36 results about "Brainstorming" patented technology

The present invention provides a failure logic modeling method for a high-speed railway train operation control on-board system, including the following steps: determining the functional relationship between the structure and the parts of the train control on-board system; analyzing the failures of the parts based on known failures of the train control on-board system in combination with brainstorming; establishing an FMEA table to generalize the failures of the parts; editing and simulating the failures by using a modeling tool. By adopting the present invention, the failure modes analyzed are more systematic, and the failure logic modeling can be carried out from a global perspective more easily, the whole process can be better targeted meanwhile, the security analysis complexity of the train control system will be effectively reduced and the development period of the train control system will be shortened.

An intelligence-driven virtual assistant for automated documentation of new ideas is provided. During a brainstorming session, one or more user participants may discuss and identify one or more ideas. Such ideas may be tracked, catalogued, analyzed, developed, and further expanded upon through use of an intelligence-driven virtual assistant. Such virtual assistant may capture user input data embodying one or more new ideas and intelligently process the same in accordance with creativity tool workflows. Such workflows may further stimulate creativity for capturing ideas, while continuing to document, analyze, and identify further aspects to develop and expand.

The invention discloses a brainstorm-based scheduling method for the economic environment of a thermal power plant. The steps include: step 1, determining the mathematical model of the environmental economic scheduling problem of the thermal power plant; step 2, obtaining various parameters in the model; step 3, obtaining the environment The initial feasible scheduling set of the economic scheduling problem; Step 4, evaluate the feasible scheduling set; Step 5, update the feasible scheduling; Step 6, judge whether the entire feasible scheduling is updated; Step 7, evaluate the non-inferior scheduling in the external archive set The solution set is updated; step 8, iteratively outputs the final feasible scheduling scheme, and it is completed. The method of the present invention uses the thermal power plant as the power supply system, utilizes the collection and analysis of the data of the power system and the thermal power plant, and combines the brainstorming optimization algorithm based on the idea of ​​clustering and variation to realize the solution to the thermal power plant economic scheduling problem that requires comprehensive environmental protection solve.

An intelligence-driven virtual assistant for automated documentation of new ideas is provided. During a brainstorming session, one or more user participants may discuss and identify one or more ideas. Such ideas may be tracked, catalogued, analyzed, developed, and further expanded upon through use of an intelligence-driven virtual assistant. Such virtual assistant may capture user input data embodying one or more new ideas and intelligently process the same in accordance with creativity tool workflows. Such workflows may further guide development and expansion upon a given idea, while continuing to challenge ideas, provide opportunities to collaborate, and optimize scheduling of ideation sessions for efficiency, while continuing to document, analyze, and identify further aspects to develop and expand.

For the disadvantages that the existing method is poor in optimizing result and slow in convergence speed and loses efficacy while the joint perceptive probability is relatively high when the problem of optimal equivalent working sensor distribution in target coverage is solved, the invention provides a novel optimal target coverage method for a heterogeneous sensor network. By means of the optimal target coverage method provided by the invention, the disadvantage that the current brainstorming optimization algorithm cannot be applied to a discrete problem can be solved; and the application range of the brainstorming algorithm is expanded. A simulation result shows that: compared with the existing classical target coverage method, the convergence speed and the convergence precision of the optimal target coverage method in the invention are relatively superior; therefore, the effectiveness of the optimal target coverage method in the invention is proved; under same conditions, when the joint perceptive probability constraint is relatively strict, the traditional method can lose efficacy; but, the method in the invention still is feasible. According to the optimal target coverage method provided by the invention, scheme exchange in a brainstorming process is reflected in the generation mode of a new scheme in a fusion manner; and, compared with scheme exchange of the original brainstorming algorithm, the scheme exchange is relatively wide, and is closer to a real brainstorming process.

The invention discloses a facial emotion recognition feature selection method based on an improved brainstorming optimization algorithm, the steps are as follows: (1) extracting expression features from a facial image by using a histogram of direction gradient HOG to form an initial feature vector; (2) calculating the emotion The difference value between the eigenvector and the neutral emotion eigenvector, find all the features that are different due to emotional changes, construct the difference eigenvector, and pass it to the feature selection module; (3) use the improved brainstorming method in the feature selection module The optimized feature selection method finds a feature subset with the least number of features and the highest classifier accuracy; (4) uses the support vector machine determined by the above feature subset as an emotion classifier to classify new facial images, and then The facial emotion recognition is completed; the invention significantly improves the recognition accuracy of the algorithm; reduces the search space of the brainstorming optimization technology, and obviously improves the processing speed of the facial emotion recognition problem.