129 results about "Decision level" patented technology

The invention discloses a bearing fault diagnosis method based on an LS-SVM and the D-S evidence theory. Through a multi-level information fusion method, the least squares support vector machine (LS-SVM) is adopted in a feature layer, and the D-S evidence theory is adopted in a decision-making layer to solve the problems that the single sensor fault diagnosis accuracy is low and the sensitive features are difficult to extract. Firstly, the wavelet noise reduction technology is used to improve the signal-to-noise ratio of a rolling bearing vibration signal, and eight parameters in the time domain and the frequency domain are introduced as feature parameters of the bearing vibration. Secondly, the bearing is fault identified by the LS-SVM. Thirdly, the LS-SVM feature output is used as the D-S evidence theory input, and the D-S evidence theory is used for fault decision making. The method can effectively improve the accuracy of rolling bearing fault diagnosis. The invention has certain significance for improving the fault diagnosis accuracy of the rolling bearing and the reliability of the diagnostic system.

The invention provides an intelligent electrical-network fault diagnosis method based on multilevel feedback adjustment. The method comprises the steps that historical switching information and electrical quantity attribute information of different devices in an electrical network when the electrical network has faults are stored in a historical fault information base; electrical quantity information of the devices in the electrical network is obtained when the electrical network has faults; diagnosis at the rough identification level is carried out according to the obtained electrical quantity information of the devices in the electrical network, and suspected fault elements are determined in a minimal breaking zone defining method based on the equivalent network to form a candidate set of the suspected fault elements; diagnosis at the fuzzy decision making level is carried out; diagnosis at the accurate positioning level is carried out; and according to results of three levels of fault diagnosis, intersection elements are obtained from suspected fault element sets E1 and E2 to finally determine a diagnosis result of fault elements. The electrical network with faults is analyzed at different levels according to the different sources of the multi-source fault information and the obtaining and processing difficulty of different type of information, and different types of fault information is fully utilized and information is complementary to improve the accuracy of fault diagnosis.

The invention relates to a multi-source information fusion fire prediction method based on a dynamic integrated neural network. Innovative logic design, establishing a fire prediction model based on amulti-source information fusion method; various fire characteristic signals pass through an information layer, a characteristic layer and a decision-making layer in sequence; in a feature layer, LSTMand RBF-BP neural networks in deep learning are used as sub-networks to carry out adaptive learning on multi-source fire feature signals; according to the fire prediction method, the output result issubjected to integrated analysis, and fire prediction is completed through the decision-making layer, so that the problems of time-varying characteristics and nonlinear characteristics of the fire signal and high missing report rate and false alarm rate of the single-characteristic signal fire prediction method are solved, and the recognition accuracy of the fire prediction system is effectivelyimproved. The method has high expandability, a complete prediction model can be established only by providing a data set again after a detection environment is changed, and the method has high adaptability.

The invention discloses a multi-modal sentiment classification method based on a heterogeneous fusion network, and belongs to the technical field of opinion mining and sentiment analysis. The method comprises the steps of 1) preprocessing video data; 2) constructing a text feature vector and identifying a text emotion category; 3) constructing a picture feature vector and identifying a picture emotion category; 4) constructing an audio feature vector and identifying an audio emotion category; 5) constructing a multi-modal global feature vector and identifying a multi-modal global emotion category; 6) constructing a multi-modal local feature vector and identifying a multi-modal local emotion category; and 7) adopting a voting strategy to obtain a final sentiment classification result. The heterogeneous fusion network adopts two fusion forms of intra-modal fusion and inter-modal fusion, two fusion angles of macroscopic fusion and microscopic fusion, and two fusion strategies of feature layer fusion and decision-making layer fusion. According to the method, the implied associated information among the multi-modal data can be deeply mined, and mutual complementation and fusion among the multi-modal data are realized, so that the accuracy of multi-modal sentiment classification is improved.

The invention discloses a virtual experiment system and method based on multi-modal interaction. The experiment system mainly comprises an input layer, a sensing and recognition layer, a fusion layerand an application layer. The method comprises the steps: acquiring corresponding visual information, sensing signals and voice signals through a visual channel, a tactile channel and an auditory channel respectively; respectively identifying the information of different channels; fusing corresponding vector sets constructed by modal information of the visual channel, the tactile channel and the auditory channel on the AR platform by adopting a multi-modal fusion method based on a decision level according to an identification result; and according to the fusion result, presenting an experimentprocess and an experiment result by adopting voice navigation, visual display and tactile feedback modes. Through the application, multiple channels can be fully utilized, and an experimental processis achieved by adopting a multi-mode fusion method, so that the operation load of a user is reduced, the immersion of an experiment is improved, and the efficiency of virtual interaction is improved.

The invention relates to a processing method and device for enterprise intelligent decision analysis, computer equipment and a storage medium. The method comprises the steps of obtaining to-be-processed data corresponding to an enterprise identifier; performing feature extraction on the to-be-processed data to obtain a feature vector of the to-be-processed data; determining a logic relationship between the feature vectors through a logic layer of a pre-trained machine learning model; through a data analysis layer of the machine learning model, analyzing the feature vector by utilizing the logic relationship to obtain an evaluation value corresponding to the enterprise identifier; and processing the evaluation value through a decision-making layer of the machine learning model to obtain corresponding decision-making data. By adopting the method, the processing accuracy for enterprise intelligent decision analysis can be improved.

The invention discloses a sudden respiratory infectious disease risk prediction model for regional epidemic prevention and control. The model comprises the following steps: S1, obtaining original data; s2, carrying out data management; s3, establishing a knowledge base; s4, carrying out decision-making layer fusion; and S5, establishing a risk prediction model. The invention has the following beneficial effects: (1) the system has advantages in multi-dimensional input data and output data processing; (2) any complex nonlinear problem can be processed, so that the nonlinear mapping capability is high; (3) information or data can be processed in parallel, and the data processing capability and performance are improved; (4) the self-adaptability is relatively high; and (5) the data fusion aspect is good.

The invention discloses a decision-making layer data fusion method based on a multi-source sensor, and the method comprises the following steps: enabling the output of each sensor to be target-level data, carrying out the testing and analysis of the performances of each sensor before use, and obtaining some prior information and failure scenes of an output signal of each sensor; then, carrying outdata preprocessing on a target output by a single sensor, filtering a false detection target preliminarily, and smoothing abnormal jump data; and finally, fusing the preprocessed target-level data ofeach sensor according to priori knowledge obtained by early-stage testing, thereby obtaining stable and reliable output. According to the method, the interference of ghost introduced by false detection of a single sensor on the system is reduced, and the omission ratio of the system under specific complex working conditions can be reduced by complementary target information.

The invention discloses a multi-target flexible job shop energy-saving scheduling method based on an improved grey wolf algorithm. The method comprises the following steps: firstly, constructing a flexible job shop energy-saving scheduling problem model; two-segment coding based on natural numbers is adopted; discretizing a continuity problem by adopting a mode based on an LOV rule; introducing an aggregation rate among individuals to obtain an initial population with relatively high quality; evaluating individuals in the initial population, determining an alpha wolf set, a beta wolf set and a delta wolf set of individuals of a decision-making layer according to a proportion, and adding a non-dominated solution into an external file; a dual-mode parallel search mode is used, tracking and searching capabilities are dynamically adjusted in the search process, improved tracking operation is introduced to improve the problem solving precision, variable domain search is adopted in the search mode, the evolution speed is improved, and a local optimal solution is broken through. According to the method, the machining sequence of the workpieces on the machines is reasonably arranged, and a better scheduling scheme is provided for a production enterprise from the three aspects of minimizing the maximum completion time, the total delay duration and the total energy consumption of the system.

The invention discloses a coal seam roof water and sand inrush risk evaluation method based on multi-source information fusion. The method comprises the following steps: comprehensively considering hydrogeological conditions, geological conditions, mining activity influence and other factors; taking the sand bed thickness, the base mining ratio, the sand burst fracture zone development height, thebed rock weathering index, the unit water inflow, the water saturation sand bed thickness, the permeability coefficient and the mining influence index as water and sand burst risk evaluation indexes,and on the basis, through a series of specific implementation steps of establishing a mathematical analysis model, establishing a decision-making level main control factor thematic map, constructinga judgment matrix to determine index weights, superposing the decision-making level main control factor thematic map and the like, finally, displaying the quantified result in the form of a graph by utilizing data storage, spatial data processing and analysis functions and the like of a geographic information system, and outputting a result graph through a graph output system, and achieving waterand sand bursting risk multi-source information partitioning according with the actual situation of a mine.

The invention discloses a financial data processing system. The system is constructed by being divided into four layers, and the four layers are respectively a data layer, a data management layer, ananalysis layer and a presentation layer. The data layer, the data management layer, the analysis layer and the presentation layer transmit financial data progressively in sequence. According to the processing system, financial operation monitoring, financial statement and accounting information analysis and evaluation, financial operation key index early warning and group company financial prediction are carried out, the system realizes short-term and long-term financial analysis, financial prediction and deep data mining, provides auxiliary decision support for a decision-making level, provides financial management information service for a business level, and provides an analysis and research system platform for financial analysis personnel.