36results about How to "No prior knowledge required" patented technology

The invention discloses an inner and outer layer nesting ECMS (equivalent fuel consumption minimization strategy) multi-objective double-layer optimization method. The inner and outer layer nesting ECMS multi-objective double-layer optimization method includes steps of building multi-objective optimization models of plug-in hybrid electric vehicles; solving the multi-objective optimization modelsby the aid of inner and outer layer nesting multi-objective particle swarm algorithms to obtain multi-objective optimized Pareto solution set front edges; weighting equivalent fuel consumption per hundred kilometers and variation ranges of deviation of SOC (state of charge) final values and target values, building total evaluation functions related to the equivalent fuel consumption per hundred kilometers and SOC deviation and selecting the optimal charge and discharge equivalent factors and engine and motor power distribution modes corresponding to the optimal charge and discharge equivalentfactors. The inner and outer layer nesting ECMS multi-objective double-layer optimization method has the advantages that output power of engines and motors of the plug-in hybrid electric vehicles canbe reasonably distributed at CS (charge sustaining) stages, so that fuel consumption can be reduced as much as possible, battery SOC balance still can be effectively kept, and the fuel economy of theintegral vehicles can be improved.

The invention relates to a transformer winding looseness fault detection method based on an average displacement method, and belongs to the technical field of power transformer safety monitoring. The method comprises the steps that surface vibration data of a transformer with an unloosened winding are measured at preset time intervals within preset time range, the average displacement of the transformer is calculated, and a first average displacement sequence is obtained; surface vibration data of a transformer to be measured are obtained at the same time intervals within the same time range, the average displacement of the transformer is calculated, and a second average displacement sequence is obtained; difference values between the average displacements at the same time delay are calculated, the difference values are added, and evaluation factors for evaluating the winding looseness degree are obtained. The transformer winding looseness fault detection method is easy to implement, easy and convenient to operate and accurate in diagnosis, and the number of needed devices is small.

The present application discloses a health monitoring method of a positioning and speed measuring system, including: obtaining the full width at half maximum of the autocorrelation curve of the variables representing the state of the positioning and speed measuring system; determining each subdivision when subdividing the original matrix according to the full width at half maximum and the number of variables The number of columns of the matrix, where the original matrix is ​​composed of historical data of variables; the original matrix is ​​subdivided into multiple subdivision matrices according to the number of columns, and the multiple subdivision matrices are reconstructed to obtain a random matrix; the positioning is determined according to the random matrix The average spectral radius of the speed measurement system; according to the relationship between the average spectral radius and the preset threshold, determine whether the positioning speed measurement system is healthy. This health monitoring method does not need to know the failure mode, degradation mode and internal mechanism of the positioning speed measurement system, but only needs the historical data of the variable and the full width at half maximum. It has the advantages of small amount of calculation and no prior knowledge. In addition, the present application also provides a health monitoring device, equipment and computer-readable storage medium having the above-mentioned advantages.

The invention discloses a passive target positioning method for random deployment of wireless sensor nodes, which specifically includes the following steps: Step 1: Deploy sensor nodes and base stations in the monitoring area; Step 2: Collect RSS values ​​without interference before monitoring; Step 3: RSS value collection and classification in the monitoring stage; Step 4: Since the nodes in the monitoring area are randomly deployed, in order to accurately locate the target location, it is first necessary to determine the location of the link interfered by the target in the monitoring area, that is, to form The locations of the nodes of these interfered links; Step 5: Determination of target locations in the monitoring area. The method of the present invention can be performed without prior knowledge. In addition, the method does not require grid division, thereby avoiding the shortcomings of grid size affecting positioning accuracy in positioning methods based on grid division.

The invention discloses a method for monitoring Lamb wave engineering structural damage by utilizing signal decomposition, which comprises the following steps: collecting the Lamb wave healthy reference signals fhji of an engineering structure in a healthy state, wherein i and j can be 1, 2, 3,...; when the structure is damaged, collecting all the Lamb wave response signals f'ji of the damaged structure under the condition of an inspiring / sensing channel; utilizing the correlation operation to extract all the Lamb wave damage scattered signals of the damaged structure under the condition of the inspiring / sensing channel; obtaining the damage position and scope according to the characteristic parameters acquired in the steps above, thereby analyzing and judging the health condition of the monitored structure. According to the method, based on the condition of the traditional structural damage monitoring device, the Lamb wave damage scattered signals in a damage monitoring system are extracted by using the related treatment, thereby increasing the accuracy and stability of the monitoring system.

The invention discloses a multi-AUV communication delay active compensation method based on a data-driven predictor, comprising the following steps: establishing an autoregressive model; determining the undetermined order of the autoregressive model; designing an online update law of a parameter vector; The data-driven state predictor of j AUVs; realizes active compensation of communication delay. The present invention estimates the current actual motion state of the neighboring AUVs online in real time by designing a state predictor, and applies the estimated value to the design of the formation control law, so as to realize the active compensation of the AUV communication delay. The active compensation method for multi-AUV communication delay proposed by the present invention has no restriction on the design method of the AUV formation control law, and can flexibly apply various advanced formation control theoretical methods. The active compensation method for multi-AUV communication delay proposed by the present invention is data-driven and does not depend on the AUV motion mathematical model, therefore, no prior knowledge of the parameters and structure of the AUV motion mathematical model is required.

The present invention relates to an image and video processing method, and in particular, to a two-phase-interaction-based extraction and association method for an object of interest in a video. In the method, a user performs coarse positioning interaction by an interactive method which is not limited to a normal manner and has a low requirement for prior knowledge; based on this, a certain extraction algorithm which is fast and easy to implement is adopted to perform multi-parameter extraction on the object of interest. Base on a most suitable object of interest which is obtained from reselection interaction, the method obtains a final result by extracting various features to perform searching and weighting; corresponding images and supplementary information are extracted from a value-added information base, and finally the information is presented at a periphery of the video. In the method, on the basis of mining video information fully and ensuring user preference, in a manner where the viewing of the user is not affected, associate value-added information with the object which the user is interested in, thereby meeting the user's requirement for deeply knowing and further exploring an attention area.