9 results about "Fuzzy vector" patented technology

The invention relates to the technical field of vehicle communication, and discloses a vehicle communication control method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting a current speed contradictory index, a current space overlapping degree and a current environment complexity, and carrying out the calculation of the current speed contradictory index, performing fuzzification processing on the current space overlapping degree and the current environment complexity respectively to obtain fuzzy vectors corresponding to the current moments respectively; according to all the fuzzy vectors and a preset emergency degree table, determining trigger intensity and sub-emergency degree values under each emergency condition; determining a current emergency degree value according to the trigger intensity and the sub-emergency degree value under each emergency condition; and determining a current communication strategy according to the current emergency degree value, and controlling vehicle communication according to the current communication strategy. According to the invention, the current communication strategy adapted to control the vehicle communication in the current scene can be determined, the communication redundancy can be avoided while the vehicle communication requirement is met, and the effect of flexibly adjusting the vehicle communication strategy is realized.

The invention provides a steam turbine fault symptom identification method based on fuzzy vector mapping and independent element space reconstruction, relates to the field of steam turbine fault diagnosis, and aims to solve the problems that normal working conditions and fault features are difficult to distinguish and the non-linear and non-stationary signal processing capacity is weak in a traditional vibration analysis method, an oil analysis method and an independent element analysis method. The method comprises the following steps: collecting multivariable sensor data of a steam turbine in a normal state for preprocessing; establishing an initial independent element analysis model; a fuzzy observation vector is constructed by using a Gaussian membership function, and the influence of large-amplitude normal fluctuation is weakened; performing dynamic reconstruction in an independent element space through the hybrid matrix, and calculating a residual vector; and constructing a square prediction error statistic based on the residual vector to carry out fault judgment. According to the method, the problem of order and amplitude uncertainty in independent component analysis is effectively solved, the recognition accuracy of the early weak fault of the steam turbine is remarkably improved, and reliable technical guarantee is provided for safe and stable operation of a power system.

The present invention discloses a vehicle formation driving control method for defending against eavesdropping attacks, belonging to the field of vehicle network security technology. During the wireless transmission process of the formation vehicles, a designed fuzzy vector is introduced to effectively protect the state privacy and complete the formation control task at the same time. The method comprises the following steps: determining the lead vehicle and the following vehicle, and then determining the network topology; the lead vehicle randomly generates a fuzzy vector that meets the conditions, and transmits the false state to the following vehicle according to the formation control communication protocol; the following vehicle designs a noise vector and receives the fuzzy vector transmitted by the lead vehicle, and when communicating with the front and rear adjacent vehicles, transmits its own false state according to the designed formation control communication protocol; constructs a second-order dynamic model of the lead vehicle and the following vehicle, designs the control input based on the formation control communication protocol, and completes the formation control. The present invention has the advantages of simple operation, low computational complexity, and strong real-time performance.

The present invention discloses a control method and system for a surveillance camera, which relate to the technical field of camera control. By calculating the target defocus rate Tbd within a time window T, the blur state is no longer dependent on instantaneous image judgment, but has statistical trend characteristics, effectively alleviating the false triggering problem caused by occasional factors such as temporary interference and occlusion. The target defocus rate Tbd is compared with the defocus rate judgment threshold Thd to generate a focus control signal Foc, complete automatic focusing on a key target area, avoid the resource waste and focus delay caused by the fixed periodic focusing of the traditional system, re-collect the blur features of the focused image, construct a new fuzzy vector sequence set Ftr4, and compare it with the fuzzy vector sequence set Ftr2 to calculate the focus effect value Qev, realize the closed-loop evaluation and optimization adjustment of the quality of the focusing behavior, and overcome the problem of the lack of a feedback verification mechanism for focus control in the prior art.

The application relates to the field of satellite navigation and timing technology, in particular to a Beidou and INS (inertial navigation system) fused real-time high-precision timing method, which comprises the following steps: generating pseudo-range residuals and code-minus-carrier based on Beidou satellite signals, and generating satellite channel fuzzy vectors by applying fuzzy reasoning; performing spatial clustering based on the multi-dimensional features of satellite signals, and configuring parallel sub-filters for satellite clusters; the sub-filters utilize satellite channel fuzzy vectors and carrier dynamic information to output error correction amounts and uncertainty variances; the satellite observations are corrected by using the error correction amounts, and a composite noise covariance matrix is constructed based on the uncertainty variances; the corrected observations and the composite noise covariance matrix are input into a navigation solver to extract high-precision time information. The application significantly improves the positioning and timing precision of a radio navigation system in a complex dynamic environment by hierarchically and cooperatively estimating and compensating the propagation errors of satellite signals.

The invention discloses a control method and system for a monitoring camera, and relates to the technical field of camera control, and by calculating a target out-of-focus rate Tbd in a time window T, a fuzzy state does not depend on instantaneous image judgment any more, but has a statistical trend characteristic, and the target out-of-focus rate Tbd does not depend on the statistical trend characteristic. The problem of false triggering caused by accidental factors such as temporary interference and shielding is effectively relieved, the target out-of-focus rate Tbd is compared with the out-of-focus rate judgment threshold value Thd, a focusing control signal Foc is generated, automatic focusing of a key target area is completed, resource waste and focusing delay caused by fixed periodic focusing of a traditional system are avoided, and automatic focusing of the key target area is achieved. According to the focusing control method and device, the fuzzy feature of the focused image is recollected, a new fuzzy vector sequence set Ftr4 is constructed, and the fuzzy vector sequence set Ftr4 is compared with the fuzzy vector sequence set Ftr2 to calculate the focusing effect value Qev, so that the quality closed-loop evaluation and optimal adjustment of the focusing behavior are realized, and the problem that the focusing control lacks a feedback verification mechanism in the prior art is solved.

The invention discloses an electric vehicle battery system high-voltage accessory fault diagnosis device and method. A fault diagnosis system and a high-voltage load are connected in parallel to a high-voltage bus; the fault diagnosis system comprises a signal generation module and a data acquisition module, and a response current signal is obtained through the data acquisition module; decomposing the response current signal into a plurality of time sequence prototype features, calculating the prototype matching degree of each prototype feature, combining the prototype matching degree into a matching degree vector as a fault symptom fuzzy vector, obtaining a fuzzy diagnosis matrix, and further establishing a relationship between the fault symptom fuzzy vector and a fault mode fuzzy vector; and selecting the mode corresponding to the value with the maximum membership degree from the fault mode fuzzy vector to obtain the high-voltage accessory fault of the battery system of the electric vehicle. According to the invention, the fault diagnosis of the high-voltage accessory key part of the electric vehicle battery system is realized, the safety and reliability of the electric vehicle battery system can be improved, and the safety of the vehicle and the vehicle-mounted personnel is guaranteed.

The invention discloses a multi-baseline INSAR phase unwrapping method, system and device and a medium, and the method comprises the steps: constructing a to-be-clustered data set: obtaining the intercept information corresponding to each pixel according to the interferograms corresponding to different vertical baselines, then taking the position information of each pixel as the multi-dimensional clustering feature of the pixel, and obtaining the to-be-clustered data set; taking the pixel as a to-be-clustered target to obtain a to-be-clustered data set; wavelet clustering processing: performing clustering processing on the data set to be clustered based on wavelet clustering; clustering result correction: aiming at pixels in each noise cluster, correcting cluster labels of the pixels; phase unwrapping is carried out cluster by cluster: on the basis of the corrected cluster distribution, a fuzzy vector of each cluster is calculated by using a closed solution formula or a sparse-TSPA method, and the absolute phase of each pixel in the corresponding cluster is calculated on the basis of the fuzzy vector of each cluster. When the method is used for processing a large-size interference pattern, the method has more advantages in the aspects of efficiency, precision and base line ratio adaptability compared with an existing multi-base line phase unwrapping method.