236 results about "Fuzzy neural" patented technology

The invention relates to the technical field of slope settlement monitoring and early warning, in particular to a visual slope settlement monitoring and early warning method and platform. The method comprises the following steps: acquiring slope settlement monitoring data; preprocessing the acquired slope settlement monitoring data; respectively constructing a multi-scale slope digital twinborn model and a hybrid intelligent prediction model; constructing a multi-level early warning index system; constructing a fuzzy neural network early warning model based on a multi-stage early warning index system through a multi-scale slope digital twinborn model and a hybrid intelligent prediction model; and performing visual slope settlement early warning by using the fuzzy neural network early warning model. According to the space-air-ground integrated monitoring network constructed by the invention, a satellite InSAR, an unmanned aerial vehicle LiDAR and distributed optical fiber sensing are fused, and full-scale monitoring from regional macroscopic deformation to slope surface microcracks and deep soil displacement is realized.

The invention relates to the technical field of program control, in particular to a precise powder supply control system, which is characterized in that multi-cycle differential processing of a rotating speed fluctuation ratio of a spiral feeder is performed on multiple key parameters such as a powder batch particle size value, a batch density value, an accumulated discharging amount and a feeding cycle; abnormal fluctuation numbers are extracted in different periods and combined according to the abnormal fluctuation numbers to generate a feeding feature set, a fuzzy neural network is utilized to improve the fitting precision of a discharging rate change trend under a nonlinear condition, the screening capability of extreme value interference is improved, a fluctuation difference value sequence is associated with symbol consistency and a density mean value, and the accuracy of the fluctuation difference value sequence is improved. Analyzing the variation trend of the discharge deviation in the symbol direction and the numerical slope, matching the numbers to establish a feed deviation grade section sequence, adopting a generative adversarial network to construct a target and actual discharge quantity difference value sequence, dividing symbol consistent sections, extracting the coupling trend of the fluctuation slope and the average density value, and obtaining a target discharge quantity difference value sequence; and misjudgment caused by deviation mode covering is avoided.

The invention discloses a quantum fuzzy neural network adaptive to high-dimensional input and a classification method, and relates to the field of quantum calculation and fuzzy neural networks and the field of computer vision. The network input layer receives high-dimensional data, amplitude coding, forward and reverse enhanced chain entanglement layer, parameterized quantum transformation and fuzzy set mapping are carried out through a quantum fuzzy feature extraction module, and dynamic dimension fuzzy features are output; high-dimensional neural features are extracted through a DNN feature extraction module to adapt to quantum fuzzy feature dimensions; dynamically distributing the weights of the quantum fuzzy features and the classic neural features through an adaptive feature fusion module; and carrying out Softmax classification on the fusion features through a classifier, and outputting a category probability. According to the method, the high-dimensional data coding efficiency can be effectively improved, the complex fuzzy logic relation learning capability of the quantum part and the quantum state correlation stability are enhanced, the uncertainty of the data is represented, and accurate classification of high-dimensional uncertainty images is realized while noise interference is reduced.

The invention discloses an industrial equipment energy consumption optimization method based on sensor data and a fuzzy neural network, and belongs to the technical field of industrial automation control, and the method comprises the specific steps: S1, collecting process data including workpiece temperature and equipment power in real time through sensors disposed on industrial equipment and a workpiece, a state estimation algorithm based on a Kalman filtering theory is adopted to carry out online calibration on key state variables of a pre-constructed multi-physics field coupling digital twin model so as to generate a system state vector capable of accurately reflecting high-dimensional information such as a predicted temperature and a curing degree in a workpiece; the method ensures the long-term effectiveness and accuracy of the digital twinborn model, and can reduce the comprehensive energy consumption while obviously reducing the residual stress of the product and shortening the curing period.

A decoupling control method for a six-axis vibration table and a system for implementing the decoupling control method for a six-axis vibration table, capable of performing real-time adjustment on the vibration table according to specific conditions, thereby enhancing the robustness of the system, and achieving high reliability and good accuracy. The method comprises: acquiring data information of a target six-axis vibration table; representing a dynamic coupling model of the target six-axis vibration table in a global linearization manner to construct a koopman predictor; using a deep neural network to obtain a feature function and an operator matrix of the predictor; performing training to obtain a deep koopman estimator, and obtaining state information of the target six-axis vibration table; using the deep koopman estimator as a prediction model to design a multi-dimensional model prediction controller; using a fuzzy neural network to perform online tuning; and using the tuned multi-dimensional model prediction controller to control the target six-axis vibration table, so as to complete decoupling control over the target six-axis vibration table.

The invention discloses a mine water storage layer leakage risk early warning and emergency decision intelligent system, which is characterized in that the system acquires osmotic pressure gradient, microseismic events, tracer migration rate and rock stratum displacement data in real time through distributed sensors, and generates a standardized multi-parameter data set through processing such as wavelet threshold denoising and variation mode decomposition; outputting a leakage probability value P and a potential fracture azimuth angle theta by using a fuzzy neural network model; early warning in three levels according to the P value, wherein Plt is greater than or equal to 0.3; when 0.6, regulating and controlling pore pressure, wherein 0.6 < = Plt; when P is larger than or equal to 0.85, sampling is encrypted, a grouting path is generated, and when P is larger than or equal to 0.85, an optimal evacuation path is calculated; constructing a grouting pressure gradient field according to the theta and the early warning grade, and dynamically matching the ratio of the leaking stoppage material; and online updating of model parameters is realized through closed-loop control. The system realizes multi-physics field coupling monitoring and dynamic adaptive decision making, and improves leakage risk assessment accuracy and emergency response efficiency.

The invention discloses a fuzzy neural network PID photovoltaic series welding temperature control method based on improved PSO optimization. The method comprises the steps that a target photovoltaic series welding equipment heating transfer function model is acquired; setting an initial PID parameter; adjusting a PID increment parameter of the PID control module in real time according to the temperature error, the temperature error change rate and a preset fuzzy rule base; the neural network is combined with fuzzy control, and parameters and rules of fuzzy control are automatically modified through training data; according to the photovoltaic series welding temperature condition, the fitness function of the PSO algorithm is improved, the adjustment mode of the inertia weight is improved, and the improved PSO algorithm is used for optimizing the initial PID parameters of fuzzy neural network PID photovoltaic series welding temperature control. Self-adaptive precise control and robust control of the welding temperature control system are achieved, the temperature fluctuation phenomenon in the photovoltaic series welding process is effectively improved, and the robustness and control reliability of the system are enhanced.

The invention discloses a water surface unmanned ship control method and system under path tracking, and relates to the technical field of attitude control, and the method comprises the steps: building a kinetic model of a water surface unmanned ship; establishing a path tracking error equation for describing a system error of the unmanned surface ship relative to an expected path under a Serret-Frenet coordinate system; constructing a path tracking controller based on a linear active disturbance rejection controller; inputting the system error and a water surface unmanned ship control signal into a fuzzy RBF neural network, and outputting an optimal control parameter of a path tracking controller by taking minimization of the system error as a target; updating a path tracking controller according to the optimized control parameters; and re-estimating external disturbance by using the updated path tracking controller, outputting a control signal for optimizing the water surface unmanned ship, and controlling the water surface unmanned ship to advance along an expected path. According to the invention, the attitude control precision under strong ocean current and changeable weather is improved, and the path tracking precision of the water surface unmanned ship is greatly improved.

The invention relates to the field of control, in particular to a reaction kettle operation control method and system for resin production, real-time operation parameters of a reaction kettle are obtained, a fuzzy neural network model is iteratively trained by adopting a hierarchical collaborative hybrid optimization strategy, a preceding member membership function of the fuzzy neural network model is composed of a Gaussian mixture model, and the preceding member membership function of the fuzzy neural network model is obtained. According to the optimization strategy, an improved quantum particle swarm optimization algorithm is used for carrying out global search to determine Gaussian mixture model parameters, a recursive least square algorithm is used for carrying out local search to determine consequent coefficients after each time of iteration, and in the training process, the parameters of the Gaussian mixture model are subjected to global search to determine the parameters of the Gaussian mixture model. And calculating an importance index according to the average activation degree of the fuzzy rule and the contribution of the fuzzy rule to the prediction error, removing the rule of which the importance is continuously lower than a preset threshold value, and after training is completed, generating and executing a control instruction for controlling the heating system power and the material feeding rate of the reaction kettle at the next moment according to the real-time parameters.

The invention belongs to the technical field of irony detection, and discloses an inconsistency perception quantum fuzzy irony detection system and method, and the system comprises a multi-mode feature coding module which is responsible for coding originally inputted text and image data, and converting the data into a high-dimensional feature matrix; the cross-modal inconsistency embedding module is responsible for respectively extracting inconsistency features of a fact level and inconsistency features of an emotion level from the encoded text and image features; the inconsistency fuzzification module is responsible for utilizing fuzzy logic processing and representing inherent uncertainty in fact and emotion information to prepare for subsequent quantum calculation; and the quantum modal fusion and detection module is responsible for performing deep fusion and interaction on the fuzzified features in a quantum calculation space, and capturing a complex cross-modal relationship by using the characteristics such as quantum entanglement and the like. The invention designs a multi-mode irony detection framework of a hybrid quantum fuzzy neural network based on inconsistency perception.

The invention relates to a power transmission inspection image enhancement method and device based on a fuzzy neural network, equipment and a medium. The method comprises the following steps: acquiring a power transmission inspection image and scene parameters corresponding to the power transmission inspection image, extracting fuzzy features of the power transmission inspection image, processing the scene parameters and the fuzzy features through a fuzzy neural network model to obtain enhanced parameters, and determining a defect type of the power transmission inspection image, and performing enhancement processing on the power transmission inspection image based on the enhancement parameter and the defect type to obtain a target inspection image. By adopting the method, the image quality can be improved.

The invention discloses a hazardous chemical substance transportation early warning system based on a fuzzy neural network, and relates to the technical field of traffic transportation and road safety monitoring. According to the hazardous chemical substance transportation early warning system based on the fuzzy neural network, road geometric influence in original data of a sensor is accurately stripped from dynamic response of a vehicle; according to the method, false risk signals caused by road design can be eliminated, the method focuses on instability risks really caused by dynamic factors such as vehicle control and load transfer, and the rollover risk can be judged more essentially and more accurately; converting the preprocessed characteristic quantity into a fuzzy linguistic variable, reasoning by simulating a fuzzy rule of human expert experience, and finally outputting continuous risk indexes and levels; the processing mode not only better conforms to the imprecise characteristic of risk assessment, but also enhances the adaptability and robustness of the system to different vehicle types, loads and road conditions.

The invention provides an intelligent proportion control method and system for an antistatic agent synthesis process, and the method comprises the steps: fuzzifying real-time parameters through real-time and historical process parameters by using an asymmetric membership function constructed based on data distribution skewness and kurtosis; historical data samples are mapped into graph theory nodes, communities are divided through a community discovery algorithm to generate fuzzy rules, initial weights are set, and an initial rule base is constructed; using a recursive least square method to identify rule consequent parameters, combining redundancy rules according to cosine similarity, and combining a particle swarm optimization algorithm to optimize antecedent parameters; and inputting the fuzzification real-time parameters into the optimized fuzzy neural network, calculating the activation intensity of the rule, adjusting the weighted average weight based on the information entropy of the current activation intensity, and obtaining the proportion control quantity of each component after defuzzification.

A large ship safety supervision system, configured to realize shipmen monitoring and ship safety supervision, wherein shipmen monitoring comprising monitoring of real time positions of shipmen in cabins and shipmen health data, and ship safety supervision comprises oceanic condition warning, on board devices running condition monitoring, ship navigation / construction monitoring and ship remote guidance. Data transmission in between ships and shores is realized by the hybrid self-adaptive compression technology based on model classification and the data transmission link intelligent selection technology based on fuzzy neural network creatively, in the meanwhile, real time safety management and supervision and ship remote work analysis and guidance can be realized.

The invention provides a software quality evaluation method and device for a power grid dispatching automation system. Belongs to the technical field of power dispatching automation. The method comprises the following steps: acquiring running state data of power grid dispatching automation system software, and extracting a third-level index from the running state data; determining subjective and objective fusion weights of the third-level indexes; aggregating the third-level indexes related to the same quality feature according to the subjective and objective fusion weights of the third-level indexes to obtain corresponding second-level indexes; an index vector composed of the second-level indexes is input into the dynamic fuzzy neural network model, and the first-level indexes of the corresponding subsystems are output to serve as comprehensive quality evaluation scores; and determining the software quality grade of the business subsystem according to the comprehensive quality evaluation score and a preset grade threshold value. The method is suitable for power grid dispatching automation system software quality evaluation oriented to complex operation scenes, and comprehensive evaluation and self-adaptive optimization of dispatching software under multi-dimensional and dynamic conditions can be achieved.

The invention discloses an optimal control method for a thermal power plant desulfurization wastewater air evaporation concentration technology, which comprises the following steps: S1, multi-source data real-time acquisition: deploying high-precision sensor groups in a concentration tower and at each inlet and outlet, and acquiring air parameters and wastewater parameters in real time; s2, data preprocessing: preprocessing the collected multi-source data to construct a data set; s3, constructing a fuzzy neural network model: constructing a two-layer fuzzy neural network model comprising a first-layer network and a second-layer network based on a fuzzy neural network algorithm; the first-layer network comprises an air parameter treatment sub-network and a wastewater parameter treatment sub-network which are arranged in parallel; the second-layer network is a decision-making layer and outputs and generates the rotating speed of an air fan, the rotating speed of a circulating pump and the concentration ratio; s4, feedback control and real-time adjustment, wherein the output value serves as a control parameter set value, and the air volume, the circulating wastewater flow, the circulating wastewater temperature and the concentration ratio are adjusted. According to the invention, the evaporation efficiency can be obviously improved, the energy consumption is reduced, and scaling is inhibited.

The invention discloses a multi-joint torque control method of a mechanical arm for cleaning foreign matters on a power distribution line. The multi-joint torque control method comprises the following steps: S1, setting an initial value and an expected track of a joint in the mechanical arm; s2, a mechanical arm multi-joint torque control model is constructed based on a fuzzy neural network; s3, performing initialization processing on the fuzzy rule, and determining an initial value of a fuzzy neural network parameter; s4, constructing an error function to calculate an error, and performing adjustment and iterative correction on each parameter; s5, the error and the error change rate are calculated to serve as input of the fuzzy neural network, and the control torque parameter of the mechanical arm serves as output; s6, performing iterative optimization processing on the output parameters of the fuzzy neural network based on a particle swarm and grey wolf hybrid optimization algorithm to obtain a global optimal solution of the output parameters of the fuzzy neural network, and obtaining the joint torque of the mechanical arm; according to the method, the joint output torque can be accurately controlled, the joint position tracking error is reduced, and the overall robustness and stability of a mechanical arm control system are improved.

The invention discloses an electric vehicle charging load prediction method based on an optimized fuzzy neural network, and the method comprises the steps: obtaining a plurality of historical load data of an electric vehicle, and processing all historical load data to generate a multi-dimensional feature data set; dividing an input feature space for the multi-dimensional feature data set based on an improved fuzzy clustering algorithm to obtain a clustering result and an activation intensity-contribution degree two-dimensional evaluation index; iteratively optimizing FNN parameters based on an improved adaptive differential evolution algorithm; constructing a rule contribution degree analysis model based on an orthogonal experimental design, setting a quantitative truncation threshold with the cumulative interpretation degree greater than or equal to 85%, and realizing self-adaptive compression of the scale of the rule base; an activation intensity threshold value dynamic calculation method is used, low-efficiency rules are automatically identified through sliding window statistics, and rule pruning is achieved; and outputting a load prediction result, and applying non-negativity and peak constraint to a prediction value.

The invention relates to the technical field of photovoltaic power generation system control, and provides an MPPT control method and system based on an adaptive fuzzy neural network, and the method comprises the steps: synchronously collecting voltage, current, illumination and temperature signals, and generating original sensing data; transmitting the generated original sensing data to a memory mapping area through a hardware channel; sensing data is obtained from the memory mapping area, calculation is executed, a power variation and an illumination variation are predicted, a fuzzy reasoning algorithm is operated based on the power variation and the illumination variation, and a power point decision scheme is obtained; a fuzzy rule is updated based on the predicted power variation and illumination variation, the updated fuzzy rule is fed back to the dual-core heterogeneous processor in real time, and fuzzy reasoning logic is optimized; and driving the four-phase interlaced power topological structure to work in an interlaced manner according to the power point decision scheme so as to realize maximum power point tracking. According to the invention, rapid tracking and high-precision stable control of the maximum power point of the photovoltaic array in a complex environment can be realized.

The invention discloses a multi-input multi-output fuzzy neural network control method and system for a pseudo-ginseng planting greenhouse, and the method comprises the steps: obtaining original parameters including greenhouse temperature, humidity, illumination, water and fertilizer concentration, pseudo-ginseng growth state and water flow velocity, and carrying out the standardization of the original parameters, and obtaining input parameters; inputting the parameter into a three-layer back propagation neural network pre-training model, and outputting initial adjustment coefficients of six control quantities such as the opening degree of the sunshade; constructing a fuzzy rule base based on a parameter coupling scene, inputting a preliminary adjustment coefficient to obtain a fuzzy output quantity, and performing defuzzification through a gravity center method to obtain a corrected adjustment coefficient; a control instruction is calculated in combination with an equipment rated range, and closed-loop control is realized through dynamic adjustment. The system automatically operates through a multi-parameter acquisition module, an equipment control output module and the like. The application breaks through the traditional single-input single-output limitation, reduces the parameter fluctuation by more than 50%, adapts to the whole growth cycle of pseudo-ginseng, improves the utilization rate and yield of water and fertilizer, reduces the labor cost, and has remarkable economic benefits.

The invention discloses a fuzzy neural network-based intelligent tension control method and system for a plateau tunnel pre-stressed anchor rod, and the system is characterized in that a free section of the anchor rod is sleeved with a negative Poisson ratio outer sleeve, a magneto-rheological regulation and control cavity is formed in the anchor rod, magneto-rheological fluid is injected into the magneto-rheological regulation and control cavity, and a friction power generation-sensing assembly is mounted at the tail end of the anchor rod; therefore, the anchor rod unit integrating friction increase along with the load, adjustable damping and self-energized sensing is formed. In the tensioning process, the friction power generation-sensing assembly is used for measuring the elongation of the anchor rod and providing self-powered energy, the pressure of a magneto-rheological regulation and control cavity and the displacement of a piston are collected, a state vector containing design prestress, the elongation, equivalent damping force and environment vibration indexes is constructed and input into a fuzzy neural network controller, and the state vector is calculated. The expansion amount of the hydraulic jack and the current of the electromagnetic coil are output, and intelligent application, dynamic compensation and long-term monitoring of prestress of the anchor rod group are achieved. Self-sensing, self-adaptive adjustment and active friction increasing of the pre-stressed anchor rod group can be achieved without an external power source.

The invention discloses a mutual inductor operation error on-line monitoring system, and belongs to the technical field of detection and analysis. The method is used for solving the technical problems of secondary load and error characteristics of the mutual inductor in the existing scheme. Electrical characteristics and environmental parameters are fused through fuzzy neural reasoning, so that the identification error of error characteristic parameters can be effectively reduced; wavelet denoising and momentum item gradient descent are combined, so that the parameter updating stability and the system robustness can be effectively improved under sensor noise and temperature drift; by correcting a secondary load coupling dynamic model in real time, the goodness of fit of secondary load-error nonlinear mapping can be effectively improved; through real-time compensation of the magnetic control reactance, dynamic error suppression is realized, and ratio error drift and angular error drift caused by secondary load change can be effectively reduced; and the reactance-error mapping relation is corrected by using the dynamically updated output error characteristic parameters, so that the overall error suppression capability of modeling, identifying and compensating a closed loop can be effectively improved.

Disclosed in the present invention is a disturbance observation method for a six-axis vibration table. The method comprises: acquiring data information of a target six-axis vibration table; constructing an extended state observer for the target six-axis vibration table; constructing a fuzzy neural network identifier and a fuzzy neural disturbance observer for the target six-axis vibration table, setting start-stop rules, and performing training; and using a trained fuzzy neural disturbance observer to complete disturbance observation for the target six-axis vibration table. Further disclosed in the present invention are a system implementing the disturbance observation method for a six-axis vibration table, and a disturbance rejection control method comprising the disturbance observation method for a six-axis vibration table. In the present invention, by means of the design and implementation of an extended state observer, a fuzzy neural network identifier and a fuzzy neural disturbance observer, not only disturbance observation and disturbance control of a system are realized, thus improving the tracking performance of the system, but also higher reliability and higher accuracy are realized.

This invention relates to the field of remote control technology for plant irrigation, and discloses a remote control method for irrigation of plants under bridges based on the Internet of Things. This method collects soil moisture gradient, light intensity distribution, and air temperature and humidity stratification data through a three-dimensional gridded sensor array, constructs a heterogeneous transmission network based on LoRaWAN and NB-IoT to achieve dynamic compression and encrypted transmission, establishes a digital twin model integrating BIM and point cloud data on a cloud platform, uses a fuzzy neural network to predict plant water requirements and generate zonal irrigation decisions, and performs timing optimization scheduling of multi-level PWM commands through an edge computing gateway to control a modular solenoid valve array to perform precise irrigation. This method solves the technical problems of insufficient environmental parameter acquisition dimensions, poor data transmission stability, limited dynamic control adaptability, and lack of system collaborative control capabilities.

This invention provides a method and system for predicting vehicle speed in commercial vehicles using the E-Power architecture, relating to the field of commercial vehicle speed measurement technology. First, vehicle operating data is collected via hardwired and CAN bus lines. Second, the PCA method is used to extract features from the collected vehicle operating data, obtaining variables highly correlated with vehicle speed, which serve as input to the speed prediction model, improving computational efficiency. Finally, a speed prediction model is established based on a recursive fuzzy neural network and a multi-step prediction strategy, achieving accurate prediction of vehicle speed during operation and improving fuel economy and driving comfort.

This disclosure relates to the field of exoskeleton technology, and discloses an exoskeleton control method, storage medium, and exoskeleton. The exoskeleton control method includes: acquiring gait feature values; inputting the gait feature values ​​into a fuzzy neural network model to obtain a target gait output value; determining candidate gait phases and previous gait phases based on the target gait output value, wherein the gait output value of the previous gait phase is temporally adjacent to the target gait output value; determining the target gait phase based on the candidate gait phases and the previous gait phase; and controlling the exoskeleton based on the target gait phase. This embodiment identifies gait phases by comprehensively considering previous gait phases and candidate gait phases, which helps improve the accuracy and reliability of gait phase identification, thereby enabling the exoskeleton to more effectively drive the user's limbs, thus providing human-machine compliance.

The invention provides a resin composition for a halogen-free flame-retardant cable sheath and a preparation method of the resin composition, and belongs to the technical field of polymer composites for cables, the preparation method comprises the following steps: S1, raw material grading pretreatment and core parameter accurate determination; s2, gradient pre-dispersion and uniformity feedback regulation and control; s3, step-by-step grafting compatibility and grafting rate feedback regulation and control; s4, carrying out dynamic flame-retardant regulation and control melt blending; and S5, gradient cooling granulation and finished product performance verification. In two key links of gradient pre-dispersion and dynamic melt blending, an improved fruit fly optimization algorithm-adaptive fuzzy neural network algorithm and an improved sparrow search algorithm-kernel extreme learning machine algorithm are respectively embedded, and the two algorithms are bidirectionally interactively fused, so that the problems that traditional process parameters are set according to experience and the performance fluctuation is large are solved, and the dynamic melt blending method is suitable for industrial production. The synergistic improvement of the uniformity, the flame retardant property and the mechanical property of the resin composition is realized.

This invention discloses a primary frequency regulation optimization control method for new energy systems that considers the State of Energy (SOC) of energy storage. It overcomes the problem that traditional wind power control parameter tuning often relies on empirical formulas and fails to fully consider the dynamic changes in energy storage SOC. This method analyzes the power frequency characteristics of the grid-connected system and constructs a grid-connected frequency control model. Based on the high and low operating ranges of the energy storage SOC, it divides the system into correction coefficients and power allocation weights, calculates the total demand power based on frequency deviation, and allocates it to the wind turbine and energy storage system, establishing an adaptive frequency regulation power allocation strategy. A multi-objective function is constructed with the goals of minimizing frequency deviation, SOC fluctuation, and energy storage losses. An improved fuzzy neural network-deep reinforcement learning algorithm is used to identify operating conditions and iteratively correct control parameters, forming a closed-loop optimization to adapt to multiple operating conditions. This invention improves the frequency response speed and regulation margin of wind power under complex operating conditions while also considering energy storage lifespan, achieving synergistic optimization of frequency regulation performance and energy storage economy.

The application provides a training method and device of a fault detection model, a fault detection method, electronic equipment and a readable storage medium. Embodiments of the application acquire fault information of a display substrate and maintenance data corresponding to the display substrate, input the fault information into a fuzzy neural network to generate a maintenance scheme through the fuzzy neural network, and train the fuzzy neural network according to the maintenance data and the maintenance scheme to obtain a fault detection model. In this way, embodiments of the application can continuously acquire fault information and learn the processing mode for faults, thereby realizing identification and processing of more faults and helping to improve the identification effect of the faults existing in the display substrate.

The invention provides a human body joint angle measuring method and device, and the method comprises the steps: obtaining the sensing data of a multi-mode sensor for synchronously monitoring a joint angle to be measured, obtaining the joint angle measured by various sensors based on the sensing data collected by the various sensors, and calculating the angle of the joint. Joint angles measured by various sensors are subjected to data fusion through a fuzzy neural network, and a final accurate joint angle measurement result is obtained. According to the method, the inherent limitation of the technology of measuring the joint angle through a single sensor is overcome, the joint angle is measured through multiple sensors used for measuring the joint angle, fusion is carried out, and advantage complementation is achieved. The invention further provides a multi-mode sensor which comprises an inertial measurement unit, a flexible strain sensor and an optical distance measurement sensor, and in the joint angle measurement process, the defect of a single sensor is eliminated through a data complementation and fusion algorithm.