10 results about "Fuzzy classification" patented technology

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment process parameter adjusting method and system based on fuzzy reasoning, and the method comprises the steps: obtaining to-be-treated sewage and a historical treatment log set, carrying out data cleaning on the historical treatment log set to obtain a cleaned log set, and obtaining a membership function set, performing fuzzy classification based on the membership function set and the cleaned log set to obtain a fuzzy historical data set, constructing a fuzzy rule base, performing water quality detection on to-be-treated sewage to obtain real-time water quality data, summarizing the real-time water quality data and a process parameter set to obtain a real-time treatment log, and adding the real-time treatment log to a historical treatment log set. And on the basis of the updated processing log set and the standard real-time water quality data, sewage treatment process parameter adjustment based on fuzzy reasoning is completed. The intelligent level and stability of parameter adjustment of the sewage treatment process can be improved.

The invention discloses a water ecology multi-objective optimization regulation and control method based on a self-attention architecture, and the method comprises the steps: collecting environment data from a target region, and carrying out the data preprocessing of the environment data; constructing an initial unsupervised pre-training model by adopting historical data, and generating a fuzzy classification threshold value; according to the real-time environment data, an initial unsupervised pre-training model is adopted, a classification result is dynamically generated, and density grades and risk states of different algae are marked; and dynamically adjusting water quality protection measures according to the classification result. The method is used for algal bloom risk assessment, water quality dynamic monitoring and other ecological protection tasks in a water ecosystem, and provides powerful technical support for scientific management and real-time response. The model shows excellent classification accuracy in an experiment, and the flexibility and adaptability of practical application are improved.

The invention discloses a regional scale axle load spectrum determination method and system based on multistage check classification. The method comprises the following steps: acquiring axle load data and intermodulation data of a plurality of stations; performing data integrity, axle load parameter logicality and cross-site consistency verification on the acquired data, and screening out site combinations meeting spectrogram calculation conditions; on the basis, main vehicle models in the area are identified, effective data are extracted, a direction coefficient, a lane coefficient, a vehicle model distribution coefficient and an axle load distribution coefficient are calculated, and an axle load spectrogram is drawn; furthermore, on the basis of historical measurement data and traffic operation characteristics, the axle load spectrum change trend of the next period is predicted. According to the method, the defects of low classification precision, strong subjectivity and the like caused by fuzzy classification depending on graphical representation in the prior art are effectively avoided, the problem that single-port data are easily influenced by equipment errors and environmental interference to generate deviation is effectively avoided, the reliability and accuracy of the axle load spectrum are improved, and the method is suitable for popularization and application. The method is suitable for various engineering scenes such as road structure analysis and load simulation.

The invention discloses a robust visual SLAM system based on fuzzy classification and differential deblurring, and belongs to the technical field of image processing, multi-sensor fusion and visual SLAM. The system constructs a closed-loop processing framework of fuzzy discrimination-differential deblurring-feature matching enhancement-SLAM integration, and comprises an image and IMU data acquisition module, a fuzzy discrimination module, a differential deblurring module, an improved GMS feature matching module and a visual SLAM core module. The blurring discrimination module combines image gradient features and IMU motion information to realize blurring degree discrimination of the image, and further distinguishes global blurring and local blurring for repairable blurring; the differential deblurring module introduces IMU constraint deblurring or lightweight deblurring processing for different types of blurring so as to meet the SLAM feature extraction requirement; the improved GMS feature matching module improves the matching stability in a fuzzy scene through a multi-scale and adaptive neighborhood mechanism. And the visual SLAM core module fuses the clear image and the deblurred image to realize high-precision positioning and mapping. According to the method, the robustness and real-time performance of visual SLAM in dynamic and fast motion scenes are effectively improved, and the method is suitable for application scenes such as robot navigation, automatic driving, unmanned aerial vehicles and AR / VR.

The application discloses a two-viewpoint collaborative TSK fuzzy classification method based on residual dynamic guidance, which comprises the following steps: obtaining two characteristic viewpoints for the same class of samples; constructing a two-viewpoint deep stack TSK fuzzy classification model; introducing a cross-viewpoint staggered semantic consistency constraint mechanism to train two sub-models in the TSK fuzzy classification model; calculating the output results of each current layer of the two viewpoints to construct a classification residual signal; activating the distribution stability by using a fuzzy information entropy evaluation rule, and constructing a dynamic residual weight by combining the classification residual; constructing a cross-viewpoint projection operator to map the weighted residual guidance information of one viewpoint to the original characteristic space of the other viewpoint, and completing the dynamic mutual guidance and feature updating of the two viewpoints layer by layer. The application retains the inherent explainability of the zero-order TSK model, is lightweight in structure design, and effectively enhances the cross-viewpoint collaboration ability and generalization performance.

The invention relates to a severe convection monitoring method and system based on multi-source data. The method comprises the following steps: collecting multi-source meteorological data; preprocessing the multi-source meteorological data to obtain a preprocessed meteorological data set; on each space grid unit, extracting a multi-source characteristic parameter used for reflecting the severe convection activity from the preprocessed meteorological data set; inputting the multi-source characteristic parameters into a pre-trained scoring model for reasoning analysis, and generating a severe convection score value of each space grid unit; performing fuzzy classification based on the severe convection score value of each space grid unit and a preset fuzzy membership rule to obtain a severe convection grade result of each space grid unit; and calculating the convection development trend of each space grid unit based on the numerical difference between the severe convection score value and the continuous score value at the previous two moments. The method has the effect of improving the precision of severe convection monitoring.

The invention discloses a two-stage decision-making multi-constraint logistics order combination method, and belongs to the technical field of logistics operation optimization. The method comprises an order classification stage: performing accurate classification and fuzzy classification based on order data, and outputting a structured order classification result; in the order combination stage, order combination optimization is carried out by adopting an improved genetic algorithm based on a classification result, and an optimal logistics order combination scheme is output in combination with greedy constraint check and a multi-target fitness function. According to the invention, through two-stage collaborative decision-making, the processing efficiency and optimization speed of large-scale orders are significantly improved; through dynamic adjustment and multi-constraint consideration, the resource utilization rate such as the vehicle loading rate is effectively improved, and the comprehensive cost such as the total driving distance and the illegal operation risk is reduced; the scheme is high in practicability and can adapt to different order scales and network structures. According to the invention, efficient and reliable order combination decision support is provided for logistics enterprises.

The application relates to an interpretable traffic cognition method based on fuzzy theory and belongs to the technical field of artificial intelligence. Real-time traffic data of a traffic scene is organized into an external attribute feature matrix, a traffic cognition feature matrix and an adjacency matrix. The preprocessed external attribute feature matrix is sent into a fuzzy reasoning mechanism, and an attribute influence feature matrix is output after feature calculation. The matrix, the traffic cognition feature matrix and the adjacency matrix are input into a graph convolutional neural network, and then input into a time characteristic capturing network based on a gated recurrent unit. Finally, a prediction result is output and used for attribute influence fuzzy classification and traffic cognition. The application can efficiently extract time and space dependence characteristics between multiple roads in a traffic scene, has higher traffic data cognition performance and interpretability, and solves the problems of low transparency, poor interpretability of a deep neural network and insufficient consideration of external attribute features of a traffic scene in a traffic cognition process.

The invention provides a traffic target detection and inter-frame dynamic adjustment method based on a cascade structure multi-level routing detection model, a medium and equipment. The method comprises the following steps: collecting traffic time sequence data, and establishing a multi-level label structure, the first class being large class labels and the second class being subdivision labels; a universal detection model and an expert model which are cascaded are constructed and trained, the universal detection model carries out large-class classification on the detection data, the expert model is selected according to a routing algorithm, and subdivision classification is carried out through the expert model; and in combination with output results of the general detection model and the expert model, constructing a prediction trajectory combined with time series data, and correcting a preposed detection result by dynamically adjusting and returning a new large-class classification confidence threshold and a subdivision classification confidence threshold. According to the method, the fuzzy classification situation possibly encountered in actual production is fully considered, the advantages of time sequence data and performance limitation of edge equipment are combined, and a better result is achieved in a routing and parameter dynamic adjustment mode.