83 results about "Fuzzy logic inference" patented technology

The invention provides a hybrid model fault early warning method and system based on time sequence prediction and fuzzy logic, and the method comprises the steps: collecting operation and maintenance data of equipment in a continuous operation period, constructing an equipment state feature set, carrying out the feature correlation analysis of the equipment state feature set, generating an equipment state potential vector which reflects a dynamic coupling relation between parameters, and carrying out the fault early warning of a hybrid model. Calling a pre-trained fuzzy logic reasoning model to carry out fuzzy rule matching, and generating a fuzzy state set containing a multi-dimensional fuzzy subset; performing space-time correlation processing on the fuzzy state set to generate a multi-dimensional decision cloud picture, and performing spatial reconstruction on the multi-dimensional decision cloud picture through a quantization weight distribution mechanism to obtain an optimized decision matrix; and generating a fault early warning signal containing a fault early warning level and fault position positioning information based on the confidence distribution characteristics corresponding to the fault types. According to the invention, the accuracy and timeliness of fault early warning can be improved, and reliable decision support is provided for equipment operation and maintenance management.

The invention discloses a rainfall type landslide early warning method based on multi-source environment threshold and nonlinear fusion. The method comprises the following steps: constructing a landslide disaster-inducing factor set; introducing soil humidity, vegetation indexes and evapotranspiration, and calculating a threshold parameter corresponding to the rainfall type landslide event to obtain an environment threshold point set; according to the landslide disaster-inducing factor set, constructing and training a landslide susceptibility model based on progressive learning; adopting a double-layer nonlinear fusion model based on multi-source data fusion and quality perception gating, and combining a landslide susceptibility model to obtain comprehensive risk probability distribution; and performing graded early warning based on the comprehensive risk probability distribution to complete rainfall type landslide early warning. According to the method, on the basis of traditional rainfall parameters, early-stage disaster-pregnant environment factors are introduced, and a multi-source environment threshold value is formed; fuzzy logic reasoning is adopted to realize nonlinear fusion of the threshold information and the landslide susceptibility base map; and dynamically reflecting a regional environmental condition evolution process in combination with an annual iterative updated susceptibility layer, and realizing finer and more adaptive space grading early warning.

The invention discloses an operation risk analysis method and system for a virtual power plant, relates to the field of risk analysis, and solves the problem of insufficient operation stability of an existing virtual power plant, and the method comprises the steps: S1, collecting the operation data of various devices of the virtual power plant; s2, acquiring historical fault data, and constructing a fault rule base; performing fuzzy logic reasoning based on historical fault data to obtain membership values of the abnormal parameters; real-time equipment parameters are collected, calculation is carried out, and a fault judgment value is obtained; fault equipment is judged; s3, calculating the fluctuation condition of the equipment parameters, constructing a fluctuation interval, analyzing and calculating a fault risk value according to the fluctuation interval, judging pre-fault equipment, counting the fault equipment and the pre-fault equipment, and constructing a maintenance equipment list; s4, adjusting the energy supply of the equipment according to the energy supply plan in combination with the maintenance equipment list; the stability of the virtual power plant can be effectively improved.

The invention discloses a charging pile power management system based on the Internet of Things, and particularly relates to the technical field of the Internet of Things, and the system comprises a data collection module which collects the real-time operation data of a charging pile through a sensor, and forms a data set; the data processing module is used for processing the data according to a preset or optimization strategy and generating a processed data set; the scene analysis module performs analysis based on the processing data set and divides the operation scene of the charging pile; the data feature extraction module is used for extracting and analyzing features when an optimization mechanism is triggered; the logic judgment module is used for formulating an optimization strategy through fuzzy logic reasoning and sending the optimization strategy to the data processing module; the power energy efficiency management module is used for adaptively adjusting power distribution and improving the energy efficiency of the system; the data transmission delay can be reduced, the response speed can be improved, a user can obtain smoother experience when using the charging pile, transmission of redundant information is reduced through an optimization strategy, and efficient power management is achieved.

The invention relates to the technical field of agricultural Internet of Things, and discloses an edible mushroom growth environment regulation and control method and system based on multi-source sensing data, and the method comprises the steps: deploying a multi-source sensor network, unifying time synchronization, and carrying out event-driven space-time alignment and three-dimensional mapping. Carrying out multi-dimensional reliability evaluation and environment correction, and obtaining an environment parameter fusion data set by adopting a fuzzy logic reasoning method; performing space-time Kriging interpolation, and obtaining a heterogeneous microenvironment feature database by adopting a multi-source data collaborative correction method; carrying out cross-modal attention fusion; performing multi-scale convolution and time sequence attention classification; carrying out deep reinforcement learning and multi-objective optimization; carrying out online incremental learning optimization to obtain a self-adaptive optimized partition collaborative regulation and control strategy; according to the method, the technical problems of insufficient multi-source data fusion precision, lack of environment spatial and temporal distribution modeling, inaccurate parameter coupling prediction, lack of self-adaptability of regulation and control strategies and the like are solved.

The invention provides a quality detection method and system for magnesium ingot production. The method comprises the following steps: acquiring a visible light image, three-dimensional point cloud data and an infrared thermal image of a magnesium ingot to be detected; constructing a three-dimensional voxel model representing surface geometry and color information of the magnesium ingot; identifying and outputting the type, three-dimensional position and size information of the surface defect of the magnesium ingot; taking an infrared thermal image as a surface temperature boundary condition of a heat conduction model, and taking a defect position and a defect size as a heat conductivity disturbance source in the model; obtaining an equivalent heat conductivity distribution field in the magnesium ingot; on the basis of the spatial non-uniformity of the equivalent thermal conductivity distribution field, calculating to obtain an index for representing internal density; calculating contour conformity parameters; and inputting the surface defect severity parameter, the contour conformity parameter and the index of the internal density into a fuzzy logic reasoning system, performing reasoning according to a preset fuzzy rule library matched with a magnesium ingot downstream application process, and outputting the magnesium ingot quality grade.

The invention relates to the technical field of robot intelligent control, and discloses an inspection robot task scheduling and state switching method for quantifying a state machine, which comprises the following steps: acquiring state machine operation configuration data; performing health degree evaluation on the sensor, navigation, communication and battery systems in an initial state; in the idle state, a segmented charging strategy is adopted, and task priorities are calculated through a neural network; a breakpoint resume and event-driven architecture is adopted to execute tasks in the inspection state; in a warehouse returning state, a deep neural network is adopted to predict return flight energy consumption, and a path is re-planned through multi-objective optimization when the electric quantity is insufficient; processing a control instruction by adopting speed limitation in a manual or mapping state, and starting an SLAM module in the mapping state; and in an abnormal state, a fuzzy logic reasoning system is adopted to calculate an abnormal grade, and recovery waiting, degradation protection or alarm is executed according to the grade. According to the invention, autonomous operation and intelligent decision making of the inspection robot in a complex dynamic environment can be realized.

The invention discloses an industrial robot adaptive control system fused with a situation prediction mechanism, and particularly relates to the field of situation prediction based on a feedforward network and fuzzy logic, comprising the following steps: in the execution process of an industrial robot, obtaining state input data through continuous sampling; and inputting the state input data into a feedforward neural network with a time sequence feature mapping capability to obtain a prediction output result of the task trend of the robot. A dynamic entropy deviation cooperation mechanism between a feedforward neural network and a fuzzy logic reasoning model is constructed to realize modal attribution conflict identification and control right coordination of a critical state of a multi-modal task, so that continuity of a task response path and stable operation of a control system in a fuzzy transition interval are ensured; therefore, the problems of modal dislocation and out-of-control execution caused by lack of entropy weight coupling of feedforward prediction output and fuzzy reasoning judgment in a control right decision domain are solved.

The invention relates to the technical field of unmanned aerial vehicle flight control, in particular to a track tracking control method for a tandem dual-rotor unmanned helicopter, which comprehensively considers factors such as aerodynamic force and inertia force and is completed through structural parameter measurement, aerodynamic force analysis, kinetic equation derivation and model verification and correction. Designing a self-adaptive sliding mode controller, defining a sliding mode surface, designing a control law and a self-adaptive law, ensuring the robustness of the system, and optimizing a control strategy; in combination with model prediction control, establishing a prediction model, defining a target function, improving the track tracking precision through rolling optimization and feedback correction, and correcting prediction errors and interference influences through sliding mode control; a multi-task switching control strategy is designed, task stages are divided, parameters are set, switching conditions are determined, and a fuzzy logic reasoning or neural network algorithm is adopted to realize rapid adjustment of the control strategy. The technology can accurately track the track, enhance the robustness, improve the real-time performance, flexibly adapt to multiple tasks and have a good application prospect.

The invention belongs to the technical field of airport boundary security and protection monitoring, and discloses an airport boundary grading early warning linkage method and system based on fuzzy logic reasoning. According to the method, an airport boundary is subjected to panoramic scanning to generate an infrared and visible light panoramic image, intrusion target detection and identification are performed on a boundary defense area in the infrared and visible light panoramic image through a multi-mode target detection algorithm based on deep learning, intrusion target early warning grade division is performed through a fuzzy logic algorithm, and an intrusion target early warning result is obtained. And carrying out early warning level priority judgment on the intrusion target, carrying out track labeling on a plurality of coordinate points closest to the intrusion target in the surrounding panoramic base map by using an OPENCV algorithm library, displaying the motion track of the current intrusion target, and judging the intention of the intrusion target. According to the invention, different airport environments are adapted through the rule base self-optimization capability, the manual patrol load is reduced while the high reliability is ensured, and the intelligent level and the safety prevention and control efficiency of the enclosure security and protection system are enhanced.

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.

This invention belongs to the field of industrial network performance optimization technology, specifically providing a DDS-5G network collaborative optimization method based on fuzzy logic. By setting static mapping rules, the QoS Policy parameters of the DDS are mapped to the QoS flow identifiers of the QoS flows in the 5G network, thereby achieving initial DDS-5G collaboration. Simultaneously, by establishing a fuzzy logic inference system, using the QoS Policy parameters and real-time network state parameters as input variables and 5QI as the output variable, and by optimizing and adjusting the QoS Policy parameters of the DDS through DQN and updating the fuzzy logic parameters of the fuzzy logic inference system, further DDS-5G network collaborative optimization is achieved. Through a cross-layer adaptation mechanism, efficient collaboration between the application layer and the 5G network is realized, making it suitable for scenarios with high requirements for low latency and high reliability, such as industrial automation and intelligent manufacturing.

This invention relates to the field of speed control technology, specifically to an intelligent lamination and tension control system and method for multi-ribbed belt production lines in washing machines. The system includes: a multi-dimensional state information extraction module, a real-time collaborative control module, a control performance evaluation module, and a fuzzy rule self-tuning module. In this invention, by calculating tension error and lamination position deviation, a basis for dynamic adjustment is provided, eliminating reliance on preset fixed parameters. By invoking a fuzzy logic inference engine, tension error, error change rate, and position deviation are fused and processed to achieve synchronous and precise control of the transmission roller speed and the position of the correction mechanism. This effectively replaces the slow-responding mechanical tension device, solving the problems of tension fluctuation and lamination deviation. Furthermore, a long-term comprehensive performance index is introduced to evaluate the control effect. When performance deteriorates, a gradient descent algorithm is immediately triggered to self-tune the fuzzy rule parameters, continuously optimizing the control strategy and ensuring the system maintains high stability and high accuracy.

The application discloses a charging pile power management system based on Internet of Things and concretely relates to the technical field of Internet of Things, and comprises a data acquisition module for collecting real-time operation data of the charging pile through a sensor to form a data set; a data processing module for processing the data according to a preset or optimized strategy to generate a processed data set; a scene analysis module for analyzing the processed data set and dividing charging pile operation scenes; a data feature extraction module for extracting and analyzing features when the optimized mechanism is triggered; a logic judgment module for formulating an optimized strategy through fuzzy logic reasoning and sending the optimized strategy to the data processing module; and a power energy efficiency management module for adaptively adjusting power distribution and improving system energy efficiency; the application can reduce data transmission delay and improve response speed, so that users can obtain a smoother experience when using the charging pile, and the transmission of redundant information is reduced and efficient power management is realized through the optimized strategy.

The invention belongs to the field of computer system resource allocation based on a specific calculation model, and discloses a display resource allocation system based on fuzzy logic, which comprises the following steps that: the system acquires an acceleration vector of user interaction, and performs authenticity discrimination based on direction consistency of the acceleration vector to generate weighted interaction acceleration; the fuzzy logic reasoning engine maps the weighted acceleration into a resource allocation priority, and meanwhile, the operation cycle of the engine is dynamically adjusted by the core temperature of the system so as to compensate calculation delay; according to the method, resource scheduling is changed from an application state to a user physical intention subjected to authenticity verification, deep synchronization of system response and user attention is realized, delay and lagging of key interaction are avoided, and smooth and harmonious qualitative change of man-machine interaction experience is achieved.

The invention discloses a transformer health degree intelligent evaluation system based on fuzzy logic, and relates to the technical field of fuzzy logic reasoning. According to the test operation data type with the exception identification, the exception type combination and the operation data numerical value interval combination are obtained, then a plurality of multi-mode state combinations are generated, and the initial decision evaluation value is set for each multi-mode state combination; the method comprises the following steps: performing multi-round iteration on an initial decision evaluation value of each multi-modal state combination to obtain a result decision evaluation value, collecting a plurality of items of real-time operation data when a target transformer is put into use, and when it is judged that the real-time operation data is abnormal at a health evaluation time point, judging that the real-time operation data is abnormal; and calling a plurality of multi-modal state combinations according to the type and the numerical value of the real-time operation data, and selecting the multi-modal state combination with the maximum result decision evaluation value to update the residual life duration of the target transformer.

The application provides an automatic turning method and system, and relates to the technical field of power station maintenance, comprising: obtaining first information and second information, the first information is a sensor deployment scheme, sensor data and corresponding turning effect evaluation index of historical turning operation, and the second information is real-time monitoring data; obtaining third information by quantum annealing processing according to the first information; obtaining fourth information by Kalman filtering processing according to the first information and the second information; obtaining fifth information by dynamic programming processing according to the third information and the fourth information; and obtaining sixth information by fuzzy logic reasoning processing according to the first information and the fifth information. Based on quantum annealing processing and dynamic programming processing, combined with graph construction, random forest algorithm and shortest path algorithm and other technologies, the application realizes intelligent design of the optimal control strategy of the turning operation, can automatically adjust the turning operation according to the real-time monitoring data and the turning effect evaluation index, and improves the turning efficiency and precision.

The invention discloses a control method and system for a temperature control instrument of a switch cabinet, and relates to the technical field of intelligent electrical equipment thermal management, and the method comprises the steps: building a thermal effect periodic change function according to the obtained first data of the switch cabinet and a preset period length; constructing a load-heat effect rhythm regulation and control model based on the heat effect periodic change function and the obtained current load data; based on the regulation and control model, generating a switch cabinet heat effect prediction result through machine learning; generating a temperature control response strategy through fuzzy logic reasoning according to the prediction result and the real-time load state data; and regulating and controlling the operation state of the temperature control instrument control system according to the temperature control response strategy. The problem that a traditional switch cabinet temperature control instrument control method is difficult to generate a temperature control response strategy according to the heat effect is solved.

The invention relates to the technical field of bladder internal pressure monitoring and management, and provides a neurogenic bladder internal pressure monitoring and management system based on incremental learning, which comprises a data acquisition module, an embedded hardware platform and a user side prompt module. The data acquisition module is responsible for acquiring physiological parameter information such as bladder internal pressure, abdominal pressure and body position angle in real time. The bladder internal pressure prediction module integrated by the embedded hardware platform establishes a pressure-time relationship in a piecewise linear modeling mode, continuously adjusts model parameters by applying an incremental learning algorithm, and generates prediction data in a future time window; and the multi-modal state identification module receives the prediction data and the current multi-source physiological information, performs comprehensive analysis by applying a fuzzy logic reasoning mechanism, identifies and eliminates the influence of instantaneous interference factors, and outputs the grade evaluation of the bladder state. And the user side prompting module executes a grading early warning strategy according to the state grade and sends intervention prompting information of different emergency degrees to the patient.

The invention discloses an energy efficiency optimization control method for a motor system driven by an industrial internet of things, and provides a three-stage progressive energy efficiency optimization algorithm: in the first stage, a collaborative optimization mechanism of a multi-physics coupling loss model and fuzzy logic reasoning is established; carrying out nonlinear mapping on iron loss, copper loss and mechanical loss by adopting a variable universe adaptive fuzzy controller; in the second stage, a self-adaptive parameter identification engine based on improved particle swarm optimization (PSO) is designed, and motor parameter online drift compensation and loss model coefficient dynamic correction are achieved; and in the third stage, a real-time execution strategy combining model predictive control (MPC) and dynamic flux linkage trajectory planning is implemented, and the amplitude and phase angle of the stator flux linkage are actively adjusted according to a load torque prediction result. The method overcomes the defects that traditional loss model control (LMC) is sensitive to parameters and search control (SC) is slow in convergence.

This invention provides a quality inspection method and system for magnesium ingot production. The method involves acquiring visible light images, three-dimensional point cloud data, and infrared thermal images of the magnesium ingot to be inspected; constructing a three-dimensional voxel model characterizing the geometric and color information of the magnesium ingot surface; identifying and outputting the type, three-dimensional location, and size information of defects on the magnesium ingot surface; using the infrared thermal image as the surface temperature boundary condition of the heat conduction model, and using the defect location and size as the thermal conductivity perturbation source within the model; obtaining the equivalent thermal conductivity distribution field inside the magnesium ingot; calculating an index characterizing the internal compactness based on the spatial non-uniformity of the equivalent thermal conductivity distribution field; calculating the shape contour conformance parameter; inputting the surface defect severity parameter, the shape contour conformance parameter, and the internal compactness index into a fuzzy logic inference system; performing inference based on a preset fuzzy rule base matching the downstream application process of the magnesium ingot; and outputting the magnesium ingot quality grade.

The application discloses a kind of transformer health degree intelligent evaluation system based on fuzzy logic, it is related to fuzzy logic reasoning field.The application obtains abnormal type combination and operation data value interval combination according to the test operation data kind with abnormal identification, and then generates several multimodal state combinations, sets initial decision evaluation value to each multimodal state combination, obtains result decision evaluation value by the initial decision evaluation value of each multimodal state combination for multiple rounds of iteration, starts to collect several real-time operation data from the target transformer put into use, whenever a health assessment time point judges that real-time operation data exists abnormal, according to the kind and the value of real-time operation data, multiple multimodal state combinations are retrieved, the multimodal state combination with the maximum result decision evaluation value is selected to update the residual life length of target transformer.

The invention belongs to the technical field of industrial network performance optimization, and particularly provides a DDS-5G network collaborative optimization method based on fuzzy logic, through a set static mapping rule, a QoS Policy parameter of a DDS is mapped to a QoS flow identifier of a QoS flow of a 5G network, so that preliminary collaboration of DDS-5G is realized, and meanwhile, through establishing a fuzzy logic reasoning system, the QoS flow identifier of the QoS flow of the 5G network is mapped to the QoS flow identifier of the QoS flow of the 5G network. According to the method, the QoS Policy parameter and the real-time network state parameter serve as input variables, the 5QI serves as an output variable, the QoS Policy parameter of the DDS is optimized and adjusted through the DQN, the fuzzy logic parameter of the fuzzy logic reasoning system is updated, DDS-5G network collaborative optimization is further achieved, efficient collaboration between an application layer and the 5G network is achieved through a cross-layer adaptation mechanism, and the optimization efficiency of the DDS-5G network is improved. The method is suitable for industrial automation, intelligent manufacturing and other scenes with high requirements for low time delay and high reliability.

This invention belongs to the field of wind power technology and relates to an integrated control system and method for wind turbine pitch control and support structure vibration reduction. An integrated control system for wind turbine pitch control and support structure vibration reduction based on fuzzy logic reasoning includes a tower fuzzy active control system and a fuzzy pitch control system. The tower fuzzy active control system includes a tuned mass device, an active actuator, and an actuator fuzzy controller. The tuned mass device is located at the platform of the wind turbine tower and connected to the inner wall of the tower, vibrating when the tower is subjected to external excitation. An active actuator is provided between the tuned mass device and the inner wall of the tower. The active actuator is connected to the actuator fuzzy controller and outputs a force under the control of the actuator fuzzy controller, acting on the tuned mass device. The fuzzy reasoning-based control strategy of this invention can maintain stable power generation tracking and effectively reduce the load and vibration response of the wind turbine under different operating conditions, significantly improving the stability and safety performance of the support tower.

The invention discloses a water level threshold value dynamic adjustment method and system based on automatic drainage of a transformer substation, and relates to the technical field of transformer substation hydraulic monitoring, and the method comprises the following steps: obtaining water level data of a transformer substation drainage pipe, and classifying water level states based on the water level data to obtain a water level state set; constructing a membership degree input set based on the water level state set, and performing fuzzy logic reasoning on the membership degree input set to obtain a drainage pump control signal; adjusting an interval range of a preset membership function according to the drainage pump control signal to obtain a water level fuzzy decision set; state transition is carried out on a drainage pump control signal based on a water level fuzzy decision set, a transition control threshold sequence is obtained and discretized into a drainage pump start-stop command capable of being recognized by a PLC, and the problem that drainage is not timely or frequent start-stop is possibly caused due to the fact that a drainage threshold value is fixed and inflexible due to water level fluctuation in substation drainage pump control is solved.

This invention discloses an energy-saving operation method for hydraulic motors based on pressure detection and energy replenishment, specifically relating to the field of energy-saving control. The method includes: firstly, collecting, filtering, and preprocessing multiple operating parameters of the hydraulic system and drive motor using time synchronization; then constructing a dimensionless characteristic function from multiple dimensions and achieving nonlinear fusion through adaptive weight allocation to obtain a comprehensive state coupling value; constructing a pressure advance prediction model based on historical data, and performing consistency verification and confidence analysis on the comprehensive state and predicted state; using the comprehensive state coupling value, pressure prediction value, and confidence value as inputs, employing fuzzy logic reasoning to output motor speed and accumulator valve group control commands, ultimately driving the actuator and transmitting the operating parameters back to form a closed loop; this invention improves the system's dynamic response and anti-interference capability, optimizes the motor operating range while ensuring pressure stability, and improves energy utilization, making it suitable for efficient and energy-saving operation of hydraulic systems under complex working conditions.

The application provides a kind of hybrid model fault early warning method and system based on timing prediction and fuzzy logic, device state feature set is constructed by collecting the operation and maintenance data of equipment in continuous operation cycle, feature correlation analysis is carried out on device state feature set, device state potential vector reflecting the dynamic coupling relationship between parameters is generated, fuzzy rule matching is carried out by calling pre-trained fuzzy logic inference model, and fuzzy state set containing multi-dimensional fuzzy subset is generated;Fuzzy state set is processed by space-time correlation, and multi-dimensional decision cloud chart is generated, which is reconstructed in space by quantization weight distribution mechanism, and the optimized decision matrix is obtained;Based on the confidence distribution characteristics corresponding to each fault type, the fault early warning signal containing fault early warning level and fault location positioning information is generated.The application can improve the accuracy and timeliness of fault early warning, and provides reliable decision support for equipment operation and maintenance management.

The invention discloses a trajectory tracking and stability cooperative control method for a multi-axis distributed driving vehicle, and provides a hierarchical control architecture. The upper-layer trajectory tracking controller comprises a self-adaptive preview linear quadratic regulator based on fuzzy logic reasoning, and the self-adaptive preview linear quadratic regulator dynamically adjusts the preview distance according to the vehicle speed and the acceleration and outputs a steering angle instruction; and the seven-section S curve speed planner generates a smooth speed and acceleration curve. The lower-layer stability and torque distribution controller comprises an equivalent sliding mode controller for calculating an additional yaw moment based on the yaw velocity and the side slip angle error; and based on a torque distributor of quadratic programming, introducing an inter-axle yawing moment coefficient as a geometric constraint, tracking a required torque, and minimizing a tire attachment utilization rate and a slip rate as a target distribution torque. Through cooperation of the upper layer and the lower layer, high-precision trajectory tracking, high robust stability and longitudinal smoothness are considered, and the comprehensive performance of the multi-axle vehicle under the limiting working condition is remarkably improved.

The application relates to the technical field of unmanned aerial vehicle detection, and discloses an unmanned aerial vehicle detection method and system based on infrared image analysis. The method collects infrared image sequence data within a preset time window; based on the sequence data, environment stability analysis is performed to determine an image heat distribution stability factor, an environment temperature stability factor and a motion characteristic stability factor; fuzzy logic inference rules are applied to infer an infrared response interval in combination with the three factors, and based on the interval, target analysis is performed on the infrared image sequence data according to a preset detection item set to obtain a periodic detection data set; and the set is identified by using an infrared target identifier to obtain an unmanned aerial vehicle detection result. The method can fully consider dynamic changes in the environment by analyzing the environment stability factors, combining fuzzy logic inference to determine the infrared response interval, and then performing target analysis and identification, thereby improving the adaptability and effectiveness of unmanned aerial vehicle detection, and being suitable for unmanned aerial vehicle detection scenes in complex environments.

The application relates to a significant scene graph generation method and device based on fuzzy logic reasoning relationship importance, a scene graph generation model test method and device, and a computer device. The method comprises the following steps: S100, calculating the importance score of the real relationship of each image sample in a target data set; S200, constructing a target detector; S300, constructing a semantic extractor and an instance feature refinement module; S400, constructing a bounding box modeling module and a feature splicing module; S500, constructing a feature fusion module and a relationship representation modeling module; S600, constructing a relationship loss weighting module; S700, under the condition that the number of training image samples reaches the batch processing size, returning to step S200; under the condition that all image samples in the training set are read, entering step S800 to output a scene graph generation model. The method can flexibly and comprehensively evaluate the importance of the relationship without reducing the number of relationship samples.