52results about How to "Reduce decision risk" patented technology

The invention discloses a multisource information fusion method in an evidence high-conflict environment, which comprises the following steps of: determining the mutual support degree of evidences according to evidence distances, further calculating the relative discount factor of each evidence by taking a characteristic vector corresponding to the maximum characteristic value of a evidence support degree matrix pattern as a weighting vector of the evidence, wherein the evidence with the maximum weighing coefficient is a key evidence; and discounting each evidence information, and fusing the information of multisource sensor data by using the D-S (Dempster / Shafer) rule so as to increase the accuracy and precision of a monitoring proposition judgment under an evidence high-conflict status.The method can effectively eliminate the uncertainties of multisource information, increase the accuracy of information evaluation and overcome the limitation that correct evaluation results can not be acquired by using the traditional D-S evidence theory under the evidence high-conflict status, and has the advantages of simple algorithm, high convergence rate and the like.

The invention discloses a method for optimizing demand-side time-of-use power price based on photovoltaic grid-connected uncertainty. The method comprises the following steps: obtaining the daily load data after distributed energy is connected, and determining load peak-valley periods according to a fuzzy membership function; determining the objective function and the constraint condition of the optimization method of the demand-side peak-valley time-of-use power price in an operation cycle, and creating a peak-valley time-of-use power price optimizing model; by using a chance constraint theory, converting power balance constraint into deterministic equality constraint; and obtaining optimal peak-valley time-of-use power price by using a particle swarm optimization algorithm. The method provided by the present invention, based on peak shaving and load shifting adjustment of a power distribution network demand side according to the peak-valley time-of-use price optimization model, takes account of the connection of distributed power. At the same time, the optimization method adopts the chance constraint theory to solve the problem of prediction uncertainty of distributed power grid-connected power, reduces the decision risk caused by uncertainty and improves the pricing rationality of peak-valley time-of-use price.

The invention discloses a coronary artery virtual stent implantation method and system based on haemodynamics analysis. The coronary artery virtual stent implantation method comprises the steps that CTA image data of a patient are read, plaque is identified, a vascular three-dimensional image model is constructed, the plaque is removed, and the position of the plaque is recorded; the physiological parameters of the patient is received, a CFD boundary condition is set, and CFD calculating is conducted; according to a three-dimensional vascular form, a plaque characteristic and FFR values throughout blood vessels, a lesion is identified; according to the identified lesion, a stent specification database and a virtual stent implantation strategy, an implantation scheme is generated, and a new vascular three-dimensional geometric model is generated; under the new three-dimensional geometric model, fluid calculation is conducted again, according to a preset selecting standard, an optimal stent implantation scheme is output. According to the coronary artery virtual stent implantation method and system, blood vessel stenosis degree calculating, stent implantation strategy generating and quantitative evaluating of the virtual stent implantation effect are completed automatically, the operation scheme is planned accurately, the doctor decision efficiency is improved effectively, and the risk of dependence on the artificial judgment is reduced.

The invention relates to a distributed online monitoring system and method for an electric energy metering device. The distributed online monitoring system for the electric energy metering device at least comprises a monitoring subsystem (4) for the electric energy metering device in a transformer substation, a customer electric energy meter centralized reading motoring subsystem (9), a monitoring unit (10) for the electric energy metering device in a metering point and a monitoring center (7) for the electric energy metering device in a power supply company. Data abnormality and fault early-warning, error alarming and inspection and verification request judgment are performed through an on-site monitoring device; relevant power grid event information is acquired through a master monitoring station (6) for the electric energy metering device in the transformer substation and the monitoring center (7) for the electric energy metering device in the power supply company to be used for assisting in decision making; finally, the decision is made through business personnel. The traditional regular inspection and verification mode is broken, and the inspection, verification or maintenance efficiency is high. The communication burden on a monitoring network is reduced, the requirements for a communication environment are lowered, data amount of the master monitoring station (6) for the electric energy metering device in the transformer substation and the monitoring center (7) for the electric energy metering device in the power supply company is reduced, and the operation efficiency is higher.

The invention provides a modeling method for a production scheduling problem distribution set robust model, which belongs to the field of production scheduling and operations research. A distributed set robust optimization method based on an uncertain distribution function set is adopted to model the production scheduling problem, the model is composed of an objective function and constraint conditions, the objective of the model is to search for an optimal robust scheduling sequence, and thus, in a condition in which the total flow time of the sequence obeys the worst distribution in the processing time, the minimum condition risk value is realized. In the case of solution, the model is decomposed to an assignment subproblem and an integer two-order cone programming subproblem, and a minimum average processing time priority criterion and two Cauchy relaxation algorithms designed by the invention are adopted to optimize the model and obtain the optimal solution. The distribution set robust optimization method is applied to the production scheduling problem, the method is more applicable to the actual production condition compared with the existing robust modeling method, and in a condition of ensuring the system performance, a decision risk is reduced.

The invention relates to an unmanned plane and barrier data fusion method. The method is characterized by comprising steps that data acquired by each sensor is processed by a data fusion layer; millimeter wave radar sensor output data is a relative distance R1 between an unmanned plane and a barrier, the relatively speed V1 and angles between the barrier and a radar normal, and an azimuth theta1 and a pitch angle psi1 are comprised; a relative distance R2 between the unmanned plane and the barrier is inputted by an ultrasonic radar sensor; an object area S, an azimuth theta2 and a relative distance R3 are outputted by a binocular vision sensor; and a height value R4 between the unmanned plane and the ground is outputted by a radar height sensor. The method is advantaged in that incompletedata of local environments provided by same or different types of sensors at different positions are fused, possible redundancy among the sensors and contradiction data are eliminated, complementationis carried out, and uncertainty is reduced.

The invention belongs to the technical field of information processing and particularly relates to a marine environment safety assessment method based on the D-S evidence theory. According to the marine environment safety assessment method, according to the D-S evidence theory, multiple marine environment element data are fused, so that marine environment safety assessment is achieved. The marine environment safety assessment method comprises the steps that (1) a computer obtains marine environment element values, and the basic probability assignment of each marine environment element is output through calculation; (2) the computer conducts fusion on the basic probability assignment obtained in the step (1) according to the D-S evidence theory fusion rule, and the fused basic probability assignment is output; (3) the computer judges the fused basic probability assignment according to the decision rule, and then the marine environment safety elevation result is output. By the adoption of the marine environment safety assessment method based on the D-S evidence theory, marine environment safety assessment is effectively achieved, reasonable decision support can be provided for a decider, and the safety of a ship sailing on the sea and the economical efficiency of the ship are improved.

The present invention discloses a dimensionless index based fault diagnosis method for rotating machinery. The method comprises the following steps: (1) acquiring fault type signal data; (2) performing dimensionless index calculation; (3) performing sample processing to form a data set; (4) using a Kolmogorov-Smirnov test method, and using a kstest2 function in matlab to acqurie similarity between a to-be-tested sample and the known data set, so as to obtain a basic belief function; (5) performing corrected D-S data fusion by using an evidence combination method based on a weight coefficient and collision probability redistribution; and (6) analyzing a fusion result and making a decision. According to the technical scheme provided by the present invention, by using the dimensionless index, K-S and D-S combined rotating machinery fault evidence combination diagnosis method, a large number of fault signal characteristic values can be extracted; by using the K-S method, the rotating machinery fault evidence combination diagnosis is implemented more reliably; and by using a more reliable and reasonable fusion result, decision-making risk is reduced.

The invention provides a single-machine minimized total tardiness scheduling method based on a distributed robust model and belongs to the technical field of production scheduling and internal and external production resource optimization. The method comprises the steps that a distributed robust optimization model for solving the single-machine scheduling problem is established firstly to obtain atarget function expression of the model; then, the distributed robust optimization model is converted into an integer second-order cone programming model; the converted model is solved, arrangement combinations of all workpiece machining sequences are expressed in a search tree in an enumeration way, the search tree is pruned through a branch-and-bound algorithm, and finally an optimal single-machine scheduling scheme of the minimized total tardiness is obtained. The method gives consideration to uncertain factors of a production environment, so that the model is a determined single-machine model more conforming to the actual production situation compared with an assumed production environment, and the obtained scheduling scheme can be better applied to actual production.

The invention discloses a state identification method during crude oil pipeline blending process. The state identification method comprises the following steps: firstly analyzing data collected by a plurality of sensors so as to obtain BPA (Basic Probability Assignment) of various running state subsets by the different sensors; then sending to a reasoning identifier to process, amending the BPA based on the reliability of each sensor firstly by the reasoning identifier; and finally performing evidence combination reasoning to obtain a final identification result of the state during the crude oil pipeline blending process. The state identification method is suitable for the condition that various sensor collected information exists in a control system during the crude oil pipeline blending process. The running state identification precision of the control system during the crude oil pipeline blending process can be obviously improved, the state erroneous judgment rate is effectively reduced, and the system reliability is improved.

The invention relates to a method for controlling single-rotor plant protection unmanned plane obstacle avoidance flight. According to the method, P1, firstly, the relative distance between an unmanned plane and an obstacle is determined, and the relative distance is divided into three parts in three scopes of being smaller than N1m, between N1m and N2m and between N2m and N3m; P2, secondly, afterdistance division, dangerous grade division is carried out according to the relative speed between the unmanned plane and the barrier; P3, a height value between the unmanned plane and the ground isdetermined, and grade division of the height value H is carried out; P4, for the dangerous grade, step P3 operation is needed to carry out; for the alarm grade, step P3 operation after emergent speedreduction is needed to carry out; for the prompting grade and the irrelevant grade, third-step determination is not needed, and the process returns for re-detection. The method is advantaged in that after-disaster rescue on-site complex environment sensing of the after-disaster rescue rotor unmanned plane can be better realized, the more accurate obstacle data information is acquired, and more accurate obstacle avoidance decision and obstacle avoidance control can be carried out.

The invention discloses a method for quantitatively evaluating reliability of a simulation model. The method comprises the steps of: 10, determining a simulation model set to be evaluated; 20, determining an experimental data I / O set of the simulation model to be evaluated; 30, defining an input set and an output set of the experimental data I / O set; 40, using the input set of the experimental data I / O set as an input set of the simulation model to be evaluated to obtain an output set of the simulation model to be evaluated; and 50, figuring the reliability of the simulation model to be evaluated according to the output set of the simulation model to be evaluated and the output set of the experimental data I / O set. The invention can realize the reliability quantitative evaluation of the simulation model, effectively reduces the decision risk made based on the simulation, and provides the basis for the optimization design of the simulation model.

The invention discloses a farmland multi-source information dynamic adjustment fusion method and system, and the method comprises the steps: determining evidence factors and an identification framework, and calculating the probability distribution value of each evidence factor for each proposition in the identification framework; calculating a conflict coefficient according to a probability assignment value and a calculation formula of a conflict coefficient in a D-S evidence theory, and judges whether the conflict coefficient is in a set threshold interval, and if not, carrying out data fusion by using a classical D-S evidence theory synthesis rule, if so, adopting an average evidence factor to replace a probability distribution value of a conflict factor to correct an evidence source, and adopting a classic D-S evidence theory synthesis rule to carry out data fusion; or improving the classic D-S evidence theory synthesis rule according to the weight coefficient of each evidence factor and a historical accumulated data factor; and adopting the improved classic D-S evidence theory synthesis rule to perform data fusion, so that the reliability and reasonability of farmland monitoring data fusion are improved, and the decision risk is reduced.

The invention discloses a method and a system for estimating the credibility of a simulation system. The method for estimating the credibility of the simulation system comprises the following steps of: 10, determining a real case set used for comparison estimation; 20, calculating the credibility of each case in the real case set; and 30, weighting the credibility of each case in the real case set, and thus obtaining the credibility of the simulation system. By the method, the credibility of a large-scale simulation system can be estimated, a policy risk based on simulation is effectively reduced, heavy loss probably caused by the credibility is avoided, and the development level of the simulation system is promoted.

The invention provides an improved evidence dissimilarity index and an improved evidence fusion algorithm. The improved evidence dissimilarity index and the improved evidence fusion algorithm comprisethe following steps 1-6. The method comprises the following steps: step 1, calculating dissimilarity indexes among different evidence bodies based on proposed improved evidence dissimilarity; step 2,calculating the support degree between different evidence bodies according to the evidence dissimilarity degree, and constructing a support degree matrix; step 3, calculating a characteristic value and a characteristic vector of the support degree matrix; step 4, taking the feature vector corresponding to the maximum feature value as a weight coefficient of each evidence body; step 5, except forthe evidence with the maximum weight, correcting the rest evidence bodies by taking the weight coefficient as a discount factor; and step 6, performing evidence fusion on the corrected evidence body through a D-S combination formula, and completing a final decision. According to the method, the conflict degree between different evidence bodies can be evaluated more effectively, and the accuracy and precision of evidence fusion are improved, so that the decision risk is reduced.

The invention provides a robust single machine scheduling method based on interval uncertainty, and belongs to the field of production scheduling and operations research. The method includes: establishing a robust optimization model RSMSP of single machine scheduling, wherein an optimization target is to search an optimal workpiece machining sequence to enable the maximum waiting time of the sequence to be minimum in a poorest scene; during solution, converting the model RSMSP to a mixed linear integer programming model P; and solving the model P by employing a two-stage heuristic solution algorithm to obtain the optimal machining sequence, namely an optimal scheme of robust single machine scheduling. According to the method, uncertain parameters are expressed by employing the mode of interval estimation, a method for recognizing finite possible poorest scenes in an infinite scene set, which is in more accordance with the production reality, is firstly proposed, and decision risks canbe reduced to the maximum and the system performance can be guaranteed in the condition of poor information.

The invention provides a distributable certainty-based probability transforming method in evidence theory. The method comprises the following steps of: S1, calculating trust function values and likelihood function values of propositions in evidence; S2, calculating the distributable certainty information of the evidence according to the trust function values of the propositions; S3, judging whether the evidence comprises all single propositions or not; and S4, carrying out probability transformation according to the judgment result in the S3: S4.1, if the evidence comprises all the single propositions, carrying out the probability transformation on the credibility of the evidence by using a proportional distribution method and S4.2, if the evidence only comprises part of single propositions, carrying out the probability transformation by using a weighted combination method. The distributable certainty-based probability transforming method in the evidence theory, which is provided by the invention, is higher in accuracy and wide in application range.

A method for recognizing the petroleum product blending pressure state comprises the following steps that (1) pressure sensors are used for data acquisition; (2) abnormal data are processed; (3) elementary probability allocation is conducted on data without the abnormal data, and then evidence information, acquired by the pressure sensors at different positions during petroleum product blending, of the petroleum product blending pressure state is obtained. By means of the method for recognizing the state of a petroleum product blending optimal control system, the situation that a part of the data loose efficacy or become abnormal due to the fact that an instrument of the multiple pressure sensors is damaged can be handled; the method is adapted to not only the situation that the multiple pressure sensors are consistent in information but also the situation that the multiple pressure sensors are contradictory in information; the rate of convergence is higher, a correct target can be obtained through convergence under the condition of a small amount of evidence, the misjudgment rate is low, the recognition accuracy is high, and the stability is good.

The invention, which belongs to the field of production scheduling and operations research, provides a production scheduling method based on a distributed set robust parallel machine scheduling model.According to the method, a distributed set robust optimization model DR-PMSP-RA having a risk aversion characteristic is constructed; on the basis of an objective function and a constraint conditionof the model, an expression of an initial model DR-PMSP-RA1 is obtained; the objective function of the DR-PMSP-RA model is transformed to obtain an estimation upper bound of the objective function, the initial model is transformed into an estimation model DR-PMSP-RA2, wherein the estimation model can be decomposed into two independent sub models, the two sub models are solved and the optimal sub model solution is an optimal solution of the model, and thus an optimal production scheduling plan is obtained. The model established based on the method conforms to the actual production situation well; and with information in a production environment, the decision-making risk is reduced under the circumstance that the system performance is guaranteed.

A greenhouse control decision fusion method based on a D-S evidence theory comprises the following steps of: 1, extracting a outlier from values measured by a sensor in a greenhouse by using a box plot and correcting the outlier; 2, subjecting the corrected measured value to cluster fusion to obtain a cluster value set; and 3, subjecting the cluster values to decision distribution according to theD-S evidence theory by using the cluster value set and a decision reference value set. The greenhouse control decision fusion method based on the D-S evidence theory can improve the fusion accuracy and the decision reliability of the index data in a greenhouse environment and reduce the risk of decision making.

The invention discloses a unified power flow controller (UPFC)-containing multi-target optimal power flow calculation method considering a wind power decision risk. A UFPC can simultaneously control active power flow and reactive power flow of a power transmission line, a new technological means is provided for solving the problem of insufficiency of a power flow control means in a current power grid and has important significance to research on the UFPC; in a system containing wind power, relatively difference is generated between actual power generation cost and expected cost if a dispatching strategy of a machine set is not appropriate and wind power output fluctuates, and the situation that adjustment capability among different scenes is not enough is probable to occur; on the basis, the invention proposes a UPFC-containing multi-target optimal power flow calculation method model considering the wind power decision risk, the model fully balances and considers UPFC optimization-containing expected value and variance of wind power, and a potential decision risk is reduced by taking minimum expected value of power generator cost and power generation cost variance as multi-target functions; and an example simulation result shows that the UPFC-containing multi-target optimal power flow calculation method is effective.

The invention provides a same-type parallel machine production scheduling method considering moment information indeterminacy, and belongs to the field of production scheduling and operational research. The method comprises the steps of firstly building a distribution set robust optimization model DR-PMSP-MU considering the moment information indeterminacy, and obtaining an expression of an initial model DR-PMSP-MU1 comprising an inner layer problem and an outer layer problem; converting a decision variable of the model DR-PMSP-MU, solving the inner layer problem, and equivalently converting the model DR-PMSP-MU1 into a solvable model DR-PMSP-MU2; and taking an optimal solution of the model DR-PMSP-MU2 as an optimal solution of the whole model, wherein the optimal solution corresponds to multiple optimal production scheduling schemes, and a decider can perform selection as needed. The built model better meets an actual production condition; and by utilizing more information in a production environment, the decision-making risk can be lowered under the condition of ensuring system performance.

The invention discloses a multisource information fusion method in an evidence high-conflict environment, which comprises the following steps of: determining the mutual support degree of evidences according to evidence distances, further calculating the relative discount factor of each evidence by taking a characteristic vector corresponding to the maximum characteristic value of a evidence support degree matrix pattern as a weighting vector of the evidence, wherein the evidence with the maximum weighing coefficient is a key evidence; and discounting each evidence information, and fusing the information of multisource sensor data by using the D-S (Dempster / Shafer) rule so as to increase the accuracy and precision of a monitoring proposition judgment under an evidence high-conflict status.The method can effectively eliminate the uncertainties of multisource information, increase the accuracy of information evaluation and overcome the limitation that correct evaluation results can not be acquired by using the traditional D-S evidence theory under the evidence high-conflict status, and has the advantages of simple algorithm, high convergence rate and the like.

The invention discloses an intranet attack detection model construction method based on machine learning, and the method comprises the steps: carrying out the analysis of collected log records, carrying out the different traceability analysis of the log records according to different corresponding scenes, and determining an attack path and an attack behavior; The method comprises the steps of 1, log collection; step 2, defining an attack scene; step 3, defining behavior actions of each scene; and step 4, newly establishing an attack traceability analysis trigger point. The method has the advantages of high accuracy, decision risk reduction and the like.

The invention discloses an energy internet safety assessment method based on expert fuzzy preference, and the method comprises the steps: firstly building an energy internet safety assessment index set, and collecting the index data of each to-be-assessed energy internet; Forming a fuzzy decision matrix of energy internet security assessment according to fuzzy evaluation given by experts, and calculating a positive ideal solution and a negative ideal solution of the security assessment; then determining favorable values and adverse values of all the to-be-evaluated energy networks under all the indexes, and calculating favorable effect values and adverse consequence values of all the energy networks under all the indexes; A fuzzy entropy weight method is adopted to give the weight of eachsafety assessment index; And finally, weights given by experts under favorable and unfavorable conditions are calculated respectively, and safety assessment values of all energy internets are given. According to the invention, reference can be provided for planning construction and investment decision-making through safety assessment; And for the built energy internet, the discovery of the security weak link of the energy internet is facilitated, and the gradual improvement of the energy internet is promoted.

A system, method and computer program product configured for generating predictions using social data and comprising assembling data, using a processor, from multiple sources, wherein at least one of the sources comprises social data; and combining the data including using a processor configured for comparing corresponding data provided by more than one of the multiple sources.

The invention discloses a method for optimizing demand-side time-of-use power price based on photovoltaic grid-connected uncertainty. The method comprises the following steps: obtaining the daily load data after distributed energy is connected, and determining load peak-valley periods according to a fuzzy membership function; determining the objective function and the constraint condition of the optimization method of the demand-side peak-valley time-of-use power price in an operation cycle, and creating a peak-valley time-of-use power price optimizing model; by using a chance constraint theory, converting power balance constraint into deterministic equality constraint; and obtaining optimal peak-valley time-of-use power price by using a particle swarm optimization algorithm. The method provided by the present invention, based on peak shaving and load shifting adjustment of a power distribution network demand side according to the peak-valley time-of-use price optimization model, takes account of the connection of distributed power. At the same time, the optimization method adopts the chance constraint theory to solve the problem of prediction uncertainty of distributed power grid-connected power, reduces the decision risk caused by uncertainty and improves the pricing rationality of peak-valley time-of-use price.

The invention relates to a method for controlling single-rotor plant protection unmanned plane obstacle avoidance flight. According to the method, P1, firstly, the relative distance between an unmanned plane and an obstacle is determined, and the relative distance is divided into three parts in three scopes of being smaller than N1m, between N1m and N2m and between N2m and N3m; P2, secondly, afterdistance division, dangerous grade division is carried out according to the relative speed between the unmanned plane and the barrier; P3, a height value between the unmanned plane and the ground isdetermined, and grade division of the height value H is carried out; P4, for the dangerous grade, step P3 operation is needed to carry out; for the alarm grade, step P3 operation after emergent speedreduction is needed to carry out; for the prompting grade and the irrelevant grade, third-step determination is not needed, and the process returns for re-detection. The method is advantaged in that after-disaster rescue on-site complex environment sensing of the after-disaster rescue rotor unmanned plane can be better realized, the more accurate obstacle data information is acquired, and more accurate obstacle avoidance decision and obstacle avoidance control can be carried out.

The invention belongs to the technical field of information processing and particularly relates to a marine environment safety assessment method based on the D-S evidence theory. According to the marine environment safety assessment method, according to the D-S evidence theory, multiple marine environment element data are fused, so that marine environment safety assessment is achieved. The marine environment safety assessment method comprises the steps that (1) a computer obtains marine environment element values, and the basic probability assignment of each marine environment element is output through calculation; (2) the computer conducts fusion on the basic probability assignment obtained in the step (1) according to the D-S evidence theory fusion rule, and the fused basic probability assignment is output; (3) the computer judges the fused basic probability assignment according to the decision rule, and then the marine environment safety elevation result is output. By the adoption of the marine environment safety assessment method based on the D-S evidence theory, marine environment safety assessment is effectively achieved, reasonable decision support can be provided for a decider, and the safety of a ship sailing on the sea and the economical efficiency of the ship are improved.

The invention provides a single-machine minimized total tardiness scheduling method based on a distributed robust model and belongs to the technical field of production scheduling and internal and external production resource optimization. The method comprises the steps that a distributed robust optimization model for solving the single-machine scheduling problem is established firstly to obtain atarget function expression of the model; then, the distributed robust optimization model is converted into an integer second-order cone programming model; the converted model is solved, arrangement combinations of all workpiece machining sequences are expressed in a search tree in an enumeration way, the search tree is pruned through a branch-and-bound algorithm, and finally an optimal single-machine scheduling scheme of the minimized total tardiness is obtained. The method gives consideration to uncertain factors of a production environment, so that the model is a determined single-machine model more conforming to the actual production situation compared with an assumed production environment, and the obtained scheduling scheme can be better applied to actual production.