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 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.