135 results about "Fuzzy inference system" patented technology

The invention provides a transformer life prediction method and system based on machine learning and the Internet of Things, and relates to the technical field of transformer life prediction, and the method comprises the steps: collecting and preprocessing the multi-modal operation data of a transformer, constructing a multi-modal data fusion model based on a graph neural network, and carrying out the prediction of the life of the transformer; and mapping the dimensionality-reduced feature data into a graph structure, inputting transformer multi-modal operation data collected in real time into an optimized fuzzy inference system to obtain a health state evaluation result and a final residual life prediction result of the transformer, and obtaining a prediction result of the residual life of the transformer on the basis of transformer maintenance suggestions and historical maintenance records. Constructing a transformer digital twinborn model for maintenance strategy optimization, inputting the health state evaluation result, the residual life prediction result and the transformer maintenance suggestion into the transformer digital twinborn model, performing fault simulation and predictive maintenance analysis, and based on the fault simulation and predictive maintenance analysis result, obtaining a maintenance strategy of the transformer. And generating an intelligent maintenance scheme in combination with the expert knowledge base.

The invention discloses an energy-saving intelligent street lamp automatic emergency response system and a control method thereof, relates to the technical field of industrial Internet of Things control, and solves the problems that an existing intelligent street lamp system is poor in dynamic scene adaptability, single in emergency response strategy and insufficient in communication stability. According to the method, a dynamic priority scheduling matrix is generated through multi-source data fusion and an adaptive weighted decision tree, and an intelligent dimming strategy is trained in combination with improved fuzzy reinforcement learning; a multi-level fuzzy control and fuzzy reasoning system is used for generating emergency parameters driven by accident levels; dynamically selecting an optimal communication link transmission instruction based on a multiple access protocol and multi-scale channel sensing; an IEEE 1588PTP protocol and Bayesian clock drift correction are adopted to guarantee time sequence consistency, and energy consumption and safety balance are optimized through multi-target reinforcement learning; the dynamic adaptive capacity, the emergency response accuracy and the communication reliability of a complex scene are remarkably improved, and collaborative optimization of energy-saving efficiency and road safety is realized.

The invention discloses an unmanned ship multi-mode perception and decision support method and system based on fuzzy logic and a rule engine, and the method comprises the steps: obtaining multi-source sensor data, carrying out the standardization processing, calculating a dynamic weight coefficient based on information entropy, and carrying out the data fusion; mapping the fusion data to a feature space, analyzing feature correlation, and evaluating an environmental threat degree; performing fuzzy partitioning on the feature space and generating an initial rule, and optimizing a rule set through information gain; a fuzzy inference system is constructed and cooperatively operated with a rule engine, and collaborative decision parameters are optimized; and performing multi-level risk assessment and generating a level decision, and optimizing a decision instruction through reinforcement learning. According to the method, the sensing accuracy and decision reliability of the unmanned ship in a complex sea condition environment are improved through multi-modal data adaptive fusion and collaborative decision of the fuzzy logic and the rule engine, and the method has good engineering application value.

The invention provides an electric power spot market price prediction method and system based on artificial intelligence, and relates to the technical field of electric power price prediction, and the method comprises the steps: calculating an impact factor weight through employing a deep reinforcement learning optimization fuzzy reasoning system based on historical transaction data and external association data; performing wavelet transform decomposition on the data to obtain a feature fluctuation sequence, and performing weighted fusion to generate a market feature fusion sequence; the method comprises the following steps: constructing a bidirectional feedback neural network by using a graph attention network to extract market transaction features and generate feature correction parameters, generating layered early warning signals in combination with a cascade forest algorithm to construct a dynamic market behavior feature map, and inputting an optimized feature map into a prediction model group comprising a plurality of prediction sub-models, and realizing information sharing and complementary learning through a cross-modal feature interaction module, generating an initial prediction result, performing constraint correction according to an intelligent clearing rule, correcting a correction prediction result, and finally outputting a predicted price of the electric power spot market.

The invention relates to the technical field of data processing, and discloses an intelligent risk early warning method, device and equipment for a power distribution network, and a medium. Historical risk monitoring data of the power distribution network under multiple dimensions are fused through a graph convolutional network and a variational auto-encoder; a variational recurrent neural network and a long-short term memory network are trained in combination with historical risk fault data of the power distribution network, and an attention mechanism is introduced in the training process to generate a risk assessment model; acquiring real-time risk monitoring data of the power distribution network under multiple dimensions to extract multi-dimensional real-time fusion features and inputting the multi-dimensional real-time fusion features into the risk assessment model for processing to obtain a real-time risk assessment level so as to further process the multi-dimensional real-time fusion features through a multi-criterion decision analysis method and a fuzzy inference system; the target risk assessment level is obtained, the level is compared with the risk early warning threshold value, if the level exceeds the threshold value, early warning is triggered, and the accuracy of power distribution network risk assessment is effectively improved.

The invention discloses a driving behavior risk assessment method and system, and relates to the technical field of intelligent traffic and driving safety, and the method comprises the steps: collecting the operation parameter data of a vehicle, carrying out the feature extraction of the operation parameter data, extracting feature parameters to form a feature vector, and calculating the Mahalanobis distance score of the feature vector; according to the Mahalanobis distance score of the feature vector, carrying out risk grade division by adopting an improved hierarchical fuzzy inference system to obtain a risk grade score; and establishing a driver portrait model according to the risk level score, and when the risk level score exceeds a personalized risk threshold, outputting a driving risk behavior improvement suggestion to complete driving behavior risk assessment. According to the invention, a refined grading basis is provided for individual risk monitoring of the driver, dynamic monitoring and real-time intervention of driver behaviors are realized, traffic accidents can be reduced, and driving safety is improved.

The invention discloses an e-commerce live broadcast real-time interaction quality evaluation system based on edge calculation, and relates to the technical field of e-commerce live broadcast, and the system comprises a multi-modal interaction data collection module which collects multi-modal interaction data of a live broadcast stream in real time through a distributed edge calculation node cluster, and constructs a multi-dimensional quality feature vector, the multi-modal interaction data comprises a video coding parameter, an audio quality index, user interaction behavior data and network transmission state data; according to the invention, the distributed edge computing node cluster collects the multi-modal interaction data of the live stream in real time, the lightweight space-time attention neural network model carries out data fusion processing, the deep reinforcement learning network generates a quality optimization scheme, and the adaptive fuzzy inference system corrects the optimization scheme in real time. And dynamic parameter adjustment is carried out in combination with network bandwidth fluctuation and a terminal device resource state, so that the effect of accurately and comprehensively evaluating the e-commerce live broadcast interaction quality in real time is achieved.

The invention discloses an aircraft cabin environment personalized adjustment method based on sentiment analysis, and the method comprises the steps: collecting the facial expression, voice waveform and environment parameters of a passenger in real time through a cabin multi-source sensor, and generating a standardized physiological signal matrix and anonymized voice features through noise reduction and feature extraction; inputting the physiological signal matrix and the voice features into a pre-trained deep learning model, outputting an emotion index and a classification label, and updating model parameters through a federal learning framework; dynamically generating temperature, humidity and oxygen concentration adjusting instructions and dynamic weights by adopting a fuzzy reasoning system in combination with the emotion indexes and passenger preset preferences; environment adjustment is executed through a closed-loop control system, and environment parameter errors are fed back; synchronously updating a fuzzy inference system rule base and deep learning model parameters by utilizing reinforcement learning in combination with environmental parameter errors and emotion index changes, and completing optimization of a closed loop; according to the invention, real-time dynamic regulation and control and continuous adaptation optimization of the personalized cabin environment can be realized.

The invention discloses an underground water pollution path micro-analysis method based on a graph neural network. The method comprises the following steps: S1, acquiring a historical data set and a real-time monitoring data set of underground water pollution; s2, using a fuzzy membership function to represent the language level of the fuzzy variable; s3, constructing a fuzzy logic rule base based on the historical data set; s4, using a fuzzy inference system to process fuzzy variables in the historical data set; s5, constructing an underground water pollution path diagram structure model based on the historical data set and the real-time monitoring data set; s6, constructing an incidence matrix between the nodes; s7, based on the trained graph neural network model, inputting water flow velocity, pollutant concentration and geologic structure parameters in the real-time monitoring data set; s8, generating real-time prediction of a pollution diffusion path; and S9, outputting an analysis result of the groundwater pollution path. The problems that in the prior art, a pollution diffusion path is difficult to accurately predict under a complex geological structure, and real-time dynamic response is insufficient are solved.

The invention discloses an intelligent electric energy meter operation state risk assessment method based on strong association rule identification and multi-model integration. The method comprises the following steps that historical operation data of an intelligent electric energy meter is collected in a multi-source mode, data preprocessing is carried out, and then a comprehensive feature database is constructed; using a strong association rule identification model to mine characteristic factors influencing the intelligent electric energy meter from the comprehensive characteristic database; performing fuzzy reasoning on continuous features in the feature factors by using a fuzzy reasoning system, and performing adaptive optimization in a fuzzy reasoning link to obtain a risk assessment value of the continuous features; calculating local saliency of discrete features in the feature factors in real time in a rolling time window, and performing weighted fusion on the local saliency and the reference saliency through an attenuation factor to obtain a risk assessment value of the discrete features; and carrying out weighted summation on the risk assessment values of the continuous features and the risk assessment values of the discrete features to obtain a final state risk assessment result. According to the invention, the state risk assessment accuracy is improved.

The invention discloses a cross-working-condition bearing fault diagnosis method and device based on a multi-scale convolutional fuzzy neural network migration model, and the method comprises the steps: inputting a vibration signal of a to-be-diagnosed bearing into a trained cross-working-condition bearing fault diagnosis model, and outputting a diagnosis result; wherein the trained cross-working-condition bearing fault diagnosis model is obtained by training a multi-scale convolutional fuzzy neural network migration model by adopting a training data set, and a total loss function in the training process comprises classification loss guided by error samples, depth domain adaptive loss and confusion loss of an adversarial network domain; the training data set comprises a vibration signal from the bearing under the source domain working condition and a corresponding fault label, and a vibration signal from the bearing under the target domain working condition; the migration model based on the multi-scale convolutional fuzzy neural network comprises a multi-scale convolutional neural network module, a gated recursive convolutional attention module, an improved adaptive fuzzy inference system and an adversarial network module which are connected in sequence. The objective of the invention is to solve the problem of poor bearing fault diagnosis accuracy under variable working condition migration conditions.

The present invention relates to the field of information analysis, and discloses a multi-spectral Chinese medicine tongue image information analysis device and method, which improves the accuracy of tongue image feature extraction and classification decision-making through methods such as multimodal fusion and deep learning. The multi-spectral Chinese medicine tongue image information analysis method comprises: obtaining the reaction of the tongue surface in different spectral bands through dynamic spectral acquisition technology, obtaining multi-spectral tongue image processing data through preprocessing, extracting features from the processed data using a multi-parameter adaptive neural fuzzy inference system model, obtaining recognition results of features such as tongue color, tongue shape, and tongue coating, converting the multi-spectral tongue image processing data into a graph structure, constructing a graph neural network model to capture the relationship between pixel points, and extracting relationship features. Through multimodal fusion technology, the feature vectors of different modes are fused into a comprehensive tongue image feature vector, and the fused feature vector is classified and decided using a preset classifier to obtain the Chinese medicine tongue image information analysis result.

The invention discloses a grinding wheel wear degree identification method and device based on adaptive neural fuzzy reasoning. The method comprises the steps that a grinding temperature signal, grinding force and the surface quality of a workpiece are obtained; determining the abrasion stage of the grinding wheel according to the surface quality, the grinding force and the grinding temperature; extracting the total number of thermal pulses in the grinding temperature signal as the number of dynamic effective abrasive particles; a grinding wheel surface topography graph is obtained, abrasive dust and abrasive particle abrasion in the grinding wheel surface topography graph are recognized and distinguished through an HSV segmentation method, the area rate is calculated, and the abrasive dust proportion and the abrasive particle abrasion proportion are obtained; and the abrasive dust proportion, the abrasive particle abrasion proportion and the dynamic effective abrasive particle number serve as input, the abrasion stage serves as output, the self-adaptive neural fuzzy inference system is trained, and the trained self-adaptive neural fuzzy inference system is obtained for abrasion degree recognition. According to the method, the abrasion condition of the grinding wheel can be accurately and conveniently judged by utilizing the self-adaptive neural fuzzy reasoning technology and synthesizing the multi-dimensional characteristic signals in the grinding process.

An Adaptive Neuro-Fuzzy Inference System (ANFIS) for total intravenous anesthesia management is disclosed, enabling control over administration of anesthetic agents and dynamic adjustment according to patient physiological feedback. The system processes patient data, including processed EEG signals, hemodynamic information, capnography, and pulse oximetry, to facilitate real-time anesthetic dosage adjustments.

The invention relates to the technical field of unmanned road rollers, in particular to an unmanned road roller cluster minor radius curve path planning method based on an artificial potential field technology, which comprises the following steps: S1, collecting road environment information, and establishing an environment model; s2, setting an artificial potential field function in the environment model, and defining potential fields of an obstacle and a target point; s3, calculating control parameters of path planning according to the kinetic model of the unmanned road roller; s4, generating an optimal path by adopting a model predictive control algorithm in combination with an artificial potential field function; s5, adjusting the parameters of the artificial potential field function in real time according to the change of the surrounding environment of the road roller by using a fuzzy inference system; in the invention, aiming at the defects of the traditional path planning method, the adaptability and the practicability of the algorithm are improved through multiple technical improvements.

The invention relates to a kitchen waste sewage accurate aeration control method, system and device based on fuzzy PID and deep learning and a medium, and the method comprises the steps: collecting parameters, and processing the parameters to obtain preprocessed data; predicting the oxygen demand through a deep learning time sequence prediction model, and generating a dissolved oxygen set value interval in combination with multi-target reinforcement learning; based on the deviation and the deviation change rate of a dissolved oxygen set value interval and a real-time measured value, an evolutionary fuzzy reasoning system is used for adjusting gain parameters of an aeration controller, PID control quantity is output and converted into an aeration equipment driving signal, and multi-area aeration intensity dynamic distribution is implemented in combination with a hydrodynamic mapping relation and preprocessed data. Through the synergistic effect of the technologies, the core defects of single sensing dimension, prediction lag, control target conflict, extensive execution and the like of a traditional method are systematically solved, and collaborative optimization of degradation efficiency, energy consumption economy and equipment operation life in the kitchen waste sewage treatment process is realized.

The invention discloses an unmanned vehicle trajectory tracking control method based on adaptive time domain model predictive control, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining a system predictive equation, converting a target function into a standard quadratic programming form function, and obtaining a system predictive equation based on a constraint condition and the standard quadratic programming form function; calculating to obtain a control increment sequence in a control time domain; constructing a model prediction trajectory tracking controller; a model prediction trajectory tracking controller is used for tracking the trajectories of the tested vehicle under different working conditions, and an optimal time domain data set is determined; training the optimal time domain data set by using an adaptive neural fuzzy inference system to obtain a time domain optimization module; and based on the time domain optimization module and the model prediction trajectory tracking controller, generating a self-adaptive time domain model prediction controller which is used for adjusting the prediction time domain and the control time domain of the tested vehicle under different working conditions in real time. The adaptability and trajectory tracking effect of the system are effectively improved, and the vehicle out-of-control risk is reduced.

The invention discloses a channel safety monitoring and intelligent dredging method and system based on a generative adversarial network. The method comprises the following steps: S1, collecting real-time dynamic data of a ship and carrying out standardization processing; s2, generating potential collision scene data according to the historical channel data; s3, comparing the generated potential collision scene data with actual navigation channel data, and evaluating the authenticity of the generated data; s4, evaluating a collision risk value according to the generated potential collision scene data and the real-time dynamic data of the ship; s5, generating a channel dredging strategy of the ship by using an adaptive fuzzy inference system; s6, adjusting the sailing path of the ship according to the channel dredging strategy, and optimizing the ship sailing sequence; and S7, adjusting the position, the speed and the course of the ship in the channel management system in real time. According to the method, an efficient and scientific optimization scheme can be provided in channel safety monitoring and intelligent dredging, and remarkable technical values and economic benefits are brought to practical application.

The invention belongs to the field of intelligent construction, and particularly discloses a shield axis deviation prediction model, a construction method thereof, a prediction method and a system, and the method comprises the steps: training a mixed model through a training set, and enabling the trained mixed model to be the shield axis deviation prediction model; the training set comprises splicing parameters and corresponding shield axis deviation data, segmenting a shield parameter time sequence signal, and processing each obtained signal segment to obtain the splicing parameters; the hybrid model comprises an adaptive neural fuzzy inference system ANFIS and a full-connection space-time diagram neural network model FC-STGNN; the ANFIS is used for quantizing the splicing parameters, and a parameter # imgabs0 # is output; performing graph construction and graph convolution by the FC-STGNN based on the splicing parameters, and outputting a parameter # imgabs1 #; and carrying out weighted fusion on the parameters # imgabs2 # and # imgabs3 #, and outputting a shield axis deviation result. According to the invention, the precision and adaptability of shield axis deviation prediction can be improved.

The invention relates to a V2V (Vehicle to Vehicle) information routing transmission method and system between urban roads facing the Internet of Vehicles, and the method comprises the steps: calculating a packet routing global path through employing a Dijkstra algorithm according to the real-time traffic flow density information of the roads; when a data packet is transmitted in a road section, comprehensively evaluating the quality scores of neighbor vehicles by adopting a fuzzy inference system, and selecting the vehicle with the highest score as a next hop node; and when the data packet needs to be transmitted across the road, a crossroad dynamic group forwarding strategy is adopted, vehicles in the crossroad range are fully utilized as cross-road transmission relay nodes, and V2V information routing transmission is realized. The invention aims to construct an optimal path for information transmission in a complex vehicle environment so as to reduce the average end-to-end time delay and packet loss rate of V2V communication.

The invention provides an intelligent evaluation and optimization regulation and control method for peak regulation and frequency modulation of a nuclear power unit, and relates to the technical field of evaluation and optimization of a power generation system. Defining a fuzzy set for the operation data of the nuclear power unit, determining a vibration amplitude level, formulating a fuzzy rule, and obtaining a fuzzy evaluation result of the nuclear power unit in the aspects of stability and reliability by using a fuzzy reasoning system according to input real-time data and the fuzzy rule; constructing nodes of the Bayesian network, and determining a probability relationship between the nodes according to historical data and expert knowledge; setting an optimization variable of a simulated annealing algorithm and defining a target function; setting particle swarm particles, and combining a particle swarm optimization algorithm with a simulated annealing algorithm to prevent the particles from falling into a local optimal solution; the nuclear power unit and the intelligent power grid establish real-time communication connection and share operation state information of the two parties. The evaluation accuracy is improved, and the peak regulation and frequency modulation strategy is optimized.

The invention relates to the technical field of heavy-load robot control, in particular to a self-adaptive robust control method of a heavy-load robot fusing differential homeomorphic mapping. According to the method, position information of each joint is collected in real time and accurately compared with an expected trajectory, a ternary input vector is output, differential homeomorphic mapping parameters are updated in real time through a T-S fuzzy inference system, and a joint space state is mapped to a new coordinate space based on the optimized parameters, so that the position information of each joint is accurately compared with the expected trajectory. Neighbor joint information is obtained through distributed communication to generate a cooperative control item, a sliding mode surface is constructed in combination with a local tracking error to generate a robust control item, a compensation item is generated based on adaptive law estimation parameter uncertainty, three-item parallel control output is formed, dynamic weight distribution and fusion are performed on the three control items, a basic control moment is obtained, and the control precision is improved. And then a feed-forward compensation item based on a nominal dynamical model is superposed to generate a final comprehensive control torque, and the comprehensive control torque optimizes energy distribution while ensuring the performance.

The invention relates to an unmanned aerial vehicle path planning method based on a fuzzy adaptive parameter RRT algorithm, and belongs to the technical field of path planning. Comprising the following steps: S1, system initialization and environment perception: defining a starting point and a target point of an unmanned aerial vehicle in a three-dimensional task space, loading information of a static obstacle and a dynamic obstacle, and configuring a sensor to obtain environment parameters; s2, designing a discrete fuzzy inference system: defining input variables as obstacle density RN and total node number TN, and defining output variables as step length SS and target deviation probability RP; constructing a fuzzy rule base for dynamically reasoning an output variable according to the input variable; s3, executing a fuzzy adaptive parameter RRT algorithm, optimizing and improving the discrete fuzzy inference system, and adjusting adaptive parameters; and S4, path optimization: optimizing the initial path by adopting a cost function rewiring method, and reducing the length of the path. In the dynamic process of path planning, according to the number of random nodes and the total number of nodes of the current random tree, a fuzzy inference system is designed to adaptively adjust the selection probability and the step length of an algorithm, and the path planning efficiency and the success rate are effectively improved. The problems in the prior art are solved.

The invention discloses a cold chain hybrid power vehicle energy consumption optimization control method based on personalized temperature control, and belongs to the field of cold chain transport vehicle energy consumption optimization, and the method comprises the steps: obtaining the travel information of a cold chain hybrid power vehicle, and initializing the key parameters of the vehicle; calculating a cabin temperature difference and a cargo compartment temperature difference by using travel information and vehicle key parameters, and establishing a personalized temperature control energy consumption prediction model to carry out personalized temperature control; designing a reference SOC prediction model based on an adaptive neural network fuzzy inference system to predict the reference SOC of the battery; defining a multi-objective optimization problem considering fuel oil, electric energy and temperature control energy consumption in combination with the personalized temperature control energy consumption prediction model and the reference SOC prediction model, and constructing an energy management model based on adaptive dynamic programming; an energy management model is applied, power distribution among power sources is adjusted in real time, and the overall energy consumption of the vehicle is optimally controlled. According to the method, the problems of personalized temperature control and real-time energy consumption optimization of multi-dimensional information fusion in a complex and strong time-varying environment of the cold chain hybrid vehicle are solved.

A power transformation equipment oil temperature prediction and abnormity early warning method based on a fuzzy algorithm comprises the following steps that real-time operation parameters and oil temperature historical data of power transformation equipment are collected, the data are preprocessed, and a high-quality input data set is constructed; constructing a fuzzy inference system considering multiple input factors, and setting a fuzzy membership function and an inference rule base; according to the fuzzy prediction result and the real-time oil temperature change, a self-adaptive dynamic safety threshold model is compared, a potential abnormal trend is identified, and automatic grading early warning is carried out according to the overtemperature grade; and corresponding exception type identifiers and disposal suggestions are generated and are pushed to the operation and maintenance platform through the communication module, so that remote operation and maintenance scheduling and response control are supported, and intelligent cooperative processing is realized. According to the invention, intelligent prediction of the oil temperature change trend under the influence of multiple factors is realized, the capability of sensing temperature rise abnormity in advance is improved, the accuracy of sensing the operation state of the equipment and the timeliness of early warning response are also remarkably improved, and the risk of equipment failure caused by the abnormal oil temperature is reduced.

The invention discloses a farmland irrigation method of a multilayer sensor model based on a WOA-LSO algorithm, and relates to the technical field of agricultural irrigation. The method comprises the following steps: acquiring meteorological data factors of a plurality of samples, and determining a plurality of factor combinations; a factor combination most related to the soil transpiration amount is screened out through a correlation test, and then the number of nodes of an input layer, an output layer and a hidden layer of a multi-layer sensor (MLP) model is determined; an MLP prediction model optimized based on a WOA-LSO algorithm is constructed, the optimal sequence is obtained by optimizing the weight and threshold sequence of the MLP, and the model is endowed with the optimal sequence; inputting a target meteorological data factor of a research area into the model, and predicting the soil transpiration in a future time period; inputting the soil effective water content and transpiration into a fuzzy inference system to obtain an irrigation coefficient, and calculating the irrigation amount required by the farmland according to the irrigation coefficient. According to the method, the problem of inaccurate prediction of the soil transpiration caused by scarcity and incompleteness of data in a plateau region is solved, and the prediction accuracy of the soil transpiration is remarkably improved.

The invention relates to the field of data processing, in particular to a traffic safety facility statistical table automatic generation method. Collecting structured data of traffic safety facilities, and counting Boolean missing values of all fields; constructing a field credibility scoring model, combining semantic tags, identifying confidence and missing information, and identifying low credibility fields and missing fields; inputting the data into a fuzzy inference system to generate a complementation result, and fusing the complementation result with original data; constructing a trajectory density function model based on the spatial position, and automatically grouping the road paragraphs by adopting an adaptive density clustering algorithm; generating a statistical table structure template according to the facility category and the field configuration relationship; and completing field filling, state classification and semantic annotation in combination with a grouping result, outputting a statistical table file with a field source mark, and supporting export of various formats. The method can improve the integrity, credibility and system suitability of the statistical result, and is suitable for an intelligent traffic facility management scene.

The invention discloses a brain-controlled motion intention recognition method based on global optimization ANFIS, which utilizes an ANFIS model to perform brain-controlled intention classification decision, introduces a global spatio-temporal expression module, fully considers global spatio-temporal information of multi-input EEG data, introduces a rule attention module, and allows the model to recognize the most active intention-related fuzzy rule through an attention mechanism. And important intention features are selected to carry out accurate EEG motion intention classification decision. According to the method, the interpretability characteristic of the fuzzy inference system and the learning ability of the adaptive network are comprehensively utilized, system parameters can be changed according to priori knowledge, the extracted intention characteristics are explained layer by layer, and the final decision output is closer to the real output. According to the method, the performance of a brain-controlled intention recognition model applied to the field of brain-computer interaction is effectively improved, and the method can be suitable for assisting brain-controlled motion intention classification decision making based on EEG under a lower limb rehabilitation task and has high robustness and high efficiency.

The invention discloses a fault detection method, device and equipment of a nuclear power plant control system, a medium and a product, and relates to the field of intelligent monitoring of nuclear power equipment, and the method comprises the steps: obtaining the real-time operation data of the nuclear power plant control system; according to the real-time operation data, multiple models are adopted to carry out fault classification on the nuclear power plant control system, and a fault classification result and a confidence score of each model are obtained; wherein the plurality of models are respectively a random forest model, a long short-term memory network and a fuzzy inference system; each model is obtained by training a training sample set in combination with a dynamic simulation model library in advance, and samples in the training sample set comprise real samples and supplementary samples; and determining the final fault category of the nuclear power plant control system according to the fault classification result and the confidence score of each model. The fault detection accuracy of the nuclear power plant control system is improved.

The invention provides a state-of-charge estimation method, system and equipment for a flooded lead-acid battery. The method comprises the following steps: establishing a first-order RC model of the flooded lead-acid battery; obtaining a state equation and an observation equation; according to the state equation and the observation equation, calculating the time update of the estimation state and the time update of the error covariance; calculating a Kalman gain adjustment coefficient value through a fuzzy inference system by taking a current value, a temperature value and a current change rate at any moment as input ends of the fuzzy inference system according to time updating of an estimation state and time updating of an error covariance; and according to the Kalman gain adjustment coefficient value, calculating the Kalman gain, and obtaining the measurement update of the estimation state and the measurement update of the error covariance so as to obtain the final estimation value of the real-time state of charge of the flooded lead-acid battery. By combining the first-order RC model, the Kalman filtering algorithm and the like, the technical problem that the estimation precision of the state of charge of the existing flooded lead-acid battery is limited is solved.