41 results about "Neural fuzzy" patented technology

The invention belongs to the field of data processing, and particularly relates to a dynamic cooperative control system and method for a gas turbine and a micro-grid, and the method comprises the steps: constructing a micro-grid real-time monitoring module, continuously collecting distributed energy real-time output, controllable load demands, bus voltage frequency and equipment state parameters, and carrying out the filtering and noise reduction through a preprocessing unit, thereby guaranteeing the data precision; calculating a real-time power difference value based on the preprocessed data, calling an adaptive neural fuzzy inference system, taking the power difference value, the bus voltage deviation and the frequency deviation as input, and judging whether the power difference value, the bus voltage deviation and the frequency deviation exceed a preset threshold value by means of a fuzzy rule base and a neural network model; if the threshold values are not exceeded, the current states of the gas turbine and the energy storage system are maintained; if any one exceeds the threshold value, a dynamic cooperative control instruction is triggered, precise cooperative control of the gas turbine and the micro-grid is achieved, and the operation stability, the operation efficiency and the reliability of the micro-grid are improved.

The invention discloses an automatic temperature control heating method and system. The method comprises the steps that heating state data are obtained in real time and preprocessed; a deep neural network is adopted to predict the heating state data to obtain heating control parameters; predicting the historical heating state sequence data by adopting a long short-term memory network to obtain a future parameter change value; a reinforcement learning algorithm is adopted for reinforcement feedback, and an optimization control signal for the heating control parameters is obtained; performing fuzzy reasoning by adopting an adaptive neural fuzzy reasoning system, and adaptively adjusting rules and weights to obtain a fuzzy control signal; and integrating the parameters and the signals, and generating a heating control signal for the heating equipment through weighted fusion. According to the technical scheme, intelligent automatic control over the heating equipment is achieved, the monitoring capacity and the process management and control capacity of the heating equipment are greatly improved, the heating quality of products is improved, the rejection rate of the products is reduced, and the automation and intelligence level is improved.

The invention provides a vehicle fault diagnosis method and device based on multi-source data fusion and a storage medium. The method comprises the following steps: collecting real-time state data and real-time alarm data of a target part, and obtaining external environment data and map navigation data; preprocessing the data, fusing the preprocessed data to obtain a multi-source data set target feature, inputting the target feature extracted from the multi-source data set into an adaptive neural fuzzy inference system, mapping the target feature to a plurality of fuzzy sets, and fuzzifying by using a member function; calculating the rule weight of each target feature to reflect the fuzzy intensity; and training an inference system by adopting a neural network learning algorithm, adjusting a member function and a rule weight, and converting fuzzy output into a specific numerical value or a fault level by utilizing an output member function in a defuzzification process so as to judge the health state of the part. According to the invention, high-interpretability fault diagnosis output is provided, the accuracy is improved, and fault diagnosis can be carried out in a wide vehicle operation environment.

The invention relates to the field of artificial intelligence and computer science, in particular to a causal structure and latent variable-based adaptive reinforcement learning reasoning migration method, which comprises the following steps of: constructing a causal world model fused with multi-modal observation and a decoupling latent variable space; establishing a hierarchical inference engine comprising an intuition layer, a conventional layer and a planning layer; pre-training a quick response and judicial planning dual-mode strategy and generating an interpretable fuzzy rule base; performing calculation level coarse tuning based on task identification and causal complexity; evaluating the real-time state criticality through an adaptive neural fuzzy system and dynamically switching a decision mode; after the action is executed, the threshold and the rule are subjected to closed-loop optimization, and cross-environment efficient migration is realized by utilizing a causal modularization characteristic. According to the technical scheme, consumption of computing resources is remarkably reduced on the premise that decision precision and safety are guaranteed, and the response speed and cross-scene adaptive capacity of a system on edge equipment are improved.

The invention relates to the field of education data mining and teaching process evaluation, in particular to an online and offline mixed teaching performance dynamic evaluation method and system based on a PDCA (Packet Data Convergence Architecture) circulation, and the method comprises the steps: generating initial Rubric configuration and formulating a multi-source data collection scheme through the analysis of course types and learning condition information around the PDCA circulation; then, online and offline multi-modal information is collected and preprocessed, and data with labels and basic statistics are obtained; and in combination with causal graph construction and quantum multi-group fuzzy weight optimization, carrying out comprehensive evaluation on the data, outputting adaptive Rubric configuration and student scores, and generating a readable report and an early warning through a neural fuzzy abstract. And according to the score and early warning, implementing difference enhancement and holographic AR feedback, collecting intervention process data again, and dynamically adjusting Rubric to complete iteration. According to the invention, the teaching performance can be continuously optimized, and accurate early warning and personalized intervention are realized.

The invention discloses an intelligent feces treatment system and method, and belongs to the field of sewage treatment.The intelligent feces treatment system comprises a data acquisition module used for collecting COD, ammonia nitrogen, DO, pH, water temperature, SS and microbial activity parameters of sewage in real time and conducting normalization processing on the collected water quality parameters to form a unified standard data basis; the first model module is used for optimizing membership function parameters and fuzzy rules of an adaptive neural fuzzy inference system by applying a particle swarm algorithm based on the data processed by the data acquisition module, outputting a predicted value of COD concentration in a biochemical pool in real time by training an ANFIS model, and forming a COD prediction model; and the second model module is used for constructing a Stacking integrated model by taking the COD predicted value in the first model module and the data processed in the data acquisition module as input, the integrated model comprises a base learner and a meta learner, and then a prediction result of the base learner is generated by adopting a five-fold cross validation mode.

The invention discloses a lithium battery system state-of-charge estimation method based on a Hammerstein model, and the method comprises the steps: constructing a second-order RC circuit equation of a lithium battery, describing a dynamic linear module of the Hammerstein model through a noise transfer function model, and constructing a lithium battery system through a static nonlinear module of an adaptive neural fuzzy network; designing a Gaussian signal, inputting the Gaussian signal into the agent model of the lithium battery system to obtain corresponding Gaussian signal output, and decoupling the static nonlinear module and the dynamic linear block by using the covariance function characteristic of the Gaussian signal; identifying parameters of the noise transfer function model by using a least square method based on a covariance function, solving parameters of the adaptive neural fuzzy network by using a crown porcupine optimization algorithm, and updating the weight of the adaptive neural fuzzy network by using a stochastic gradient algorithm with a forgetting factor; and constructing an OCV-SOC curve of the open-circuit voltage and the state of charge by adopting polynomial fitting, and taking output obtained by the Hammerstein model as input of the polynomial fitting to obtain an estimated value of the state of charge SOC.

The invention discloses an intelligent segmentation and feature analysis method for a medical image, and relates to the technical field of image analysis, and the method comprises the steps: reading a multi-modal medical image, and carrying out the preprocessing and size standardization; performing medical image segmentation based on a U-Net structure, performing edge detection by adopting a Sobel operator, and optimizing the edge of the segmented image in combination with morphological operation to obtain an edge-optimized focus segmentation map; feature point matching is carried out on the multi-modal focus segmentation image based on self-organizing mapping, and non-rigid registration is carried out through TPS transformation based on matched feature points; carrying out foreground region enhancement operation on the registered image, introducing a channel attention module based on a DenseNet-201 model to carry out feature extraction on the registered image, and optimizing features by using a whale optimization algorithm to generate an optimal feature subset; and carrying out illness state classification on the feature subsets by an adaptive neural fuzzy inference system optimized based on a genetic algorithm.

The invention relates to the technical field of industrial control systems, and particularly discloses a selenium-rich water equipment water yield control system based on neural fuzzy control. The system comprises a data acquisition module, a neural fuzzy control module, an actuator driving module and an online learning and parameter adjustment module, fuzzy rules and membership parameters are dynamically optimized through a neural network, a gradient descent algorithm and a working condition memory unit are combined, high-precision self-adaptive control over the water yield is achieved, and the response speed and stability of the system are improved.

The application provides a power system frequency anti-interference regulation method and device under certainty network multiple attacks, and the method comprises the following steps: obtaining multiple groups of frequency regulation related parameters of a controlled area; inputting regional frequency deviation data and regional power deviation data in each group of frequency regulation related parameters into an adaptive network attack detection system to obtain estimated regional control error data; the adaptive network attack detection system utilizes sample data under multiple network attacks collected in advance to train a neural fuzzy system physical model; according to actual regional control error data and estimated regional control error data of the multiple groups of frequency regulation related parameters, root mean square error is calculated; according to the root mean square error and a preset range, a relieved regional control error is determined from the actual regional control error and the estimated regional error. The application comprehensively considers multiple attacks, can quickly detect new attacks, and improves power adjustment efficiency.

The invention provides an arterial traffic coordination signal timing optimization method, system and device and a medium, and belongs to the field of traffic control, and the method comprises the steps: building a dynamic model of traffic signal control through a Markov decision process, obtaining a value function according to the traffic signal control model, and achieving the precise description of signal timing. A value function is introduced into a brightness function and a position updating function of a firefly algorithm, and an adaptive light intensity absorption coefficient and a dynamic random disturbance factor are introduced to improve the firefly algorithm, so that an improved firefly algorithm is obtained, and the global search capability of the firefly algorithm is improved. And performing optimization training on the adaptive neural fuzzy inference system through an improved firefly algorithm to obtain a signal timing optimization model. And outputting the adjustment amount of the signal timing parameter according to the signal timing optimization model, thereby realizing the self-adaptive intelligent control of the traffic signal timing. The method can be applied to a complex and changeable arterial traffic environment, and a signal timing optimization strategy is efficiently and accurately provided for the arterial traffic.

The invention provides a power system frequency anti-interference regulation and control method and device under multiple attacks of a deterministic network. The method comprises the following steps: acquiring multiple groups of frequency regulation and control related parameters of an area to be controlled; inputting the regional frequency deviation data and the regional power deviation data in each group of frequency regulation related parameters into an adaptive network attack detection system to obtain estimated regional control error data; the adaptive network attack detection system is obtained by training a neural fuzzy system physical model by using pre-collected sample data under multiple network attacks; according to the actual area control error data and the estimated area control error data of the multiple groups of frequency regulation and control related parameters, calculating a root-mean-square error; and according to the root-mean-square error and a preset range, determining a mitigation region control error from the actual region control error and the estimated region error. According to the method, multiple attacks are comprehensively considered, new attacks can be rapidly detected, and the power adjustment efficiency is improved.

The invention provides a partial discharge intelligent identification method and system based on collaborative reasoning, and belongs to the field of partial discharge detection.The method comprises the steps that a semantic structure of high-dimensional PD data is explicitly deconstructed through a multi-scale feature information graph and a sparse spectrum division algorithm, and feature sub-channels strongly related to a discharge mechanism are separated; semantic interference in field data is effectively inhibited; furthermore, a fuzzy modeler induced by a channel structure is adopted to independently learn a local rule set, and a dynamic fusion mechanism driven by cross-channel prediction consistency is combined, so that the tolerance to voltage phase loss, background noise disturbance and equipment isomerism is remarkably improved. Compared with a traditional neural fuzzy model, the method has the advantages that non-exclusive fuzzy classification of an unknown discharge mode is realized while interpretability is maintained, the problem of model failure caused by data distribution offset in field deployment is solved, the recognition error rate is obviously reduced, and a high-robustness solution is provided for intelligent diagnosis of power equipment.

The invention relates to the technical field of performance evaluation of fiber composite yarns, in particular to a comprehensive performance evaluation and intelligent prediction method for multi-component fiber composite yarns. By combining a fuzzy analytic hierarchy process, a fuzzy ideal solution sorting method Fuzzy-AHP-TOPSIS and an adaptive neural fuzzy inference system (ANFIS) algorithm, quantitative evaluation of yarn performance and accurate prediction of key indexes are realized. A Fuzzy-AHP algorithm is utilized to reasonably distribute the weight of each performance index, and a Fuzzy-TOPSIS method is utilized to compare the distance between a sample and an ideal optimal solution and the distance between the sample and a worst solution so as to carry out dynamic sorting. An ANFIS algorithm is adopted to learn a complex nonlinear relation between fiber characteristics and yarn performance, so that a high-precision prediction model is established. The research and development efficiency and scientificity of textile enterprises are improved, an innovative solution is provided for evaluating and predicting the performance of the multi-component fiber composite yarn in an actual scene, and the method has wide application prospects in the fields of textile material research and development and yarn production.

The invention discloses a method for testing the oil content of low-water-content oil field sludge, and relates to the technical field of intelligent oil field environmental protection monitoring, and the method comprises the following steps: collecting a solid slag deposition original frequency signal, combining a frequency drift rate characteristic and a sludge viscosity parameter, constructing a neural fuzzy inference model, and generating a real-time oil content estimated value; calculating a comprehensive dynamic risk quantized value and a solid slag accumulation space gradient vector according to the real-time oil content estimated value, triggering a mechanical arm grabbing instruction, and marking a high-oil-content solid slag sample; carrying out vacuum pressure forming treatment on the marked high-oil-content solid residue sample, calculating a thickness error and a flatness error, and generating a standard detection sample with a flat surface and controllable thickness; a standard detection sample is placed in a quantum-terahertz coupled field, quantum-state multi-source response data are synchronously collected, and accurate oil content is generated through a deformable convolution kernel modulated by fluorescence lifetime. According to the method, the bottleneck of insufficient sensitivity under the condition of low water content is broken through, and high-precision quantitative analysis is realized.

The invention relates to the technical field of milling process risk assessment and parameter optimization, in particular to a deep neural fuzzy reasoning system which comprises a semantic layer, a rule layer and a reasoning layer which are in signal connection in sequence and cooperatively achieve tool risk and quality risk assessment of a milling process. The method specifically comprises the steps that a semantic layer receives milling parameters, and numerical milling parameters are converted into a fuzzy feature matrix through an interval Type-2 Gaussian membership function; according to the method, expert knowledge is embedded through interval Type-2 fuzzy logic to adapt to a small sample milling scene, an IF-THEN activation rule is output to enable a risk decision basis to be traceable, reasoning real-time performance is guaranteed by means of Top-k sparse aggregation, meanwhile, closed-loop self-adaptive adjustment of milling process parameters is supported, the method is suitable for milling scenes of complex parts and novel materials, and the method is suitable for large-scale popularization and application. And the production efficiency can be improved while the machining quality is guaranteed.

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 application provides a vehicle fault diagnosis method and device based on multi-source data fusion and a storage medium. The method comprises: collecting real-time state data and real-time alarm data of a target part, obtaining external environment data and map navigation data; after data preprocessing, the multi-source data set target features are obtained by fusion, the target features extracted from the multi-source data set are input into the adaptive neural fuzzy inference system, the target features are mapped to multiple fuzzy sets, and the member function is used for fuzzification; the rule weight of each target feature is calculated to reflect the fuzzy strength; the neural network learning algorithm is used to train the inference system, the member function and the rule weight are adjusted, the fuzzy output is converted into a specific numerical value or a fault level in the defuzzification process by using the output member function, so that the health state of the part is judged. The application provides a fault diagnosis output with high interpretability, improves the accuracy, and can perform fault diagnosis in a wide range of vehicle operating environments.

This invention aims to provide a smart battery control method for cold chain environments, comprising the following steps: A) Sensor data acquisition, real-time acquisition of ambient temperature T, ambient humidity H, condensate level W, battery voltage U, and battery temperature Tb; B) Data preprocessing; C) Setting preset thresholds, including temperature threshold, humidity threshold, SOC threshold, and insulation resistance threshold; D) Establishing a fuzzy rule base, inputting ambient temperature T, ambient humidity H, condensate level, alarm level, and BMS status into corresponding functions of the fuzzy rule base, outputting various control parameters for control by a controller, while simultaneously providing feedback on the status. An adaptive neural fuzzy control algorithm is used to correct and optimize the above functions in real time, achieving nonlinear dynamic regulation. The method of this invention can effectively solve the technical problems of battery range degradation, high short-circuit risk, and shortened lifespan in cold chain environments characterized by low temperature, high humidity, and easy condensation.

The invention relates to the technical field of crossing of coal mine safety engineering and artificial intelligence, and provides a gas extraction and coal seam anti-reflection intelligent decision-making method based on a three-dimensional geological model, which comprises the following steps: extracting multi-dimensional geological parameters in a preset range of a working face to be mined and carrying out standardization processing to form a structured parameter matrix; inputting the structured parameter matrix into a pre-trained adaptive neural fuzzy inference system model, and outputting an extraction difficulty level score of the to-be-mined working face; according to the extraction difficulty level score, matching at least one gas extraction and coal seam anti-reflection technology combination from a preset structured measure library; and based on a mixed water circulation-moth fire optimization algorithm, engineering parameters in the matched technical combination are optimized, and an optimized gas extraction and coal seam anti-reflection construction scheme is output. According to the method, rapid and accurate optimization of multi-target engineering parameters is realized, and the scientificity and decision-making efficiency of a gas extraction scheme are remarkably improved.

The present invention belongs to the field of intelligent construction and specifically discloses a shield axis deviation prediction model and its construction method, prediction method, and system. The method comprises: training a hybrid model using a training set; the trained hybrid model is the shield axis deviation prediction model; the training set includes splicing parameters and corresponding shield axis deviation data; the shield parameter time series signal is segmented; each obtained signal segment is processed to obtain the splicing parameters; the hybrid model includes an adaptive neural fuzzy inference system (ANFIS) and a fully connected spatiotemporal graph neural network model (FC-STGNN); the ANFIS quantifies the splicing parameters and outputs the parameters; the FC-STGNN performs graph construction and graph convolution based on the splicing parameters and outputs the parameters; and the parameters and the sum are weightedly fused to output the shield axis deviation result. The present invention can improve the accuracy and adaptability of shield axis deviation prediction.

The invention discloses a personalized traffic safety education method, system and method. The method comprises the following steps: collecting and processing learning behavior data of a learner in real time; optimizing fuzzy rules and parameters of the adaptive neural fuzzy inference system by using a particle swarm optimization algorithm, constructing a classification model, and dividing the processed learning behavior data into different types of learners according to the processed learning behavior data; according to the type of the learner, recommending a personalized learning scheme; and executing a personalized learning decision, and implementing pushing and interaction operation of traffic safety education contents. According to the method, intelligent optimization and fuzzy reasoning technologies are fused, analysis and classification are carried out according to behavior characteristics of the learners, accurate learner type division is realized, accurate learning suggestions are provided, the traffic safety education effect is improved, and the problem that a traditional traffic safety education mode is lack of individuation is effectively solved.

The invention relates to the technical field of nonlinear system modeling and intelligent control, and particularly discloses a time delay Wiener system identification method and electronic equipment, and the time delay Wiener system identification method comprises the steps: inputting a separable signal into a Wiener system with time delay, and collecting the input and output data of the system; determining system time delay d by calculating a jump point of a cross-correlation function delta Ryu (tau) of output and input signals; and based on the system time delay d, constructing a linear block identification equation containing transfer function model parameters, and jointly estimating linear module parameters by adopting correlation analysis and a least square method. According to the time delay Wiener system identification method provided by the embodiment of the invention, time delay can be detected through a cross-correlation function difference jump point, rounding errors are avoided, and a four-layer neural fuzzy network is combined with fuzzy reasoning and neural network self-learning ability to adapt to complex nonlinear characteristics; then, particle swarm fast convergence and cuckoo global search capability are fused through an IPSO-CS algorithm, and local optimum is avoided.

The application discloses a valve cooling system main waterway state prediction method based on deep learning, and the method comprises the following steps: building an electric-flow-heat-mass transfer multi-physical field coupling simulation model of the valve cooling system main waterway; driving a deep learning algorithm with simulation data to obtain a deep learning valve cooling system main waterway data prediction model; using the obtained data prediction model to realize large-scale data acquisition, screening key state variables based on a variable control method; proposing a valve cooling system main waterway state evaluation method based on the deep learning model, and realizing full-automatic state division through an adaptive neural fuzzy system. The application can simulate and analyze the distribution of each key parameter under the condition of multi-physical field coupling of the valve cooling system main waterway, and reduce the dimension of the simulation model through the deep learning algorithm, realizes the high-speed of the valve cooling system main waterway state prediction, and judges the working state of the valve cooling system main waterway through the proposed state evaluation criterion.

The invention provides a color box printing self-adaptive drying method based on multi-mode perception, and belongs to the technical field of intelligent manufacturing and printing process control. Multi-modal data in the printing process are collected through a multi-modal sensor; preprocessing the multi-modal feature sequence into a multi-modal feature sequence with consistent space; and inputting the multi-modal feature sequence into the lightweight cross-modal adaptive coding network, obtaining spatial features of each modal through the shallow feature extraction network and performing weighted optimization, capturing a data time evolution rule of each modal through the time dynamic modeling network, and realizing heterogeneous modal fusion and consistency calibration through the modal consistency correction network. And outputting a comprehensive representation vector of the printing ink drying state, inputting the comprehensive representation vector into an adaptive controller, completing feature fuzzification mapping through a Gaussian membership function, and realizing neural fuzzy reasoning based on a fuzzy rule to generate an optimal drying parameter. According to the method, intelligent upgrading is realized on a closed-loop control link of perception, analysis, decision making and correction, and the energy consumption and the rejection rate are remarkably reduced.

The invention provides a weak power grid-oriented doubly-fed wind turbine generator intelligent cooperative control method, which comprises the following steps of: acquiring the actual voltage of a grid-connected point in real time to obtain a voltage deviation index in a power grid operation state, and generating a weak power grid degree index for representing the congenital strength of a power grid based on a short circuit ratio; the weights of the weak grid degree index and the voltage deviation index are flexibly set, and the control priorities of power maximization and voltage stabilization are adaptively switched, so that a corresponding weak grid strength value is generated; and acquiring the rotating speed and the wind speed of a rotor in real time, inputting the rotating speed and the wind speed into a self-adaptive neural fuzzy inference network of the wind turbine generator, and solving the self-adaptive neural fuzzy inference network by utilizing a chaotic Salp group optimization algorithm based on the weak power grid intensity value so as to output an electromagnetic torque reference value and an active power reference value. According to the method, the wind turbine generator can still operate stably under the weak grid condition, and meanwhile, voltage drop of the power grid caused by MPPT output is prevented, so that the stability of the wind turbine generator and the grid connection friend performance are improved.

The application provides a yellow rice wine fermentation prediction method and system based on an ANFIS and a random fractal search algorithm, the method comprising collecting data samples of a pre-fermentation process of different production batches of yellow rice wine; dividing the data samples into a training set and a test set, and performing normalization processing on the data samples; inputting the processed data samples into a multi-output adaptive neural fuzzy inference system model constructed in advance, identifying and optimizing model parameters of the multi-output adaptive neural fuzzy inference system model by using a hierarchical learning random fractal search algorithm, obtaining an optimized multi-output adaptive neural fuzzy inference system model, and predicting a yellow rice wine fermentation state. The application improves the precision and generalization ability of the model, and can achieve good prediction of the fermentation state of different production batches of yellow rice wine.

The application discloses an intelligent excrement sewage treatment system and method, and belongs to the field of sewage treatment.The system comprises a data acquisition module, which is used for collecting COD, ammonia nitrogen, DO, pH, water temperature, SS and microbial activity parameters of sewage in real time, performing normalization treatment on the collected water quality parameters, and forming a unified standard data basis; a first model module, which is based on the data processed by the data acquisition module, uses a particle swarm algorithm to optimize membership function parameters and fuzzy rules of an adaptive neural fuzzy inference system, trains an ANFIS model, outputs a prediction value of COD concentration in a biochemical tank in real time, and forms a COD prediction model; and a second model module, which takes the COD prediction value in the first model module and the data processed by the data acquisition module as inputs, constructs a Stacking integrated model, and contains base learners and meta learners in the integrated model, and then adopts a five-fold cross-validation method to generate a prediction result of the base learners.

The application discloses a kind of crop precision irrigation method and system based on algorithm fusion, it is related to intelligent agriculture and precision irrigation control technical field, method includes: the time series data of meteorology, soil, crop physiology and irrigation process are collected, carry out exception elimination, missing completion and standardization processing and construct derivative feature;The historical characteristic sequence is input to the bidirectional time series network with attention to obtain time series representation, input neural fuzzy inference network to output future water demand prediction value and corresponding prediction interval, obtain confidence from prediction interval;Water demand prediction value, confidence and crop stress are input to the first layer fuzzy inference to obtain basic irrigation duration;Soil water potential and post-irrigation feedback are input to the second layer fuzzy inference to obtain valve fine tuning amount;Fusion generates irrigation instruction and drives execution equipment.By outputting water demand prediction interval and constructing confidence, the prediction reliability is improved, and by double-layer fuzzy inference, soil water potential and post-irrigation feedback are combined to realize executable and safety protection of irrigation instruction.

The invention relates to the technical field of process control, in particular to a chlorinated alkane reaction heat optimization method based on dynamic neural fuzzy control, which comprises the following steps: acquiring a current process state diagram, and generating an error vector by comparing the current state diagram with a reference state diagram; the error vector is input into a dynamic neural fuzzy controller, a depth prediction neural network generates an optimal gain matrix according to spatial features of the depth prediction neural network to realize predictive adjustment, and a fuzzy engine determines basic control intensity according to an intensity trend of the depth prediction neural network to ensure system stability; and the two outputs are combined to generate a final control instruction for adaptive adjustment. The prediction model is trained by a physical simulation model calibrated by experiments. Through cooperation of the prediction model and the fuzzy logic, real-time, accurate and adaptive optimization control of the distributed thermal field is realized.