351 results about "Fuzzy neural" patented technology

The invention relates to a multi-sensor cross-scene dynamic preferential fusion positioning and mapping method, and the method comprises the steps: obtaining the data of a plurality of sensors, and completing the unification of the time-space relation of the data of the plurality of sensors; processing the data, carrying out loopback detection on image key frame data acquired by a camera, constructing to obtain an I MU pre-integration factor, a visual inertial odometer factor, a laser radar odometer factor, a GPS inertial odometer factor, a UWB factor, a GNSS factor and a loopback detection factor, and adding the factors into a factor graph for optimization; a global positioning pose and a map are obtained; and optimizing the multi-sensor data fusion strategy based on a deep fuzzy neural network. According to the multi-sensor cross-scene dynamic preferential fusion positioning and mapping method provided by the invention, high-precision positioning and navigation of an agricultural robot in different scenes are realized through real-time fusion of various sensor data, and the problems of scene dependence and insufficient precision of an existing single sensor scheme are solved.

The invention discloses a ship intelligent collision avoidance decision-making method and system, and relates to the technical field of ship intelligent navigation, and the method comprises the steps: collecting radar data and AIS information of a ship; building a ship dynamic prediction model according to the radar data and the AIS information, predicting the future navigation trajectory of the target ship through a Kalman filtering algorithm, and calculating the meeting parameters of the two ships in combination with the navigation parameters of the ship; based on the meeting parameters, establishing a risk assessment matrix, and performing risk level judgment on the current navigation situation by using a fuzzy neural network algorithm; based on the risk level judgment result, collision avoidance maneuvering parameters are calculated through an adaptive particle swarm optimization algorithm, and a collision avoidance action scheme is output. According to the invention, the whole process automation from information perception, situation prediction, risk assessment to decision execution is realized, the workload of navigation personnel is greatly reduced, and the navigation safety level is improved.

The invention discloses an intelligent storage equipment health state assessment and predictive maintenance method and system, and the method comprises the steps: arranging a multi-mode sensor network on equipment, collecting vibration, stress, temperature, current, voltage and image data in real time, carrying out the collection and preprocessing of edge data, and transmitting the data to an analysis layer; and the analysis layer performs online self-learning calibration by using a digital twin model, fuses the data in combination with a fuzzy neural network for dynamic weight adjustment, and calculates a health index and a residual life prediction value of the equipment. The system can give out an early warning before a fault symptom occurs, and automatically generates a maintenance decision and a task scheduling scheme, thereby achieving the real-time monitoring and predictive maintenance of the equipment state, and remarkably improving the operation efficiency of a warehousing system and the reliability of the equipment.

The invention relates to the field of industrial automation and intelligent control, and discloses an intelligent temperature automatic control system for a digital glass mold. The method comprises the following steps: acquiring mold surface temperature and heat flow data through a thermocouple array and a thermal infrared imager, and constructing heat flux and a disturbance coefficient; forming state vectors are established in combination with the forming process parameters and the heat flow distribution, and forming stability levels are generated through support vector regression; building a heat balance deviation model based on the thermophysical parameters and the environment variables, and predicting a temperature trend; a temperature control instruction is generated through the fuzzy neural controller, and temperature dynamic closed-loop control is achieved. The system improves the temperature control precision and thermal field balance of the glass mold, and is suitable for intelligent temperature control management in the glass container forming process.

The invention provides a river crab feed feeding mixed monitoring method, system and device and a medium, and belongs to the field of aquaculture intelligent monitoring. Data is collected and normalized, and a chain type data collection system is constructed; the edge computing and block chain technology is adopted to realize data distributed storage and transmission; a fuzzy neural network hybrid model is constructed, fuzzification processing, feature mining and defuzzification output are integrated, and a feeding amount prediction value is generated; associating the feed proportioning library with the knowledge graph, and iteratively training the model and generating a multi-target feeding decision parameter; feeding equipment is controlled to execute actions according to the decision parameters, and the real-time monitoring model outputs and triggers an early warning signal; and updating the knowledge graph and the feed matching library based on the feedback data to form closed-loop self-adaptive regulation and control. According to the method, various factors can be comprehensively considered, scientific and intelligent feed feeding decision is realized, feed waste and insufficient feeding are effectively avoided, and the growth quality of the river crabs is improved.

The invention relates to the technical field of lithium battery safety management, and particularly discloses a lithium battery thermal runaway early warning and protection method based on an adaptive neural network, which comprises the following steps: acquiring temperature, internal resistance and voltage data of a lithium battery in real time, extracting multi-dimensional features based on discrete wavelet transform, singular value decomposition and principal component analysis algorithms, and performing early warning and protection on the lithium battery thermal runaway. The influence of internal resistance and voltage on abnormal temperature fluctuation is quantified; constructing a feature vector through the comprehensive influence coefficient, inputting the feature vector into an adaptive fuzzy neural network prediction model, outputting a temperature anomaly fluctuation coefficient, and accurately predicting the thermal runaway risk of the lithium battery; according to the lithium battery thermal runaway prediction method, multi-level protection measures such as early warning response triggering, system automatic charging and discharging strategy adjusting, power output reducing and heat dissipation system starting are taken based on the prediction result, development of the thermal runaway risk is restrained, multi-dimensional data fusion and an intelligent algorithm are combined, and the lithium battery thermal runaway prediction accuracy and the risk control real-time performance are improved.

The invention relates to the technical field of gas steel cylinder pressure control, in particular to a gas steel cylinder multi-stage pressure control method based on a self-adaptive threshold value, which can dynamically correct the influence of ambient temperature on a pressure reference value through a constructed pressure-temperature compensation function, eliminate measurement errors caused by temperature drift, and improve the accuracy of pressure control. A pressure fluctuation entropy value is calculated in real time based on a sliding window algorithm, a self-adaptive threshold value adjustment coefficient alpha is generated in combination with historical working condition database matching, a control threshold value is dynamically adjusted along with gas flow velocity fluctuation and equipment aging degree, and the pressure over-limit risk is reduced; according to the method, fuzzy neural network control is introduced in a critical adjustment stage through a multi-stage pressure adjustment strategy, an improved radial basis function dynamic updating mechanism can adapt to a pressure sudden change mode online, the control response speed is increased, and the steady-state error is controlled within + / -1.5%.

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 belongs to the technical field of laser welding, and discloses a real-time feedback control method and system for laser welding penetration stability, and the method comprises the steps: obtaining plasma electron temperature characteristics in a laser welding process in real time through a spectrum monitoring system, and enabling the plasma electron temperature characteristics to be associated with penetration fluctuation as a core input signal of feedback control. The signal has better real-time performance and more accurate feature extraction capability in real-time feedback control of laser welding by virtue of broadband coverage and multi-dimensional information acquisition capability of the signal in combination with a more efficient data analysis mode. A parallel type self-learning fuzzy neural network controller is used for executing real-time feedback control output of laser welding penetration fluctuation. On a control architecture, a traditional PD controller and a fuzzy neural network are connected in parallel and are respectively used as a PD control module and a fuzzy neural network control module. And the process database is embedded into the forepart structure of the neural network control module in a fuzzy rule form.

The invention discloses an expressway hard shoulder dynamic opening method and system based on an NFGD model and a simulation platform. Comprising the following steps: collecting and adopting a dynamic confidence mechanism to carry out weighted fusion on multi-source real-time sensing data to obtain fused traffic state characteristics; a hard road shoulder dynamic control decision is made based on an NFGD model comprising a fuzzy-neural hybrid controller, a multi-target genetic algorithm controller and a reinforcement learning controller; and real-time interaction with a simulation platform is realized, and control instruction issuing, feedback acquisition and online strategy evaluation are realized. Compared with a traditional method based on a fixed empirical threshold value, the fuzzy-neural hybrid controller, the multi-target genetic algorithm controller and the reinforcement learning controller are fused, self-adaptive modeling and open discrimination are achieved, and decision-making precision and scene adaptability are improved. And meanwhile, by combining with a prediction adjustment feedback type step length control structure, simulation time drift is effectively inhibited, the timeliness of a control strategy is enhanced, and the method has relatively high practical application value.

The invention provides a wind power plant bird trajectory prediction and fan linkage control method based on a neural network, and relates to the technical field of intelligent power grids, and the method comprises the steps: recognizing a bird target in real time, generating trajectory data, processing the trajectory data through three-dimensional Hilbert-Huang transform and an adaptive decomposition algorithm, extracting feature parameters for prediction analysis, and obtaining a bird trajectory prediction result. A collision risk is predicted and evaluated based on a trajectory, a cooperative avoidance control algorithm is constructed by adopting an artificial potential field method, a control strategy is optimized by combining an adaptive fuzzy neural network and a sliding mode controller, the operation state of a fan is monitored in real time, normal operation of the fan is recovered after birds fly away safely, and intelligent bird protection of a wind power plant 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 provides an underwater propeller control method and system based on tensor recognition and fuzzy control. The underwater propeller control method and system are suitable for improving the propelling efficiency and adjustment intelligence in a complex flow field. According to the method, a wake flow simulation model is constructed based on geometric parameters and boundary conditions of a propeller, a rotation tensor and a strain tensor are derived after flow field data are obtained, and a tensor field index is extracted through function space mapping so as to determine a target grid region. A high-rotation candidate area is screened through a spectral clustering algorithm, a vortex structure is identified, and multi-dimensional features such as the scale, the strength, the axial direction and the vortex core position of the vortex structure are extracted. And constructing a state vector by combining the current propulsive efficiency and flow field disturbance parameters, inputting an adaptive fuzzy neural network model, reasoning a relationship between a vortex and an operation state, outputting a propeller rotating speed and an attack angle adjusting quantity, and realizing intelligent response and energy efficiency optimization of wake flow disturbance.

The invention belongs to a robot control method, and provides a human-guided robot-environment interaction self-adaptive control method and a related device for solving the technical problems that an existing human-guided robot-environment interaction control method is insufficient in motion stability, low in human operator safety and low in control precision. And estimating an optimal impedance parameter through a state space model, and learning a human-guided reference trajectory through a neural network in combination with the optimal impedance parameter. Then, in combination with a robot Jacobian matrix and a human-guided reference trajectory, a reference joint speed and an estimated acceleration of a joint space are obtained through closed-loop inverse kinematics, then network parameters of a robot dynamic model are obtained through approximation of an uncertain dynamic model, and finally, the robot dynamic model is obtained through combination with the reference joint speed and the network parameters of the joint space. And the mechanical arm control torque is obtained through the offset width fuzzy neural network. Accurate and compliant control under uncertain motion and dynamics conditions is realized.

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 determination of a production index for a selected well using a fuzzy logic and neural network model (a “fuzzy-neural model”). Input data may be obtained from one or more producing wells and preprocessed for use in training and testing. The preprocessed data may be fuzzified into fuzzy values using fuzzy sets, membership functions, and a rule base. The neural network may be trained using the fuzzy values from the fuzzification to output the production index. The trained fuzzy-neural model may then be used to determine a production index for new data from the selected well.

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 invention provides an electro-hydraulic servo pump control method and system for controlling hydrogen compression based on fuzzy nerve, and relates to the technical field of hydrogen compression, and the method comprises the steps that the current state of a hydraulic cylinder controlled by an electro-hydraulic servo pump is determined, and an input control variable is obtained; determining an output control variable, establishing a fuzzy control rule, and dividing a basic discourse domain; performing composite defuzzification, and obtaining a corrected weight coefficient through a feedforward neural network; the discrete rule amplitude correction factor and the weight coefficient correction are dynamically optimized and corrected; neural network training is established, a loss function evaluation model is utilized, and hydrogen compression control is achieved through a double-buffering mechanism. Fuzzy PID control is adopted, composite defuzzification is carried out according to the real-time state change of a system in combination with fuzzy logic, a membership function output by the fuzzy PID is divided according to regions, a control signal of a driver is synthesized, and hydrogen compression control is achieved; system output is adjusted according to different working conditions, power loss is reduced, and system control precision is improved.

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 provides a breast milk feeding amount intelligent calculation and dynamic adjustment method and system, and relates to the technical field of intelligent feeding, and the method comprises the steps: collecting real-time physiological parameters of an infant, constructing an infant digital model through a multi-scale morphological neural network, calculating a basic energy consumption value through combining with a biological rhythm prediction model, and calculating the feeding amount of the infant. The standard breast milk feeding amount is calculated through the self-adaptive fuzzy neural network; the method comprises the following steps: collecting mother physiological state data and breast milk component data, calculating a breast milk nutrition density coefficient by using a deep mixing cognitive network and an immune evolution self-organizing network, correcting a breast milk reference feeding amount, and finally calculating an actual breast milk feeding amount through a self-adaptive resonance theory network; growth and development data of an infant after breast milk intake are collected, a multi-layer spiral pulse neural network is used for extracting development feature vectors, a development deviation value is calculated based on a fractal dynamics model, network parameters are dynamically adjusted through a biological group intelligent optimization system, and dynamic optimization of breast milk feeding amount is achieved.

The invention discloses a material flow rate control method and system for liquid silica gel injection molding, and the method comprises the following steps: S1, collecting temperature gradient distribution in an injection mold, silica gel initial viscosity and pressure intensity data in a mold cavity in real time through a multi-mode sensor network, and inputting the data into a pre-molding parameter model; s2, based on the pre-forming parameter model; s3, outputting a result according to the dynamic response equation; and S4, dynamically calibrating the regulation and control parameter set through a fuzzy neural network in combination with a closed-loop feedback mechanism, and driving an execution mechanism to adjust the silica gel injection rate based on the calibrated parameters. The industrial bottleneck of liquid silica gel injection molding flow rate control is systematically solved; the cooperative construction of the multi-modal sensing network and the multi-field coupling model breaks through the physical limitation of a traditional empirical formula, and realizes the improvement of the dynamic response precision under a complex working condition.

The invention belongs to the technical field of battery temperature control, and provides a blade battery temperature control method and system based on a fuzzy neural network, and the method comprises the following steps: monitoring the temperature of a battery cell and a phase change material in real time, monitoring the charging and discharging current of a battery in real time through a current transformer, and obtaining current data; preprocessing the battery cell temperature data, the phase change material temperature data and the current data to obtain preprocessed data; the preprocessed data serve as input variables and are input into a fuzzy neural network for fuzzy processing, and a temperature adjusting instruction is generated according to the preset working temperature range of the blade battery; according to the temperature adjusting instruction, the temperature of the blade battery is adjusted, and the temperature change is monitored in real time; through the complex mapping capability of the fuzzy neural network, accurate control over the battery temperature is achieved, temperature fluctuation is reduced, the consistency of battery performance is improved, and the temperature control performance of the blade battery is improved.

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 provides a steering-by-wire vehicle variable transmission ratio design method and system based on a fuzzy neural network, and belongs to the field of vehicle steering-by-wire. According to a fixed gain method, the steering angle transmission ratio is too small at a low speed, and the sensitivity is low at a high speed; the existing intelligent algorithm excessively depends on the experience of a designer and the quality of a data sample. The method comprises the following steps: establishing a closed-loop driver-vehicle system, designing a multi-target evaluation method by using a quadratic cost function of a vehicle dynamic state, and obtaining a data relationship between a vehicle ideal variable transmission ratio characteristic and a vehicle longitudinal speed and a steering wheel angle; a nonlinear control model is established through a fuzzy RBF network, and a globally optimal solution is obtained based on nonlinear model learning. The robustness of the control system is improved, the stable and reliable transmission ratio can be provided under various vehicle conditions, and the problem that a steering system is light and flexible is solved, so that the experience feeling of a driver under various vehicle conditions is improved, and meanwhile the operation stability and safety of the vehicle are 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.

According to the data-driven adaptive optimization control method for the aeration process of urban sewage treatment, accurate aeration of a plurality of aerobic zones in the aeration process of sewage treatment is controlled, and the aeration energy consumption is reduced while the quality of outlet water is stabilized. According to the invention, a cascade prediction model based on the fuzzy neural network is designed, the dynamic coupling relationship between adjacent aerobic zones is learned, and the nitrate nitrogen concentration in the sewage treatment aeration process, the operation index of each aerobic zone and the future value of the dissolved oxygen concentration are accurately predicted; a self-adaptive optimization controller is designed, and a self-adaptive weight is introduced to dynamically adjust aeration of each aerobic zone so as to coordinate ammonia nitrogen removal requirements and energy consumption optimization of each aerobic zone. Experimental results show that the method can accurately control the aeration of each aerobic zone in the sewage treatment aeration process, improves the effluent quality in the sewage treatment process, and reduces the aeration cost at the same time.