68 results about "Fuzzy logic" patented technology

This application belongs to the field of power systems and specifically discloses a method for configuring energy storage in self-organizing grid distribution substations. A closed-loop decision-making process improves the accuracy of energy storage configuration. Specifically, this application uses an improved generative adversarial network to generate basic scenarios, accurately modeling the complex time-series characteristics of load and photovoltaic output, providing a reliable basis for evaluation. Secondly, fuzzy logic is introduced to correct for climate and user behavior factors, generating a target scenario set that covers extreme and special operating conditions. Furthermore, a simulation model is run based on the target scenario set, accurately quantifying the expected unsupplied power under different energy storage capacities, transforming the assessment of power supply capability from qualitative to quantitative, solving the core problem of lacking quantitative support. Finally, this quantitative indicator is selected as the core constraint, and the solution is performed with the goal of minimizing the entire life cycle cost, ensuring that the scheme achieves economic optimization while meeting power supply reliability requirements, thereby solving the problems of unscientific configuration results and insufficient reliability.

The application discloses a UAV robust path planning method and system for uncertain dynamic environment, relates to the field of path planning, and realizes real-time tracking of dynamic obstacles by using EKF, and constructs a probability repulsive field based on Mahalanobis distance, effectively solves the sensing vulnerability and static model limitation of a traditional APF algorithm, and provides a robust dynamic obstacle avoidance guide for random tree expansion; secondly, a fuzzy logic adaptive step (FLC-AS) module is designed; the module intelligently adjusts the expansion step according to the risk degree of a local environment and an exploration stage, and realizes dynamic balance of exploration safety and search efficiency; comparison and analysis of the method and various mainstream algorithms show that the PFLS-RRT* algorithm has excellent adaptability in a complex environment, and achieves the best comprehensive performance in key indexes such as path quality, planning efficiency and robustness.

This invention relates to the field of e-commerce transaction technology, and more particularly to a digitally driven agricultural supply chain demand analysis method and system. The invention includes: collecting agricultural product attribute data from the supply side and pre-setting tags to construct a knowledge graph containing supply nodes; extracting explicit search instructions and implicit descriptive text from the demand side, generating customer demand tags and mapping them to demand nodes; constructing a supply-demand relationship graph based on variety logic, geospatial factors, and logistics timeliness; dynamically allocating explicit and implicit demand weights to identify the optimal supply node and generate matching instructions; calculating semantic deviation values ​​using fuzzy logic based on transaction feedback, and dynamically correcting graph weights through a reverse feedback mechanism. This invention achieves precise alignment under fuzzy sensory preferences and flexible sourcing and allocation during stockouts, constructing a closed-loop optimization mechanism from transaction feedback to attribute correction, significantly improving the accuracy of supply-demand matching, contract fulfillment resilience, and the system's intelligent decision-making level for non-standard agricultural products.

This invention proposes a method and system for recommending English annotation methods with low forgetting rate based on the Analytic Hierarchy Process (AHP), belonging to the fields of optimization and intelligent decision theory. First, from the perspective of English learners, this invention analyzes and identifies five key factors affecting the rate of forgetting English vocabulary. Then, using the Analytic Hierarchy Process (AHP), a weight matrix that passes consistency detection is constructed for each influencing factor, and the actual influencing factor values ​​are normalized based on fuzzy logic. Based on the weight matrix that passes consistency detection and the normalized influencing factor scores under different text annotation methods, the estimated forgetting rate of English vocabulary for English learners under the corresponding annotation methods is obtained. The annotation method with the lowest estimated forgetting rate is selected as the optimal recommended annotation method to assist English learners in vocabulary learning. Finally, a one-month experimental verification of the proposed recommendation algorithm is conducted, and the experimental results demonstrate the effectiveness of this invention.

This invention discloses an intelligent oil pump oil quality monitoring method based on multi-source data fusion, belonging to the field of transformers. The method includes multi-source data acquisition, data processing, data fusion, result display, and long-distance data transmission: First, multi-dimensional raw data of the oil pump pipeline is collected through micro-water, pressure, and flow sensors and transmitted to a microcontroller control board; then, the raw data is denoised and filtered to obtain a standardized dataset; subsequently, a multi-source data fusion algorithm combining weighted average method and fuzzy logic is used to process the dataset, constructing an oil quality status assessment model to determine whether the oil quality has deteriorated; next, the monitoring results are visualized locally; finally, long-distance transmission of monitoring data and local-remote data synchronization are achieved. This invention improves the accuracy of oil quality monitoring through multi-parameter collaborative monitoring and data fusion, realizing local real-time display and remote real-time monitoring of oil quality status, adapting to the operational needs of transformer maintenance sites.

This invention proposes an innovative model predictive control-based current control method and system for permanent magnet synchronous motors (PMSMs), aiming to address the problems of slow dynamic response and low control accuracy in traditional control methods when facing parameter variations and external disturbances. By constructing an accurate mathematical model, this invention not only improves the dynamic response speed and control accuracy of the motor but also significantly reduces torque ripple, thereby enhancing the stability and reliability of motor operation. Furthermore, the introduction of intelligent algorithms such as neural networks or fuzzy logic, as well as adaptive control algorithms, further enhances the system's adaptability and robustness. The application of space vector modulation technology optimizes the current waveform, further reducing torque ripple and improving the smoothness of motor operation. The control method of this invention is suitable for industrial applications with extremely high requirements for dynamic performance and control accuracy, demonstrating its broad application potential in the field of modern industrial motor control.

The application discloses a dual-polarization radar hail size classification method, equipment, medium and product, relates to the technical field of radar meteorological detection and disaster weather monitoring, and comprises the following steps: acquiring hail data sets of S-band radar and C-band radar respectively; the hail data set comprises ground hail reports of different sizes of hail; the radar elevation data and the ground hail report are matched in space and time, and the polarization quantity of each elevation and the radar sampling body height are obtained; the polarization quantity of different sizes of hail is classified into the corresponding height interval according to the radar sampling body height, and the polarization quantity statistical data set is obtained; the membership function corresponding to each polarization quantity is selected based on the radar band and the height interval; the hail size classification result of the distance library is determined according to the observation value of the polarization quantity of each distance library and the membership function corresponding to the polarization quantity by using a fuzzy logic method, and the application can realize the identification of the hail size classification conforming to the domestic hail size classification, and improve the accuracy of the identification result.

The application discloses a phase modifier automatic start-stop control method based on energy consumption self-adaptive optimization, electronic equipment and a storage medium. The method comprises the following steps: acquiring multi-source operation data of the phase modifier and performing pretreatment and feature extraction, and constructing a standardized feature matrix; based on the matrix, a device dynamic energy consumption benchmark model is constructed by combining a long short-term memory network with a self-attention mechanism to predict a theoretical energy consumption baseline, and the theoretical energy consumption baseline is fused with power grid dispatching and load prediction data, and a future reactive power demand prediction sequence and a marginal energy consumption coefficient sequence are synchronously output through a time series convolution network; based on the above sequence, a multi-class energy consumption model containing start-stop transient, no-load and load operation loss is constructed, a dynamic start-stop decision boundary is generated through multi-objective optimization, and finally a start-stop control instruction resistant to disturbance is output by combining a sliding window comparison and fuzzy logic processing. The application realizes adaptive optimization of the phase modifier start-stop control, and improves the economy and reliability while ensuring the stability of the power grid.

This invention proposes a method and system for reducing the order of FPSO models by integrating time-recurrent neural networks and fuzzy logic, belonging to the field of marine engineering structural analysis and intelligent modeling technology. Addressing the problem of large degrees of freedom and low computational efficiency in FPSO finite element models, this invention extracts key degrees of freedom from the high-dimensional finite element model to construct low-dimensional feature vectors. It then uses a fuzzy logic system to perform regularized modeling of the dominant mechanical properties of the structure, achieving initial order reduction. Furthermore, a time-recurrent neural network is introduced to compensate for high-order residual effects that are difficult to accurately characterize using the fuzzy logic model, ultimately resulting in a low-dimensional, high-fidelity reduced-order model. The proposed method significantly reduces the degree of freedom and parameter dimension of the finite element model while maintaining the main mechanical properties of the structure, effectively reducing computational complexity and improving the model's applicability and practical engineering value in applications such as rapid finite element analysis, online structural response prediction, and engineering decision support.

This invention belongs to the field of wastewater treatment control, specifically relating to a collaborative control system for wastewater treatment equipment based on the Internet of Things (IoT). The system includes a multi-dimensional water quality sensing device, an edge computing gateway device, a long short-term memory neural network prediction device, a fuzzy logic decision controller, an aeration frequency converter, and a cloud-based monitoring and collaboration platform. This invention collects water quality parameters in real time through the sensing device. After preprocessing by the gateway, the neural network prediction device deeply analyzes the dissolved oxygen evolution trend. The fuzzy logic decision controller combines the predicted trend with real-time feedback to perform inference calculations, driving the aeration frequency converter to dynamically adjust the air supply. By deeply integrating time-series prediction capabilities with expert decision-making experience, this invention constructs a predictive sensing and decision-making closed loop, solving the problem of large time-delay control lag in the wastewater biochemical treatment process. This ensures stable effluent compliance while reducing aeration energy consumption and improving system robustness.

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.

Graphical element search technique selection, fuzzy logic selection for anchors and targets, and hierarchical graphical element identification for robotic process automation (RPA) are disclosed. The fuzzy logic selection of anchors and targets may be part of a larger, tiered, or hierarchical process for identifying graphical elements in the UI. When a selector for a UI element is not found with at least a confidence threshold, similar elements potentially corresponding to the selector for a UI element target may be searched based on fuzzy matching of the target and corresponding anchor(s). Geometric matching may also be employed between the target UI element and its respective anchor(s). The combination of fuzzy matching and geometric matching may allow for more flexible and accurate identification of the exact selector with which an RPA robot is attempting to interact.

This application relates to the field of multi-terminal flexible DC transmission system equipment, specifically to a method and system for adaptive adjustment of droop coefficient in a multi-terminal flexible DC transmission system. The method includes: real-time monitoring and acquisition of local electrical quantities; inputting the electrical quantities as input variables to a main fuzzy logic controller; outputting an initial adjustment amount for the droop coefficient; inputting the absolute values ​​of the real-time power margin deviation and DC voltage deviation to the corresponding auxiliary fuzzy logic controllers to obtain a power margin weighting factor and a DC voltage weighting factor, which are then used to weight and correct the initial adjustment amount to obtain the droop coefficient adjustment amount; adding the droop coefficient adjustment amount to the initial droop coefficient to obtain the real-time droop coefficient; and performing amplitude limiting processing on the real-time droop coefficient to obtain the final real-time droop coefficient; applying the method to the droop controller of the corresponding converter station to complete the adaptive adjustment of the converter station's output characteristics, thus forming a hierarchical intelligent decision-making system.

The application provides a centralized control automation strategy optimization method and system based on fuzzy logic, which comprises the following steps: firstly, collecting real-time operation state data of power grid operation equipment in a target centralized control area, covering power grid operation parameters and parameter fluctuation ranges; then, performing fuzzy membership processing on the data according to the parameter fluctuation ranges to generate a fuzzy rule set containing multiple fuzzy rules, each fuzzy rule corresponding to parameter dynamic adjustment logic; based on the fuzzy rule set, using fuzzy logic to optimize the current control strategy to generate an optimized control strategy containing control instructions and trigger conditions; then, sending the strategy to the execution equipment according to the trigger conditions and monitoring execution feedback data; verifying the validity of the strategy according to the matching result of the execution feedback data and a preset threshold value, and then dynamically adjusting the optimized control strategy, which can effectively optimize the centralized control automation strategy and improve the power grid operation control effect.

This application provides a reservoir permeability prediction model generation and prediction method, apparatus, equipment, and medium, relating to the field of petroleum exploration technology. The model generation method includes: generating multiple target data; determining multiple reservoir permeability intervals and their corresponding target datasets based on the target data; constructing membership functions corresponding to the reservoir permeability intervals based on fuzzy logic and determining the parameter values ​​in the membership functions; determining the target sample set corresponding to each reservoir permeability interval according to the membership degree corresponding to each target data under different membership functions; dividing the target sample set corresponding to the reservoir permeability interval into a first sample set and a second sample set, and training the initial reservoir permeability prediction model separately to obtain two target reservoir permeability prediction models corresponding to the reservoir permeability interval. This application can effectively solve the problem of low prediction accuracy in predicting reservoir permeability based on the correlation between porosity and reservoir permeability.

This invention discloses an intelligent fall monitoring device, belonging to the field of fall monitoring technology, aiming to solve the problem of accuracy in fall monitoring and prediction for the elderly. It employs a collaborative artificial intelligence model architecture, integrating two different-dimensional artificial intelligence models for risk assessment. Model AI1, based on fuzzy logic, classifies fall risk levels into five levels: normal, low, medium, high, and emergency by real-time monitoring of vital signs such as heart rate, blood pressure, and blood oxygen saturation. Model AI2, based on deep belief networks, predicts risk by analyzing daily living activity patterns such as sitting, standing, walking, running, and jumping. The prediction results of the two models are input into a meta-model for comprehensive decision-making, generating the final low, medium, and high risk level prediction. For real-time fall monitoring, this invention proposes a hybrid monitoring algorithm, combining rule-based triggering based on acceleration and posture angle thresholds with AI fine-grained classification to achieve low latency and high accuracy.

The present application relates to the technical field of aircraft icing prediction, and particularly relates to a hierarchical prediction method and system for aircraft icing, comprising the steps of: obtaining hierarchical indexes in a flight process; performing hierarchical processing on the hierarchical indexes to obtain at least one hierarchical membership degree; obtaining cloud top temperature in the flight process; determining a cloud top temperature membership degree according to the cloud top temperature and a corresponding cloud top temperature membership degree function; forming at least two groups of membership degrees according to the at least two hierarchical membership degrees and the cloud top temperature membership degree; calculating at least two initial icing potentials according to the at least two groups of membership degrees and an initial icing potential model; calculating at least two final icing potentials according to the at least two initial icing potentials; and outputting corresponding hierarchical prediction results according to the at least two final icing potentials. The present application can greatly improve the accuracy and efficiency of aircraft icing prediction with the aid of fuzzy logic.

The application discloses a game artificial intelligence character dynamic interaction method and system fusing multi-modal emotion feedback, and the method comprises the following steps: collecting player in-game interaction behavior data and terminal device sensor physiological data in real time; pre-processing and feature fusing are performed on multi-source heterogeneous data; the fused features are input into a pre-trained CNN-LSTM multi-modal emotion recognition model, and a high-confidence player real-time emotion state label is output; based on the emotion label, in combination with a game world view knowledge base composed of scene constraints, character settings and plot rules, a fuzzy logic behavior decision engine is used to dynamically generate a non-player character behavior adjustment parameter set; finally, the AINPC is driven to real-time adjust its confrontation strength, interaction frequency, guidance mode and behavior style, so that emotion-driven personalized and adaptive interaction is realized.

The application discloses a method for reducing the false alarm rate of biological induction perimeter alarm, and particularly relates to the field of infrared induction technology; background temperature data of a monitoring area is acquired, a background thermal field benchmark model is constructed, and a background benchmark temperature value is calculated; current background temperature is acquired according to a preset time interval, and a dynamic background temperature difference drift rate is calculated; heat source change information is extracted from a thermal imaging image sequence, the variance of the heat source change in a time window is calculated, and the stability of the static heat source signal is obtained; the above two features are normalized, and a feature parameter set is constructed; the set is input into a fuzzy logic reasoning model, a PIR sensor signal credibility parameter is output according to a preset fuzzy rule; according to the credibility result, an alarm judgment path is dynamically selected, intelligent fusion and judgment of the PIR signal and the video recognition result are realized; the method can effectively identify the PIR false triggering risk in a complex thermal environment, reduce the false alarm rate, and improve the reliability and adaptability of the perimeter security system.

This invention relates to the field of landslide dammed lake emergency response technology, and discloses a method and application for identifying landslide dammed lake-prone areas by integrating fuzzy frequency ratio and random forest methods. The method includes: dividing the target area into evaluation units; constructing an evaluation index system for landslide dammed lake-prone areas of the target area based on a random forest model; calculating the landslide sensitivity weights of the evaluation indicators based on the fuzzy logic frequency ratio analysis method; calculating the river blockage probability weights of the evaluation indicators based on a judgment matrix; fusing the landslide sensitivity weights and river blockage probability weights of the evaluation indicators to determine the combined weights of the evaluation indicators; and extracting landslide dammed lake-prone area information based on weighted superposition analysis. This invention employs a fuzzy logic-based frequency ratio analysis method, analytic hierarchy process (AHP), and weighted superposition analysis method to obtain indicator weights, fully integrating the statistical correlation between evaluation indicators, expert experience, and decision-makers' personal preferences, thereby improving the accuracy of the landslide dammed lake-prone area evaluation model.

The present application relates to the field of physical therapy equipment, especially to a kind of spinal step quantitative traction physiotherapy control system based on closed-loop force feedback, the system includes: data acquisition module, real-time traction force value, electromyographic signal data, displacement rate of change between traction bed movable bed plate and traction bed fixed bed body are collected;Traction bed drive module, according to the traction force control instruction output by self-learning fuzzy logic controller, execute traction action to traction bed body;Self-learning fuzzy logic controller, according to real-time traction force value, electromyographic signal data, displacement rate of change and the pain level signal feedback by the object to be traction, using step traction strategy, generates traction force control instruction, traction force control instruction is set to use the fuzzy parameter adjustment model pre-constructed based on neural network and fuzzy inference, dynamically generated by updating the shape parameter of membership function of first fuzzy logic rule.The present application effectively improves the self-adaptability and accuracy of traction force regulation.

The application discloses a kind of underwater man-machine shared teleoperation control methods based on bilateral trust mechanism.Method includes: master end force feedback device teleoperation control underwater mechanical arm moves, so that underwater mechanical arm end underwater hand-eye camera identifies underwater target, first, the master end evaluation model of master end force feedback device is established to obtain master end trust factor;Establish the visual evaluation model of underwater hand-eye camera, and finally obtain visual trust factor by density-based visual measurement outlier rejection method;Master end trust factor, visual trust factor and each control instruction are collectively processed by man-machine shared teleoperation method based on fuzzy logic, and the desired control instruction of underwater mechanical arm is obtained to carry out man-machine shared teleoperation control.The application can effectively eliminate the outlier of visual identification, ensure the reliability of visual servo system, introduce visual auxiliary system, can further reduce the burden of operator, improve the task success rate and operation efficiency of teleoperation system.

This invention discloses a large-signal modeling method for a photovoltaic-storage-charging energy routing system based on T-S fuzzy logic. The method includes: considering the coupling relationships between various units in the photovoltaic-storage-charging energy routing system, establishing a large-signal nonlinear state-space model of the system; identifying nonlinear terms in the large-signal nonlinear state-space model; performing fuzzy linearization on the nonlinear terms to obtain the linearized result; and integrating the linearized result to construct a T-S fuzzy model of the photovoltaic-storage-charging energy routing system. This invention can establish an accurate dynamic model covering the entire operating range of the system, providing an effective model foundation for large-signal stability analysis, robust controller design, and operational optimization of the system.

Methods and systems include converting an event log for an application into event log process information. The source code for the application is converted into source code process information. Relations between the event log process information, the source code process information, and an initial process model that is implemented by the application are identified using fuzzy logic on a set of events. A unified process model is generated using the relations. A problem in execution of the application is resolved based on the unified process model.

This invention discloses a smart parcel locker location selection method based on IF-WSP. It utilizes interval fuzzy numbers to establish a fuzzy evaluation matrix, defines random floating parameters, and uses these parameters to weight and integrate expert opinions. An improved chaotic mapping function based on nonlinear perturbation is employed. The method calculates cumulative and summative location selection schemes based on evaluation standard weights, generates a chaotic threshold using the nonlinear perturbation chaotic mapping function, and uses this threshold to perform multiple synthesis of the cumulative and summative location selection schemes. The average location score is then used for defuzzification to determine the final location result. Compared with existing technologies, this invention uses fuzzy logic to transform expert opinions into mathematical expressions and combines them with weighted aggregation and multiplication, fully considering the satisfaction of different customers. This results in parcel locker locations that are closer to customer needs, improving the performance of the parcel lockers and demonstrating significant social value and market application prospects.

This invention belongs to the field of safety monitoring technology for electrochemical energy storage systems, and discloses a hierarchical diagnostic method for abnormal degradation of energy storage batteries that combines data and mechanisms. The specific steps are as follows: Step 1: Multi-dimensional dynamic feature extraction. Data preprocessing, voltage range feature identification based on a sliding window, multi-dimensional dynamic feature extraction, and feature optimization and filtering are performed on the original operating data of the energy storage battery. Through standardized formulas for voltage, capacity, and temperature, and related formulas for feature calculation, the optimal feature set characterizing the battery's aging state is obtained. This invention, based on a holographic dynamic characterization method that combines sliding window voltage range feature identification with incremental capacity-internal resistance multi-source fusion, overcomes the limitations of single parameters and achieves multi-dimensional and accurate characterization of the battery's aging state. Simultaneously, a hybrid reasoning framework combining a lightweight physical neural network and temporal fuzzy logic is constructed, ensuring interpretability while quantifying uncertainty.

This invention discloses a RL control method for amphibious vehicle drive mode switching based on fuzzy logic and MFAC, comprising: 1. real-time acquisition of multi-sensor information; 2. inputting sensor information into a decision-maker to obtain the vehicle's membership degree to each mode; 3. constraining and correcting the membership degree; 4. calculating the mode switching coefficient using the constrained and corrected membership degree; 5. adaptively optimizing the vehicle's operating conditions using model-free adaptive control and reinforcement learning, designing a sliding mode control law coupled with the mode switching coefficient, and obtaining the optimal control sequence for the land drive and water propulsion systems over a future period; 6. applying the control quantity to the actuator drive module to achieve a smooth transition of the drive mode. This invention can achieve smooth, intelligent, adaptive, and dynamically stable switching of amphibious vehicle drive modes, and can improve the stability and energy utilization efficiency of the switching, thereby solving the stability and smoothness problems in the amphibious vehicle drive mode switching process.

The present application relates to the technical field of push rod control, and specifically provides a medical mobile bed silent push rod auxiliary device control method and system, the method comprising: obtaining a rotation speed synchronization error, constructing an adaptive fuzzy PID controller based on the rotation speed synchronization error; obtaining a rotation speed prediction error, and constructing a prediction compensator for compensating for the acceleration mutation in advance based on the rotation speed prediction error; obtaining a real-time control signal according to the adaptive fuzzy PID controller and the prediction compensator, and controlling two groups of silent push rod motors based on the real-time control signal. The present application constructs an adaptive fuzzy PID controller and a prediction compensator, uses the prediction compensator to process the acceleration change in the start-stop phase in advance, combines fuzzy logic to deal with nonlinear load, can eliminate the response difference (such as start-stop time difference, speed fluctuation) in the operation of the double silent push rod motors of the medical displacement bed, and avoid the shaking problem caused thereby.