83 results about "Problem transformation" patented technology

The invention discloses a dispute focus automatic identification method based on a hierarchical attention neural network model. The method comprises the steps of firstly, extracting a dispute focus statement of a court for case induction from a document containing a dispute focus induced by the court, and constructing a dispute focus system by utilizing a hierarchical clustering method; and marking a plurality of different category labels for each document by utilizing the dispute focus system, constructing a data set, and converting a dispute focus identification problem into a multi-label multi-classification problem; then, training a hierarchical attention neural network model, paying more attention to important words, sentences and paragraphs containing more information, and forming adispute focus recognizer; and finally, inputting the text of which the dispute focus needs to be identified into a dispute focus identifier to obtain the dispute focus of the input text. The method ishigh in prediction accuracy, can accurately identify and judge the dispute focus of the document, and has good expandability.

A method of realizing coordination control of a power-distribution-network static var compensator and a static synchronous compensator belongs to the electric power system technology field. A current method used for carrying out coordination control on a DSVC and DSTATCOM interaction influence problem is complex. By using the method of the invention, the above problem is solved. The method is characterized in that a coordination problem between the controllers is converted into a multi-target optimization problem; a novel multi-target heredity algorithm with a great influence is introduced, wherein the algorithm is a rapid non-dominated sorting genetic algorithm with an elitist strategy; parameters of the power-distribution-network static var compensator and the static synchronous compensator are coordinated. Compared to a traditional multi-target algorithm, by using the method of the invention, an obtained Pareto optimal solution is distributed uniformly; convergence and robustness are good. The method can be used for solving a coordination control problem of the power-distribution-network static var compensator and the static synchronous compensator.

The invention discloses a multi-objective reactive power optimization method for solving a Pareto optimal solution set. The method of the invention converts the problem into a problem of solving reactive power optimization containing two objective functions, boundary points of the two objective functions are respectively solved directly in a tangent method, an inequality constraint method is then adopted to solve Pareto frontier formed by the two objective functions, during the calculation process, algorithm for solving a single-objective reactive power optimization model adopts a primal dual interior point method, and a point for an initial value adopts a result of power flow calculation. The multi-objective reactive power optimization method for solving a Pareto optimal solution set has the advantages that optimization of multiple objectives is comprehensively considered during the reactive power optimization process, subjectivity generated when a single objective reactive power optimization model is built by other algorithms can be effectively avoided, the distribution condition of each objective function can be intuitively seen from the Pareto frontier surface, and a reasonable and reliable decision which can meet needs of system operation can be made conveniently by a decision maker.

The invention provides a counter-fact prediction method about a set type decision effect, and belongs to the technical field of machine learning. According to the method, a decision variable and confusion variable decorrelation problem is converted into a lower-dimension decision variable implicit representation and confusion variable decorrelation problem, a probability density ratio estimation method based on a deep neural network, is adopted to take the probability density ratio of the joint distribution of the decision variable implicit representation and the confusion variable corresponding to the observation data sample to the joint distribution of the decision variable implicit representation and the confusion variable unassociated as the weight of a data point formed by the decision variable implicit representation and the confusion variable. A variational sample reweighting method is adopted to synthesize the weight of a data point formed by decision variable implicit representation and confusion variables into the weight of a sample in observation data, and the weighted observation data sample is utilized to train a counter-fact prediction model to perform counter-fact prediction on the effect of an individual under the influence of a specific decision. According to the invention, the accuracy of counter-fact prediction is improved, and the method has a very high application value.

The invention relates to a design method of motor optimization based on Game Theory, which comprises the following steps: (1) determining variables to be optimized in motor design and establishing a partial objective functional equation and a constraint equation; (2) determining a noninferior solution set which is then taken as a strategy set in Game Theory; (3) taking an optimization objective as a player and taking a partial objective function and taking definite purpose as utilities of each player, disintegrating design variables into strategies for each player according to the correlation between the utilities and each partial objective function, and changing the solving problem of the optimal motor design scheme into a Game problem according to the above steps, wherein each player can cooperate with each other so as to maximize the utility of each player; and (4) adopting a method of cooperative Game for integratedly optimizing each optimization objective from Nash equilibrium solution, thus obtaining the global optimum solution. The design method of motor optimization fully takes the relation among different optimization objectives into account so as to search the integrated optimum solution, thus shortening the motor design cycle, lowering development difficulty and laying foundation for the intelligentization of motor optimization design.

The invention discloses a dynamic target traversal access sequence planning method and system. The method comprises the following steps: giving an initial position and a terminal position of each target and a moving path of each target in a planning time period; converting a moving target access sequence planning problem into an intermittent moving target access sequence planning problem through adiscrete time axis, and constructing a problem model; solving the intermittent moving target access sequence planning problem model by adopting a dynamic target sequence planning ant colony algorithmto obtain an optimal target access sequence of the intermittent moving target access sequence planning problem; fixing the target access sequence so that an original moving target access sequence planning problem is converted into a sequence fixed access time planning problem, creating and solving a problem model to acquire the optimal access time of each target and finally, acquiring the optimalsolution of the original moving target access sequence planning problem. Optimization efficiency and the optimization success rate of the moving target access sequence planning problem are effectively improved.

The invention discloses an ICT infrastructure site selection method based on variable neighborhood search, and the method comprises the following steps: 1, obtaining different double-target set coverage problem modeling and problem abstraction according to different ICT infrastructure site selection problems, and obtaining a target function of a single-target optimization problem; 2, solving an objective function of a single-objective optimization problem by utilizing a variable neighborhood random search method; according to the invention, a complex double-target set coverage problem based onan ICT infrastructure site selection background is converted into a relatively simple single-target optimization problem, and then an efficient solving algorithm based on variable neighborhood searchis designed according to the problem background, so that the situation that only expert experience can be relied on is changed into the situation that automatic calculation can be conducted dependingon a computer. And due to different neighborhoods and search strategies in variable neighborhood search, compared with other local search algorithms, the algorithm can search a better deployment scheme from ICT infrastructure deployment schemes.

The invention discloses a threat intelligence named entity recognition method based on machine reading understanding, which comprises the following steps: performing sentence segmentation processing on threat intelligence, filtering sentences which do not contain network security professional vocabularies based on a network security professional lexicon, and obtaining a sentence set after filtering; sentences in the sentence set are taken one by one, and each entity in the sentences is marked with a question and answer pair; training a recognition model by using the labeled sentence set; and performing named entity recognition on the threat intelligence by using the trained recognition model. The threatening intelligence named entity recognition based on machine reading understanding can effectively solve the problems of fuzzy classification of threatening intelligence entities and entity nesting; the built problem is provided with entity hidden information, so that the recognition accuracy can be effectively improved; according to the method, entity recognition is converted into a classification matching problem from a sequence labeling problem, so that one sentence with a plurality of entities can generate a plurality of training samples, and the requirement on the number of sentences is reduced.

The present invention proposes a layout analysis method and system for automatic classification of test paper content. The method includes: obtaining an input document image; extracting connected components of the document image to form an original set of connected components; The connected components are classified into text and non-text, and the first set of text connected components and the set of non-text connected components are obtained; for each connected component in the set of non-text connected components, the text components are detected and segmented, and the connected components of the non-text classification are obtained. The text components in the connected components, and add this component to the first text connected component set to obtain the second text connected component set; for each connected component in the second text connected component set, carry out the classification of printed characters and handwritten characters ; Output the classification result of the document image content. By adopting the method of the invention, the classification problem of elements is transformed into a global optimization problem for solving the maximum joint probability of all elements, so that the overall classification accuracy rate can be improved.

The invention provides a data processing method in learning modeling, and belongs to the technical field of Python machine learning modeling, and aims to convert a regression problem with a large amount of required information into a multi-classification problem with a small amount of required information by utilizing log transformation and equal-width standardization on the basis of a power law relationship and aiming at a y variable conforming to long tail distribution, so that subsequent modeling work can be carried out smoothly.

The invention discloses a method for automatically identifying the focus of disputes based on a hierarchical attention neural network model. First of all, extract the court's summary of the dispute focus sentence from the documents containing the court's summary of the dispute focus, and use the hierarchical clustering method to construct the dispute focus system. Then use the dispute focus system to label each document with multiple different categories, construct a data set, and transform the problem of identifying focus of dispute into a multi-label and multi-category problem. Afterwards, the hierarchical attention neural network model is trained to focus more attention on important words, sentences, and paragraphs that contain more information to form a dispute focus recognizer. Finally, the text that needs to identify the focus of dispute is input into the focus of dispute recognizer to obtain the focus of dispute of the input text. The method of the invention has a high prediction accuracy rate, can accurately identify and judge the dispute focus of documents, and has good scalability.