity of optional two historical user identifiers in historical user identifiers according to network service evaluation records fed back by historical users; acquiring a target user identifier; determining a historical user identifier group has high similarity with the target user identifier according to the target user identifier and the calculated user similarity; determining target information in the network service evaluation records corresponding to the historical user identifier group according to the historical user identifier group, and recommending the target information to a target user corresponding to the target user identifier. The method is adaptive to information recommendation.

The invention discloses a wind / solar power prediction method with a variable prediction resolution. The method comprises the following steps: firstly obtaining the resolution of a needed prediction data according to a wind / solar energy management system, computing the similarity of historic weather data and future weather data in variation tendency by extracting historic weather data of a plurality of days before the current prediction moment under the resolution, computing the weather data variation tendency weight in the next 24 hours under the prediction resolution, computing the prediction value variation tendency measures of the weather data according to the weight; and finally predicting the weather data prediction value in the next 24 hours under the needed prediction resolution. Therefore, the power prediction results in a short time interval at different power prediction resolution conditions can be obtained according to the numerical weather prediction data with low resolution and the historic data to provide powerful data support for the energy management of a wind / solar hybrid generation system; the method provided by the invention has important significance for guaranteeing the stabilization of the output power of the wind / solar hybrid generation system.

The invention is applicable to the technical field of computers, and provides an article recommendation method and a device. The method comprises the following steps of according to the similarity between a scored article and a to-be-scored article in historical scoring data, calculating the influence factor of the historical scoring data on the predicted score of the to-be-scored article; inputting the calculated influence factor into a pre-established restricted boltzmann machine model; calculating the predicted score of a user on each to-be-recommended article through the restricted boltzmann machine model; generating a recommendation list; and outputting recommended articles to the user according to the generated recommendation list. In this way, the cold start problem during the new article recommending process can be solved. The recommendation accuracy is improved.

The invention provides a trajectory-data-based method and system for recommending a taxi cruising path. The method comprises: (1) network data are initialized and regional grid division is carried out; (2) according to historical trajectory data, a historical traffic charge is calculated; (3) with combination of real-time trajectory data, a traffic charge is calculated and updated; (4) when a taxi arrives at an intersection, a sub regional traffic electric field force applied on the taxi is calculated by using the traffic charge obtained at the step (3) based on an urban traffic Coulomb law, a road network data base is inquired to obtain all road sections of the current intersection, and one road section with the smallest included angle with the traffic electric field force direction is used as a recommended road section; and (5) during the driving process at the recommended road section, if the taxis does not pick up any passenger or the passenger gets off the taxi, the step (4) is carried out; and if the taxi picks up a passenger, recommendation is stopped temporarily. According to the invention, the method has a clear idea and the effect is obvious. The empty driving cruising of the taxi can be reduced and thus the earnings of the taxi driver can be increased; and the urban traffic efficiency can be improved.

A method and computer-implemented method for loan consideration including the steps of: (a) obtaining financial data regarding a loan requester from a plurality of digital media sources, the financial data including tax data; (b) analyzing the financial data to make an affordability and risk assessment (AARA) for granting a loan to the loan requester, the AARA including calculating, at least from the tax data, a forecasted amount of unencumbered capital to be available over at least a portion of a term of said loan (i.e. by calculating an amount of historical unencumbered cash flow, which is then forecasted by the system into the future) after capital expenditure is estimated and current outstanding loan / debt repayments are deducted from the estimate; and (c) deciding whether to grant the loan based on the AARA.

The invention relates to artificial intelligence, and provides a data product recommendation method and device, a computer device and a storage medium. The method comprises the steps of receiving a data product recommendation instruction, wherein the data product recommendation instruction carries a target identifier and target customer group information corresponding to the target identifier; establishing a target feature matrix according to the target customer group information; obtaining a historical feature matrix corresponding to each historical target identifier, calculating the similarity between the historical feature matrix and the target feature matrix, and determining a historical target identifier corresponding to the target identifier according to the similarity; and obtainingdata product information corresponding to the historical target identifier, and sending the data product information to a target terminal corresponding to the target identifier. By adopting the method, the data product recommendation efficiency and pertinence can be improved.

The invention discloses an in-service intelligent electric energy meter batch fault early warning method and system. The method comprises the steps of 1, dividing all in-service intelligent electric energy meters into running batches according to batch dividing rules in batches; 2, processing verification inspection data and running data of the intelligent electric energy meters; 3, calculating the historical fault rate and the on-site inspection failure rate according to a statistical period, taking the historical fault rate and the on-site inspection failure rate as batch fault early warningresearch and judgment trigger conditions, if a set threshold is reached or exceeded, entering the step 4, and if the set threshold is not reached, returning to the step 2; 4, outputting a batch faultjudgment result; 5, judging whether the running batches meet a batch fault judgment condition or not, if yes, entering the step 6, and if not, returning to the step 2; and 6, generating an intelligent electric energy meter running batch fault disposal scheme. The method has the advantages that the workload of verification personnel is reduced; the resource waste is avoided, and the investment isreduced; and on the premise that the metering accuracy and reliability of the intelligent electric energy meters are ensured, the resources are ensured to be saved and efficiently utilized.

The invention provides a text filtering method based on a keyword weight value. The method comprises the following steps that the weight value of a keyword is calculated; a text is filtered based on the calculated weight value of the keyword; the process of calculating the weight value of the keyword comprises the steps of judging whether the keyword is a brand-new keyword or not, calculating the number of accurate judgment data and the number of wrong judgment data in historical judgment data and the number of accurate judgment data including the keyword and the number of wrong judgment data including the keyword if the keyword is the brand-new keyword, and calculating the weight value of the keyword. The invention further provides a text filtering system based on the keyword weight value.

The invention discloses a trust management method based on a nested game in a center based cognitive wireless network. The method comprises the steps of establishing a nested game model, perceiving a spectral state, making a secondary user select a perception stage strategy and upload the perception data, making a data center fuse the perception data, making the secondary user select a transmission stage strategy, selecting a sliding window value, calculating a historical credit value and the credit value of this time based on the strategy, calculating utility functions of the first and second stages, optimizing the utility functions based on the game theory to solve the optimal strategy, updating trust function values, and distributing the spectrum based on the ranking of the trust values. The method aims at the buildup of the whole cognitive cycle, and uses the nested game theory and the marginal utility theory to be capable of effectively resisting malicious attacks. The cognitive process is classified into the perception stage and the data transmission stage. A secondary user can assess the credit value in the strategies in different periods of time. Secondary users game each other to acquire the spectrum, eliminate the malicious users and make the whole system tend to be better.

Embodiments for efficiently computing complex statistics from historical time series data are provided. A hierarchical summarization method includes receiving at least one stream of data and creating data blocks from the at least one stream of data. In another embodiment, a method for computing statistics for historical data includes accessing at least one online stream of historical data, the online stream of historical data including metadata, and creating data blocks from the at least one online stream of historical data. Each data block includes a pair of timestamps indicating a sampling start time and a sampling end time, a number of data samples spanned by the data block, a SUM(X) statistic, a SUM(XX) statistic, and a SUM(XY) statistic computed for the data samples spanned by the data block. Other methods are also presented, such as methods for efficiently and accurately calculating statistical queries regarding historical data for arbitrary time ranges, among others.

Embodiments of the present invention disclose a method and a system for processing historical performance data of a storage device based on a box graph. The method comprises: obtaining historical performance data information generated by a storage device; calculating the quartile of the historical performance data by using a box graph theory, and obtaining an outlier; and taking a calculation result as a data sample for storage. According to the embodiments of the present invention, the quartile of the historical performance data can be calculated by using the box graph theory to obtain the outlier, so that the defect of the lack of analysis on the outlier in the prior art is overcome, the accuracy of the historical performance data analysis is improved, and according to the analysis result, the current status of the storage device can be accurately mastered, and prediction of the future operation status of the storage device can be facilitated.

The embodiment of the invention provides learning effect prediction method and system, and relates to the technical field of education informatization. The method comprises the steps that expression data of a learner in the current stage are acquired, the learning state of the learner in the current stage and the total learning state recording time are analyzed according to the expression data ofthe current stage, and the types of the learning state comprise the positive learning state and the negative learning state; the positive learning state parameters of the current stage is acquired according to the learning state of the current stage and the learning state record total time; the learning effect of the next stage is predicted by using the positive learning state parameter of the current stage and a preset learning effect prediction model, and the historical learning ability index and a learning ability index of the current stage are calculated. The method and system solve the problems that an existing learning effect prediction method is high in subjectivity and inaccurate in prediction result.

A method for assessing underwriting and distribution risks associated with a portfolio of subordinate debt is provided. The method is performed using a computer system coupled to a database. The method includes storing in the database historical bond issue data for a period of time preceding and proceeding at least one historical liquidity event and generating a plurality of simulated subordinate debt warehouses using the computer and the historical bond issue data stored in the database. The method also includes calculating a historical loss distribution based on the plurality of simulated subordinate debt warehouses generated. The method also includes determining a value at risk for a portfolio of subordinate debt resulting from a potential liquidity event by applying the historical loss distribution to the portfolio of subordinate debt.

The invention discloses a regional meteorological condition similarity-based large power network load prediction method. The method comprises the steps of firstly obtaining load prediction values of regions in a to-be-predicted day and a load and meteorological history data of a sample space recently; aggregating the regions with similar meteorological conditions according to a probability distance-based synchronous back-substitution elimination technology, dividing an aggregated region into q sub-regions with relatively large meteorological condition difference, then calculating an average proportional coefficient of the q sub-regions at a t moment point in a historical data sample space, and predicting proportional coefficients of the sub-regions at the same t moment point in the to-be-predicted day; and predicting a whole network system load at the t moment point by using the q sub-regions, building an optimal comprehensive model at the t moment point for q different prediction values, performing solving to obtain a final prediction result of the whole network system load at the t moment point, and building optimal comprehensive models for T moment points in the whole to-be-predicted day, thereby obtaining a whole day load prediction sequence. According to the method, the short-term load prediction accuracy of a power system can be improved.

The invention discloses a GCM correction method of considering day-by-day data fluctuation features. Months are used as calculation units to extract day-by-day actually-measured data of a historical period of a certain meteorological factor, day-by-day data of GCM of the same historical period and to-be-corrected day-by-day data of the GCM of a future period; multi-year monthly average values of the historical actually-measured data and the historical GCM data are calculated; average-value correction factors are constructed; thus day-by-day average-value correction is carried out on the futuremeteorological data; variance correction factors are constructed; and variance correction is carried out on the future meteorological data after average-value correction, and variance after correction is used as constraint conditions to obtain a new future GCM meteorological-sequence. According to the method, consistency of distribution features of the future GCM data and distribution features ofthe actually-measured data is considered, deviation correction is carried out on the future GCM data, and thus results obtained by simulation of a model adopting the GCM data are enabled to be more accurate.