17 results about "Fuzzy mapping" patented technology

The invention discloses a visual scene graph generation method based on differentiable fuzzy logic reasoning, and solves the technical problems of semantic fuzziness, logic common sense deficiency and the like during long tail relation processing in the prior art. The method comprises the following steps: inputting a training image into a target detection module, and respectively outputting corresponding high-dimensional geometric embedding vectors to a relation classifier and a fuzzy mapping layer; outputting a visual prediction branch by the relation classifier; meanwhile, the fuzzy mapping layer outputs a fuzzy membership degree vector; the logic tensor reasoning module is combined with a common sense rule in an external knowledge base, simulates a logic reasoning process by utilizing a differentiable logic operator, and outputs a logic reasoning branch of which the relation of each pair of objects meets a preset logic rule in the training image; a gating residual fusion module performs weighted fusion on the visual prediction branch and the logical reasoning branch to generate a scene graph triple of the training image; and finally, training the network model, inputting a test image into the trained model, and outputting a corresponding visual scene graph.

The application discloses a kind of building inspection method and system based on existing building structure, the method includes the following steps: based on multi-period point cloud registration calculation three-dimensional displacement, fusion principal component generates structure deformation stability index, identifies potential instability area;Through multi-scale surface fitting extraction curvature and normal vector evolution gradient, combined with signal-to-noise ratio weighting, construct local distortion sensitivity index;Gradient entropy and roughness are extracted to abnormal area, and material degradation confidence factor is generated by fusing reflection intensity variation;Based on spatial gridding calculation degradation concentration, coupled maintenance frequency and obtained weak degree score by fuzzy mapping, combined with importance weight output risk priority;Deformation rate sequence is extracted to high-risk component, trend is fitted and cumulative score is constructed, and weak score is double-target normalized, and by pareto front and euclidean distance solution, determine the highest position of comprehensive risk and label failure urgency level.The application improves the identification precision of existing building structure deformation.

The application discloses a MOM software quality optimization method and system based on a dynamic fuzzy scene, which comprises the following steps: collecting heterogeneous data in multiple dimensions from operation monitoring, user interaction and business process tracking; extracting a core feature subset through information measurement after cleaning and normalization; defining an operation scene in combination with system load and other features and core feature performance; constructing a double-layer weight model and dynamically adjusting the model; establishing a fuzzy mapping of features and preset evaluation levels for each scene to form a judgment matrix; determining the quality level according to the maximum membership degree after the weight and the matrix are combined; and generating software configuration, resource allocation or workflow adjustment instructions based on the evaluation results and executing the instructions to realize closed-loop optimization. The method realizes precise quantitative evaluation and automatic optimization.

The application discloses a weather radar wind turbine clutter recognition method and system based on fuzzy reasoning, and belongs to the technical field of fuzzy reasoning. The monitoring area of a target weather radar is divided into multiple echo observation subunits according to distance gates and scanning azimuth angles, and the reflectivity factor, radial velocity, spectral width and differential reflectivity of each echo observation subunit are continuously acquired within a preset data acquisition time sequence. By analyzing the discrete characteristics of the reflectivity factor and the radial velocity in the time sequence, echo stability characteristic quantities are constructed, echo shape consistency characteristic quantities are constructed based on the consistency deviation of the spectral width and the differential reflectivity in the neighborhood, the echo stability characteristic quantities and the echo shape consistency characteristic quantities are subjected to fuzzy mapping, and the wind turbine clutter and meteorological echoes are distinguished and recognized through a fuzzy comprehensive judgment quantity, so that the accuracy and robustness of the wind turbine clutter recognition are improved, and the weather radar data quality control under complex background conditions is suitable.

The invention belongs to the technical field of crop disease prevention and control, and particularly relates to a planting method for preventing cucumber fruit blotch germs and green mottle viruses, which comprises the following steps: acquiring soil and crop historical pathogen data, environment monitoring data and cucumber seed batch information of a planting area; querying supply chain quarantine records of the seeds, and carrying out standardization treatment on the seeds; after planting, the disease probability of each growth stage is predicted through a risk assessment algorithm, a dynamic monitoring plan is generated in combination with a fuzzy mapping algorithm, plant samples are collected, PCR detection of melon bacterial fruit blotch and RT-PCR detection of cucumber green mottle mosaic virus are carried out, and a quarantine report is issued. Based on the monitoring plan, the quarantine report and the real-time environment data, judging a comprehensive risk level, triggering a corresponding field intervention scheme, and recording an execution result to form an intervention log; after planting is finished, a prevention and control efficiency report is generated. Therefore, the problems of insufficient disease detection, lack of intervention measures and the like in the prior art are solved.

The application discloses an arc length control method, system, device and medium for electric arc additive manufacturing, and relates to the field of additive manufacturing; the control method comprises the following steps: acquiring image data of a target part; determining arc length data by using an image edge data detection method according to the image data; obtaining a fuzzy error by using fuzzy mapping according to the arc length data and set reference arc length data; determining a control quantity according to the fuzzy error; the control quantity comprises a pulse load ratio; and the deviation of the arc length data of the target part in the manufacturing process is compensated according to the control quantity, and the target part is manufactured by using a non-gas shielded welding method; and the application can realize automatic control of the arc length.

The invention provides a semantic-driven cold-chain logistics path dynamic planning method and system, and belongs to the technical field of intelligent logistics. In order to solve the problem that existing cold chain distribution is difficult to process unstructured environment information and customer flexible demands, the invention constructs a collaborative architecture of environment perception and scheduling decision, and specifically comprises the following steps: S1, based on multi-source data of cold chain logistics, defining road network environment perception and customer demand analysis rules, generating a semantic feature vector containing a road network state component and a customer demand component; s2, analyzing the semantic feature vector, and constructing a distribution constraint model by using a dynamic correction and fuzzy mapping method; s3, constructing a multi-target distribution total cost function based on the rule and the model; and S4, performing global optimization on the total cost function by adopting a parameter adaptive improved genetic algorithm, and generating an optimal cold chain distribution path and scheduling instruction. According to the method, natural language environment information can be converted into constraint conditions of a mathematical model, accurate response to a time-varying road network and refined satisfaction of customer demands are realized, and the total cost and the goods damage rate of cold chain distribution are effectively reduced.

This invention relates to a remote sensing image scene classification method based on uncertain visual-language alignment, comprising the following steps: acquiring remote sensing images as training and testing sets; using a CLIP dual encoder to map the original image and text to a shared point representation space; the uncertain encoder predicting each embedding vector along two paths; internally, the uncertain encoder performing two-level interactions on the input embedding vectors; globally modeling the image and text as two Gaussian distributions based on the mean and variance vectors, maximizing the image-text similarity that should be correctly matched in the entire batch; acquiring new remote sensing images and inputting them into the trained model, which then classifies the scenes in the remote sensing images. This application captures fuzzy mappings between modalities in the probability space, providing a more generalized representation for downstream remote sensing image scene classification tasks.

The present application belongs to the technical field of industrial machine vision, and particularly relates to an industrial workpiece surface defect detection method and system based on a TSK fuzzy mapping. The present application aims at the problems of complex noise in industrial field environment, poor robustness of defect detection model and lack of explainability, and constructs a dual-view detection framework. Among them, the TSK fuzzy module maps image features into defect rules with clear physical semantics, such as strip texture, point pit, etc.; in the training stage, the cross-view gradient consistency constraint is introduced as an anti-noise self-calibration mechanism, which forces the model to keep the optimization direction consistent under noise interference. Experiments show that the present application has an accuracy of 93.84% in the task of hot-rolled steel strip surface defect detection in the environment simulating sensor noise interference, effectively improving the reliability and traceability of industrial quality inspection.

The invention relates to a load balancing and loss optimizing system and method for a dry-type transformer. The system comprises an attribute acquisition and storage module, a spatial correlation graph construction module, a mapping rule module, an evaluation module, a reverse fuzzy control module, a dynamic adjustment module and a closed loop verification module. The method comprises the steps of collecting static and dynamic attributes of a transformer and a load to establish a database; constructing a weighted undirected graph for quantizing the spatial association relationship; determining a fixed mapping and fuzzy mapping relationship between the load and the transformer based on a preset rule; continuously evaluating the overload state of the transformer and the system loss; when overload is detected, calculating a power distribution coefficient and a membership degree adjustment step length through reverse fuzzy control, and dynamically adjusting load distribution; and the adjustment effect is optimized through closed-loop verification. According to the invention, the coordination of load balancing and loss optimization is realized through differential mapping and accurate control, and the problems that the traditional regulation and control mode is large in sweep range and easy to cause power supply impact are effectively solved.

The application provides a kind of intelligent welding current adaptive regulation method and system, belongs to industrial control system technical field.The method realizes the online accurate identification and classification of hidden welding defects such as internal incomplete penetration of weld, porosity, etc.by adopting deep convolutional neural network;At the same time, the current waveform data corresponding to the time interval of the welding defect is accurately matched by timestamp, the inverse mapping model of historical defect characteristics and current parameters is established and the current deviation prediction value is output, and the optimal current compensation and waveform correction function are iterated out by gradient descent optimization algorithm, which solves the problem of fuzzy mapping of defect space characteristics and current time parameters and lack of targetedness of parameter adjustment;Finally, the accurate current regulation instruction of the un-welded area is generated, and the adaptive adjustment of welding current according to different welding defect types is realized, so as to improve the welding quality.

This invention discloses a method, device, and storage medium for multi-parameter fusion sludge discharge control in sedimentation tanks of steel plants, within the field of wastewater treatment technology for the steel industry. The method includes: acquiring real-time multi-parameter data; calculating pre-calibrated multi-parameter data based on the real-time multi-parameter data and theoretically predicted values ​​of pre-acquired multi-parameter data; performing fuzzy mapping on the pre-calibrated multi-parameter data to obtain the membership degree of each parameter; constructing a fuzzy output set from all triggered rules in a pre-defined fuzzy rule set based on the membership degree of each parameter; and defuzzifying the fuzzy output set to obtain the sludge discharge pump frequency conversion command. This invention can alleviate the technical problems affecting sludge discharge efficiency caused by excessive or insufficient sludge discharge leading to water quality exceeding standards, interference of traditional sensors by ferromagnetic sludge, response lag due to manual reliance, and wasted water and electricity costs in existing technologies.

This invention relates to the field of page-turning clock technology, and more particularly to a page-turning clock flipping drive method and system supporting voice interaction control. The method collects historical operating data of the drive unit, extracts voice parsing response data to determine the response deviation factor of command parsing, and quantifies the uncertainty of voice interaction; acquires historical pose deviation data, uses error membership functions for fuzzy mapping, calculates the deviation membership degree, and determines the correction cost of flexible closed-loop feedback; integrates the feedback correction cost and the response deviation factor to dynamically calculate the drive compensation coefficient; and dynamically adjusts the output torque of the page-turning drive module in real time based on this coefficient, achieving end-to-end adaptive optimization from command reception to action execution. This effectively solves the problems of inaccurate command response, stiff drive process, and high noise in traditional page-turning clocks under complex sound fields, significantly improving the smoothness, accuracy, and robustness of the page-turning action.

This invention discloses an intelligent closed-loop rehabilitation method and system based on multi-source information fusion, fatigue evolution prediction, and adaptive regulation. It collects inertial motion signals and electromyographic signals through multimodal wearable sensors. After signal preprocessing and time synchronization, a multidimensional correlation-sensory attention coding mechanism and a gated recurrent network are used to perform feature encoding and future signal prediction on the multidimensional time-series signals. Based on the predicted signals, a multidimensional fatigue index system is constructed to achieve continuous, feedforward assessment of fatigue states during training. Furthermore, a fuzzy mapping method is used to map the multidimensional fatigue states into four risk levels, and training parameters are dynamically adjusted according to the risk level to form real-time, intelligent closed-loop training control. This invention can improve the scientific, precise, and individualized level of rehabilitation training while ensuring training safety, and is particularly suitable for rehabilitation training of patients with neurological diseases and those with impaired motor function.

The invention discloses a closed-loop control image deblurring method for guiding inverse process solution through a blurring mechanism, and relates to the field of deep learning and image deblurring calculation. The method aims at solving the problem that an existing algorithm depends on pairwise fuzzy-clear image pairs and is difficult to adapt to real image deblurring. The method comprises a mechanism prior guidance module, a deep coding and decoding network, a fuzzy mapping space converter, a fuzzy kernel extractor and a loss cost calculation module. A blurring kernel and a clear image initial value are estimated through a blurring generation principle, a blurred image is reconstructed through a deep coding and decoding network, double-closed-loop cross learning is realized by using a loss cost result, and regularization constraint is introduced to improve optimization stability. According to the method, a large amount of training data is not needed, high-quality deblurring can be achieved in a few-sample scene through closed-loop optimization and a frequency domain information retention mechanism, the method is particularly suitable for high-speed motion blurred scenes such as vehicle-mounted image collection, and the accuracy of subsequent image recognition and analysis is effectively improved.

The application discloses a sliding input method, device and equipment and a readable storage medium. First, a sliding track of a user on a virtual keyboard is collected; then, feature points and passing points are determined, the feature points are sliding points capable of dividing the sliding track into multiple track segments, and the center points of keys other than the keys containing the feature points in the keys corresponding to each sliding track segment are the passing points; based on the coordinate keys and error correction keys corresponding to the feature points and the coordinate keys corresponding to the passing points, a pinyin link corresponding to the sliding track is determined; finally, based on the error correction keys, the pinyin link corresponding to the sliding track is decoded to obtain a candidate result. In the scheme, the pinyin link space can be increased through the key fuzzy mapping of the feature points, and thus the correct pinyin link corresponding to wrong sliding, multiple sliding and less sliding can be found, so that the decoding effect of the sliding track is improved.

The application discloses a visual scene graph generation method based on differentiable fuzzy logic reasoning, and solves the technical problems of semantic ambiguity and lack of logical common sense in the prior art when processing long-tail relations. The application inputs a training image into a target detection module, and respectively outputs corresponding high-dimensional geometric embedding vectors to a relation classifier and a fuzzy mapping layer; the relation classifier outputs a visual prediction branch; at the same time, the fuzzy mapping layer outputs a fuzzy membership vector; a logical tensor reasoning module combines common sense rules in an external knowledge base, simulates a logical reasoning process by using differentiable logical operators, and outputs a logical reasoning branch of each pair of object relations in the training image satisfying a preset logical rule; a gating residual fusion module performs weighted fusion on the visual prediction branch and the logical reasoning branch to generate a scene graph triple of the training image; and finally, the network model is trained, a test image is input into the trained model, and a corresponding visual scene graph is output.