88 results about "Fuzzy inference system" patented technology

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 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 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 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.

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 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 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 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.

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 real-time closed-loop regulation and control method and system for the forming process of a stainless steel welded pipe. The method comprises the steps that multi-source heterogeneous data including a pipe shape image, a laser ranging sequence, temperature field distribution and roller pressure time sequence data in the forming process are synchronously collected through a distributed multi-source sensor network; performing space-time alignment and feature level fusion on the multi-source heterogeneous data, and constructing a multi-dimensional dynamic digital twinborn body in the forming process; on the basis of the multi-dimensional dynamic digital twins, an online rolling prediction model is adopted to deduce the development trend of weld forming quality and pipe diameter size deviation in real time; and according to the development trend, a cooperative regulation and control instruction set of the roller gap and the welding power is generated through a self-adaptive fuzzy inference system. By means of the embodiment of the invention, multivariable look-ahead perception and intelligent collaborative closed-loop regulation and control of technological parameters in the stainless steel welded pipe forming process can be achieved, and the stability and consistency of the forming quality are improved.

The invention relates to the field of ship engineering technology, and specifically discloses a method for measuring the spatial angle of a dredger rake pipe. The method collects motion data of a hull and a rake pipe in real time, uses median filtering to remove high-frequency noise, extracts features and constructs a comprehensive feature vector, uses a fuzzy inference system to separate the nonlinear influence of hull sway and rake pipe posture, and corrects the posture estimation result in combination with real-time error feedback. The estimation result is dynamically optimized through Bayesian estimation and particle filtering algorithms, and the system deviation is corrected, thereby improving the measurement accuracy. The system also evaluates the overall measurement accuracy in combination with a machine learning model, automatically adjusts measurement parameters and processes, and improves the system adaptability and robustness. For inaccurate measurement results, the system can analyze the causes and re-measure the spatial angle to ensure long-term stable operation.

The present invention provides a mode self-switching human-computer interaction control method, device and apparatus. The control method includes: obtaining the operation information of a human-computer interaction device, including force information and the current operation mode, wherein the force information includes non-interaction force information; constructing a force model based on the force information; compensating the force model based on the non-interaction force information to obtain interaction force information; processing the interaction force information through a fuzzy inference system to obtain an evaluation index; determining a target operation mode based on the current operation information and status information, where the status information includes at least one of the operation information and the evaluation index; and controlling the operation of the human-computer interaction device according to the target operation mode. By obtaining the operation information to construct a force model to obtain interaction force information, and making a judgment based on the evaluation index obtained from the fuzzy inference system to determine the target operation mode, the present invention achieves the purpose of automatically switching a reasonable operation mode as needed according to the actual situation of the user to improve the interaction effect.

The invention discloses a fuzzy logic-based rolling optimization integrated energy system energy scheduling method, which comprises the following steps of: improving an evolutionary fuzzy reasoning system, and solving a plurality of fuzzy decision parameter sets by a plurality of possible scenes generated in a day-ahead stage; selecting the fuzzy decision parameter set obtained in the day-ahead stage by using a model predictive control framework in the intra-day stage, and making a decision on the current time period through a fuzzy reasoning system corresponding to the selected fuzzy decision parameter set, thereby realizing economic scheduling of the integrated energy system; a rolling optimization energy scheduling method based on fuzzy logic is applied to economic scheduling of the integrated energy system, and the loss of an energy storage unit and other energy costs are fully considered. According to the method, the problem of negative influence caused by prediction error accumulation in the real-time prediction process is effectively solved through fuzzy logic, rolling prediction is performed by using the model prediction framework, the overall operation cost of the integrated energy system is reduced, and economical scheduling of the integrated energy system is realized.

An active vibration damping control method based on an adaptive neuro-fuzzy inference system, belonging to the technical field of vibration isolation control. The method includes: constructing a system model and collecting data; designing an ANFIS model based on the state variables of the platform; the ANFIS model automatically adjusts the parameters of the fuzzy membership function through training with experimental data; training the ANFIS model based on a hybrid learning algorithm; using sensors to obtain the displacement and velocity of the platform in real time and inputting them into the ANFIS model to achieve real-time active vibration damping control. By collecting the displacement and velocity data of the platform in real time and inputting them into the ANFIS model, the system can dynamically calculate the control signal, quickly respond to vibration changes, effectively suppress vibration and reduce the vibration amplitude of the system, ensuring the stability of the platform. Through the feedback loop of the sensor and the control system, it is ensured that the system can automatically adjust the control signal according to the actual vibration state of the platform at any time.

This invention discloses an adaptive neurofuzzy system for diabetes analysis and integration, comprising the following steps: S1: inputting the dataset into a CIR-ANFIS model, where the model first standardizes the dataset; S2: calculating the causal coefficient corresponding to the prediction result for each feature using causal inference, and using this coefficient as the feature weight; S3: randomly selecting multiple different subsets, constructing an adaptive fuzzy inference system for each subset, and obtaining the prediction result of each inference system; S4: obtaining the prediction result of the entire model through ensemble learning. This invention relates to the field of computer algorithm technology. The beneficial effect of this invention is that by introducing causal inference into feature selection and using causal coefficients as weights to randomly generate data subsets for training, the model training cost is reduced, and better model performance is achieved.

The present application relates to the technical field of three-dimensional point cloud data processing, in particular to a point cloud hole repairing method and system based on adaptive neighborhood and genetic fuzzy. For unorganized point cloud data, first, the curvature, normalized curvature and local density coefficient of each point are calculated through principal component analysis, and adaptive neighborhood with adaptive local geometric features is dynamically generated in combination with the reference neighborhood; then, based on the adaptive neighborhood, boundary features are extracted and hole boundary points are screened, and single-hole boundary sets are obtained through connected component division; subsequently, a single-hole local projection plane is constructed to generate two-dimensional repair points, after global overlap and deduplication, a fuzzy reasoning system optimized by a genetic algorithm is used to estimate the height coordinates of the repair points, three-dimensional repair points are generated to complete hole filling. The present application effectively improves the boundary recognition accuracy in high-curvature areas with uneven density, while taking into account the edge continuity and surface smoothness, and is suitable for complex topography unorganized point cloud hole repair scenarios.

The application discloses a kind of based on adaptive neural fuzzy inference fuel cell ship energy management strategy method, comprising the following steps: obtaining the topological structure of fuel cell ship hybrid power system, and its operating typical working condition information, the steps of obtaining the globally optimal hybrid power system load distribution by offline algorithm;By discrete solving working condition every moment optimal equivalent factor, the step of taking working condition calculation result as training sample;Build and train adaptive neural fuzzy inference system (ANFIS), calculate to obtain optimal equivalent factor, the step of bringing result into ECMS algorithm, on-line calculation ship sailing working condition obtains optimal distribution result, the improvement of the present application is realized on-line ideal power distribution by ANFIS calculation ECMS equivalent factor, combined with the calculation characteristics of three strategies, can on-line calculation obtain the optimal solution of ship real-time power distribution, with good global optimization ability.

The invention discloses an intelligent storage and accurate scheduling method for a multi-energy complementary power system. The method comprises the following steps: collecting and preprocessing wind and light output, energy storage state, load and meteorological data in real time; predicting a load demand based on a hybrid neural network of LSTM and an attention mechanism; the weight coefficients of the cost and the energy abandoning rate are dynamically adjusted through a fuzzy inference system; constructing a power generation, energy storage and power grid intelligent agent collaborative decision by adopting an improved depth deterministic strategy gradient algorithm, and generating a scheduling instruction in combination with a Pareto frontier storage pool and an attention mechanism; controlling the energy storage system to charge and discharge according to the instruction; and online updating of model parameters and dynamic correction of a scheduling strategy are realized through closed-loop feedback and cross-layer optimization. According to the method, the dynamic balance problem in multi-objective optimization is effectively solved, the energy utilization efficiency is improved, the operation cost and the energy abandoning rate are reduced, the system stability is enhanced, and the energy storage life is prolonged.

The present application belongs to the technical field of ship working condition prediction, and discloses a rotating speed prediction method for a ship gas-electric hybrid power system. A ship gas-electric hybrid power propeller rotating speed prediction model is constructed based on an adaptive neuro-fuzzy inference system (ANFIS); a difference between the obtained predicted rotating speed and the actual rotating speed is obtained; an improved rotating speed prediction model is constructed using the obtained difference and the used rotating speed information; and future rotating speed prediction is performed through the constructed improved rotating speed prediction model. The present application uses the initially constructed rotating speed prediction model to obtain the first-step predicted rotating speed, and provides the difference between the predicted rotating speed and the actual rotating speed for the improved prediction model; the improved rotating speed prediction model is constructed according to the obtained difference and the initial rotating speed information, so as to improve the rotating speed prediction accuracy and achieve the real-time prediction effect within a given time step.

The application discloses a self-diagnosis modular lightning arrester state monitoring system, which comprises a sensing module, an extension module, a power supply module, a communication module and a processing module; the sensing module is used for collecting full current signals, surface temperature signals and partial discharge signals of the lightning arrester; the extension module is used for accessing environmental humidity signals and internal pressure signals of the lightning arrester through a controller area network bus interface; the power supply module is used for extracting energy from induced current of a grounding down lead of the lightning arrester to provide electric energy required by system operation; the communication module is used for sending monitoring data to a remote operation and maintenance server; and the processing module is used for receiving various sensor signals, extracting a resistive current component by using a fast Fourier transform algorithm, constructing a three-dimensional feature vector containing a resistive current fundamental component, a partial discharge pulse number and a temperature change rate, and outputting a fault type recognition result through a honeybee algorithm optimization self-adaptive network fuzzy reasoning system classifier.

An apparatus for model selection between a first model and a second model using projector inferencing is provided. The apparatus includes a processor and a memory connected to the processor. The memory contains instructions configuring the processor to receive an entity datum from an entity device and a second datum from a client device connected to the processor. The second datum describes matching the entity datum based on a preferred allocation with target values using the models. The processor may run two projectors capable of outputting operational values by projecting the entity datum over a defined duration. The processor may score operational values to target values using a fuzzy inferencing system. Scoring the operational values may include classifying an operational value and the second datum to categories organized sequentially in multiple discrete increments.

The application provides a medical data-oriented deep convolution fuzzy neural network and a training method thereof, and comprises a medical data explainability prediction model (IP-DCFNN) based on a deep convolution fuzzy neural network. The IP-DCFNN is composed of three parts: a fuzzy logic antecedent part, a deep convolution calculation part and a fuzzy result representation part. The fuzzy logic antecedent part extracts input data, and the input data is converted from a numerical value into a set of membership degree values for fuzzy language scalars through the operation of a membership function in the fuzzy logic antecedent part. The deep convolution calculation part extracts hidden features in input rule weights, and converts hidden layer weights into high latitude information representation. The fuzzy result representation part is used to process the defuzzification process in fuzzy reasoning. The application relates to the technical field of computer technology, and the IP-DCFNN adds the concept of a deep convolution neural network on the basis of a fuzzy reasoning system to achieve the explainability prediction capability for medical data.

The present invention provides a multi-perspective fusion-driven summary generation method based on LLM, belonging to the field of data processing technology. The method specifically includes the following steps: Step 1: After obtaining a target document uploaded by a user, multiple potential analysis perspectives are generated based on the document content; Step 2: Using a clustering algorithm to group and cluster the potential analysis perspectives to obtain a final analysis perspective; Step 3: Generating an emotional perspective based on user needs; Step 4: Inputting the final analysis perspective and the emotional perspective into a fuzzy inference system for fuzzification, calculating the membership of the fuzzified perspective combination, and selecting the optimal perspective combination accordingly; Step 5: Designing a fusion perspective prompt template based on the optimal perspective combination; Step 6: Generating a summary corresponding to the target document based on the fusion perspective prompt template and a pre-trained language model. The present invention improves the efficiency, accuracy, and adaptability of summary generation.

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 application relates to a kind of front and rear power chain coordination control method and system for hybrid electric drive vehicle, which comprises: inputting historical vehicle speed, accelerator pedal and brake pedal signal into pre-constructed fuzzy reasoning system to obtain predicted vehicle speed; through power flow coupling relationship, the dynamic prediction model of front and rear power chain for control is pre-established, which is used to predict the future running state of vehicle, and a multi-objective optimization function considering vehicle speed tracking, SOC keeping, fuel consumption reduction and power fluctuation reduction is set, and the multi-objective optimization function is solved by numerical solution method under system constraint condition, to realize the coordinated optimization control of power distribution between different power sources of front power and longitudinal control of rear power chain.The application can weaken the severe fluctuation of power demand of hybrid electric vehicle power source under strong transient working condition, ensure the stability of engine operation under strong transient working condition, and improve fuel economy; it can be applied in vehicle control field.

The invention discloses a helicopter ANFIS self-adaptive vibration isolation control method suitable for a plateau environment working condition, and relates to the technical field of vibration isolation control of rotor aircrafts such as helicopters, rotorcrafts and unmanned aerial vehicles, and the vibration isolation control method is based on helicopter fuselage vibration spectrum characteristic data of different altitudes (1600m, 2500m, 3200m, 3800m, 4500m and the like) of the plateau environment working condition. The method comprises the following steps of: constructing an adaptive fuzzy neural network control system (ANFIS) model according to the change of a working condition, realizing adaptive adjustment of control parameters along with the change of the working condition, and improving the stability and the universality of a vibration isolation effect; and secondly, a vibration response closed-loop control method is provided for a head-up display system (Head-up Display, Hud) additionally installed in a helicopter cockpit, and structural vibration caused by the complex aerodynamic environment of the plateau working condition is suppressed in real time through an ANFIS training nonlinear mapping network.

The invention discloses a bearing real-time anomaly detection method based on an adaptive network fuzzy inference system and related equipment, and the method comprises the steps: determining a first feature index group and a second feature index group according to a first sensor signal in a bearing data set and an anomaly detection result, designing a fuzzy inference rule, calculating an abnormal score of the first feature index group, taking the second feature index group as input of an adaptive network fuzzy inference system, and training the system according to the abnormal score; and extracting a third feature index group of the bearing to be detected, inputting the third feature index group into the trained adaptive network fuzzy inference system for inference, and completing anomaly detection according to a negative anomaly feature value prediction result obtained by inference and a preset decision rule. According to the embodiment of the invention, bearing anomaly detection can be realized by combining the fuzzy reasoning system and the adaptive network fuzzy reasoning system, and the prediction accuracy and the operation efficiency are relatively high. The method can be widely applied to the technical field of bearing anomaly detection.