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1101results about How to "Improve forecasting efficiency" patented technology

Attack-oriented network security situation prediction method, device and system

ActiveCN108494810ARealize dynamic associationIn line with the actual environmentData switching networksSecuring communicationCountermeasureAttack graph
The invention belongs to the technical field of network security and particularly relates to an attack-oriented network security situation prediction method, device and system. The method comprises the following steps: detecting and collecting alarm data and network environment operation and maintenance information in a network countermeasure environment, obtaining an element set required by network security situation prediction, wherein the element set comprises three types of information of an attacker, a defense party and a network environment; evaluating the attacker capability and the level of the defense party, establishing a dynamic Bayesian attack graph, and calculating an attack phase number and an attack state occurrence probability vector; and combining a vulnerability scoring standard and network asset information, and performing time-space dimension quantification on the network security situation value. According to the method, dynamic association of the situation elements of the defense party, the attacker, the environment information and the like is achieved, the actual environment of the network is better conformed to, the future situation and the attack occurrencetime can be accurately predicted, higher prediction efficiency is achieved, and storage scale and timeliness of network security situation awareness are optimized, so as to provide more effective guidance for network protection.
Owner:PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU

Automotive exhaust emission data fusion system

The invention discloses an automotive exhaust emission data fusion system. The automotive exhaust emission data fusion system comprises a roadside air pollutant concentration estimation module, a roadside air pollutant concentration prediction module, a city global atmospheric environment prediction module, an automotive exhaust emission factor estimation module and an automotive exhaust emission feather analysis module, wherein the five modules are used for respectively realizing different data analysis functions, and the different functions can be realized by virtue of the different modules; the modules can be independently used, or two or more modules can be combined for use, so as to realize the storage, analysis and fusion of automotive exhaust telemetering data, automotive attributes, driving working stations, detection time and meteorological condition data; and by combining with a vehicle-mounted diagnosis system database, a portable emission test system database, a vehicle inspection station offline database, a traffic information database and a geographic information database, automotive exhaust telemetering data is analyzed, and the highest discriminatory key indexes and statistical data are acquired, so that effective supports are provided for the formulation of relevant decisions of government departments.
Owner:UNIV OF SCI & TECH OF CHINA

Prediction device and prediction method for state of emergency topic

The invention relates to a prediction device and a prediction method for the state of an emergency topic. The prediction device and the prediction method are provided based on the research of a microblog topic production mechanism and the influence of opinion leaders of emergency on the state of the emergency topic. The prediction device is provided with four components of a text acquisition unit, a topic clustering unit, a topic state recognition unit and a topic state prediction unit, and the topic state is predicted through the participation state of the opinion leaders, so that the prediction method of the topic state is simplified, the prediction accuracy is higher, and a basis can be provided for monitoring the tendency of the emergency topic. Through the calculation method of topic attention provided by the invention, the computation complexity of a traditional topic attention method is reduced. Moreover, the numerical value prediction of the topic attention is replaced by adopting the prediction of the state of the topic, so that the prediction content is more reasonable, and the prediction accuracy is higher. Furthermore, a prediction method of a hidden Markov model is provided, the set of the opinion leaders and the model of topic prediction are constructed through incremental iteration, so that the prediction operation can be obviously simplified, and the prediction efficiency is improved.
Owner:BEIJING UNIV OF POSTS & TELECOMM

Method for compensating positioning errors of robot based on deep neural network

The invention discloses a method for compensating positioning errors of a robot based on a deep neural network. The method comprises the following steps: taking theoretical coordinate data of each sampling point and corresponding actual positioning errors as one group of sample data; constructing a robot positioning error prediction model, and training and testing the robot positioning error prediction model by adopting the sample data, wherein the robot positioning error prediction model is used for predicting positioning errors of an inputted target point to obtain a positioning error prediction value; and inputting theoretical pose data to the robot positioning error prediction model to obtain prediction errors and compensating and correcting target point coordinates. According to the method disclosed by the invention, the influence of the position and the pose of a positioning point on the positioning errors is comprehensively considered; and a complex mapping relation between theoretical pose and actual positioning errors of a robot is expressed by using the deep neural network to predict the positioning errors of the target point and compensate the errors. By use of the method disclosed by the invention, absolute positioning precision of the robot can be remarkably improved.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS

Driving energy consumption prediction system and method, storage medium and equipment

The invention discloses a driving energy consumption prediction system and method, a storage medium and equipment. The prediction method comprises the steps of obtaining historical working condition data of a planned driving route; constructing a training sample data set based on the historical working condition data; performing data training on the training sample data set, and establishing a vehicle speed feature BP neural network model and a driving energy consumption BP neural network model; acquiring real-time working condition information on the planned driving route; and inputting the real-time working condition information into a vehicle speed characteristic BP neural network model for prediction to obtain vehicle speed characteristic data of future driving, and then inputting thevehicle speed characteristic data into a driving energy consumption BP neural network model for prediction to obtain future driving energy consumption data so as to realize online prediction of driving energy consumption. According to the invention, online effective prediction of the driving energy consumption under the driving working conditions of different road environments and traffic states can be realized, and the efficiency of intelligent energy management of the vehicle is improved.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS

Load prediction method based on dynamic time warping and long-short time memory

The invention discloses a load prediction method based on dynamic time warping and long-short time memory, and the method comprises the following steps: S1, obtaining the basic data required for short-term load prediction of a user from a power system; S2, carrying out the clustering of users with similar power utilization behaviors through employing a dynamic time warping method according to thehistorical load data of the user; S3, performing pooling processing on the user data of the same category; S4, selecting training data, preprocessing the training data and using the preprocessed training data as input; and S5, constructing a short-term load prediction method based on the deep long-term and short-term memory recurrent neural network, and verifying the effectiveness. According to the method, the users with similar electricity consumption behaviors are clustered according to the characteristic of large cardinal number of the to-be-predicted users, so that the prediction efficiency is improved. Meanwhile, through pooling processing on the data in the same category, the diversity of the training data is increased, the short-term load prediction precision is improved, and certain engineering application significance is achieved.
Owner:NANJING UNIV OF POSTS & TELECOMM

Expressway traffic flow forecasting method based on time series

The invention discloses an expressway traffic flow forecasting method based on time series. The expressway traffic flow forecasting method includes the steps of selecting one time scale, and carrying out statistics to build the traffic flow time series Q=(x); setting the value range of the number p of autoregression items and the number q of moving average items according to the selected time scale; solving the number p of the autoregression items and the number q of the moving average items; fitting the optimal number p of the autoregression items and the optimal number q of the moving average items through the maximum likelihood estimation in cooperation with the traffic flow time series Q to obtain an optimal ARMA model, and obtaining weight parameters of historical measured values and weight parameters of historical error values; solving the traffic flow forecasting series (please see the specifications) under the different time scales. By means of the expressway traffic flow forecasting method, an obtained time series model can better meet the requirement for forecasting various kinds of flow of an expressway, and the forecasting universality is improved; operation is simple, the forecasting efficiency is improved, the forecasting speed is increased, and the engineering requirement of traffic forecasting of the expressway is met.
Owner:四川省交通科学研究所

Database based complex-contour aircraft distributed heat environment parameter prediction method

The invention discloses a database based complex-contour aircraft distributed heat environment parameter prediction method and belongs to the field of aircraft heat environment design. The method comprises the steps that an aircraft surface heat flow database is established, order reducing processing is conducted on the database by utilizing a POD method to obtain orthogonal basis vectors of the database, and aircraft surface heat environment parameters can be predicted along a ballistic trajectory by combining with a corresponding basic coefficient interpolation method. The method can really reflect the space distribution characteristics and interference characteristics of the aerodynamic heat environment for all points on the surface of a complex-contour aircraft. Value result comparison shows that the method can remarkably improve the computational efficiency, and prediction precision is not lost. A surface distributed heat flow is provided for calculation of a heat proof temperature field through the points along the ballistic trajectory, more exquisite temperature distribution can be obtained, and accordingly the design level of a whole heat preventing and insulation system is improved.
Owner:BEIJING LINJIN SPACE AIRCRAFT SYST ENG INST +1

Medium and long term hydrologic forecasting method based on empirical mode decomposition

The invention discloses a medium and long term hydrologic forecasting method based on empirical mode decomposition. The medium and long term hydrologic forecasting method based on empirical mode decomposition comprises the steps that firstly, a hydrologic forecasting model is built according to the following procedures of (101) empirical mode decomposition, wherein empirical mode decomposition is carried out on a hydrologic time sequence s(t) of a forecast drainage basin, (102) kernel principle component analysis, wherein kernel principle component analysis is carried out on n intrinsic mode function components Fj obtained through empirical mode decomposition and a trend item rn, and p main components F'k are extracted, (103) building of a training sample set, wherein the training sample set is built according to the extracted p main components F'k, (104) building of a support vector machine model, and (105) training of the support vector machine model; secondly, annual runoff data of the years needing forecasting are forecast through the built hydrologic forecasting model. The medium and long term hydrologic forecasting method based on empirical mode decomposition is simple in step, convenient to realize, easy and convenient to operate, good in use effect and capable of effectively resolving the problem of low forecasting accuracy of an existing hydrologic forecasting method.
Owner:CHANGAN UNIV +2

Network pharmacology method used for finding active ingredients of traditional Chinese medicine and effect targets thereof

ActiveCN103150490ARapid modeling methodFast prediction methodSpecial data processing applicationsNODALAdditive ingredient
The invention discloses a network pharmacology method used for finding active ingredients of traditional Chinese medicine and effect targets thereof. The network pharmacology method comprises the steps as follows: chemical compounds and proteins that can build training sets are collected, and characteristic descriptors are calculated according to molecular structures of the chemical compounds and amino acid sequences of the proteins; the training sets are built based on the characteristic descriptors of the chemical compounds and the proteins, predictive models relative to the interaction of the chemical compounds and the proteins are built according to a machine learning method; the known chemical compound ingredients of the traditional Chinese medicine needing to be forecasted, the characteristic descriptors are calculated, and forecast sets are built; the built predictive models are utilized to forecast the forecast sets, and the chemical compounds and the proteins with the forecasted interaction results greater than a given threshold are used for building a network; and the chemical compounds and the proteins with larger network node connection number are the potential active ingredients and the potential effect targets. The network pharmacology method can improve the finding accuracy rate and finding efficiency of the active ingredients of the traditional Chinese medicine and the effect targets.
Owner:ZHEJIANG UNIV

Support vector machine (SVM) based prediction method for the degradation tendency of super-capacitor capacitance value

The invention discloses a support vector machine (SVM) based prediction method for the degradation tendency of super-capacitor capacitance value. The prediction method utilizes the regression function of the support vector machine to predict the degradation tendency of the super capacitor capacitance value and comprises: 1) pre-processing the input value and the output value; 2) carrying out trainings to the training set data for a regression estimation function; 3) using the particle swarm optimization algorithm to automatically optimize the relevant parameters of the support vector machine; 4) according to the optimization result, configuring the corresponding parameter values of the support vector machine; substituting the training set data into a correlation vector machine model to obtain a regression prediction model for the degradation tendency of the capacitance value; and 5) substituting the training set data into the regression prediction model to obtain the degradation tendency of the capacitance value. According to the invention, it is possible to conduct online prediction to the degradation tendency of the capacitance value. Through the introduction of a particle swarm optimization algorithm to modify the parameter optimization method, the prediction efficiency and accuracy of the algorithm are increased so that it can be applied in a larger scope.
Owner:DALIAN UNIV OF TECH

Human movement mode speculation model based on variation track context perception, training method and speculation method

The invention discloses a human movement mode speculation model, a training method and a speculation method based on variation track context perception. The method comprises the following steps: firstly, respectively obtaining a track semantic vector and a variation hidden variable through a circular track encoder and a variation track encoder; and obtaining an attention vector of the track basedon a variational attention mechanism, cascading the attention vector with the variational hidden variable so as to reconstruct input data of a decoder, and finally restoring a previous track and generating a prediction track according to an output semantic vector of the decoder. According to the invention, the frame of the encoder-decoder solves the problem of track context learning. Two sub-tasks, namely track recovery and track prediction, speculated by a human movement mode are completed; not only can the probability density be estimated and the lower limit of the data possibility be optimized, but also the sequence and time characteristics of human mobility can be captured, the problem of track speculation according to track context perception is effectively solved, and the effect is improved for speculation of a human movement mode.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA

Soil moisture content prediction method based on LSTM deep learning model

The invention discloses a soil moisture content prediction method based on an LSTM deep learning model. The soil moisture content prediction method comprises the following steps: firstly, collecting soil physicochemical and meteorological data of a target farmland within a period of time; preprocessing the collected data, and dividing the preprocessed data into a training sample set and a test sample set; constructing an LSTM deep learning model; training the LSTM deep learning model through the training sample set to obtain a parameter-adjusted LSTM deep learning model, verifying the parameter-adjusted LSTM deep learning model through the test sample set, and finally taking the verified LSTM deep learning model as a soil moisture content prediction model; inputting the collected data intoa soil moisture content prediction model, and finally outputting a soil moisture content prediction result at a future moment through the soil moisture content prediction model. According to the method, the soil moisture content is forecasted by using a deep learning method, manpower and material resources are saved, the influence of early-stage data on a later-stage result can be truly reflected, and time sequence characteristics are fully embodied.
Owner:ANHUI AGRICULTURAL UNIVERSITY

LSSVM annual electricity consumption prediction method based on ant lion optimization

The present invention relates to a kind of LSSVM annual power consumption prediction method based on antlion optimization, the annual power consumption prediction method comprises the following steps: determine the input variable of least square support vector machine (Least Square Support Vector Machines, LSSVM) prediction model; Initialize the antlion optimization algorithm; calculate the fitness value of the initial antlion to obtain the initial elite antlion; update the position of the ant, calculate the fitness value of the current antlion, and compare it with the fitness value of the corresponding antlion to determine whether to update the antlion Lion position; compare the fitness value of the antlion after the updated position with the fitness value of the previous elite antlion one by one, keep the antlion corresponding to the smaller fitness value, and obtain the elite antlion of this iteration; judge whether The maximum number of iterations is reached, if yes, then output the location of the elite antlion and the corresponding predicted value of annual electricity consumption, if no, continue to iterate. Compared with the prior art, the present invention has the advantages of higher prediction accuracy and higher prediction efficiency.
Owner:SHANGHAI UNIVERSITY OF ELECTRIC POWER

Method for bedrock crack prediction based on multi-attribute fusion

Provided is a method for bedrock crack prediction based on multi-attribute fusion. The method includes following steps: firstly, searching and performing statistics on data and information of bedrocksin a research region; secondly, developing calibration of a basement layer position, performing a seismic interpretation work on the boundary of the top surface of the basement of the research region, and establishing a bedrock stratigraphic framework of the research region through the interpretation work; thirdly, calculating an AFE fracture prediction attribute; fourthly, calculating a frequency division coherent attribute; fifthly, calculating an ant tracking attribute; sixthly, performing datamation on an obtained basement lithologic distribution map of the region through identification of the basement lithology, calculating different lithologic crack influence factors epsilon according to statistical results, and generating an influence factor epsilon planar data volume; and seventhly, performing an attribute fusion calculation based on the well attribute. The efficiency for basement crack prediction is substantially improved, the crack distribution characteristics can be accurately determined, bedrock buried-hill exploration target regions are further figured out, and an important practical significance is attached to acceleration of oil gas productivity construction.
Owner:CHINA PETROLEUM & CHEM CORP +1
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