762 results about "Fuzzy rule" patented technology

The invention provides an intelligent incubation bin anti-interference control method and system based on error self-learning, and relates to the technical field of intelligent control, and the method comprises the steps: respectively calculating a temperature deviation value and a humidity deviation value according to an optimization parameter sequence of a temperature prediction error and an optimization parameter sequence of a humidity prediction error; performing normalized weighted summation on the temperature deviation value and the humidity deviation value, and performing dynamic scaling through a Gaussian kernel function to obtain a correction factor; dynamically adjusting the activation function slope of a neural network hidden layer according to the correction factor and a BP neural network, reconstructing the weight of the fuzzy rule base based on the numerical distribution characteristics of the correction factor, and generating the corrected weight of the fuzzy rule base; and based on the corrected fuzzy rule base weight, constructing a three-dimensional parameter adjustment curved surface, and dynamically adjusting proportion, integral and differential parameters of a PID controller through a curved surface gradient search algorithm to generate a control signal. According to the invention, the control accuracy is improved.

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 provides a centralized control automation strategy optimization method and system based on fuzzy logic, and the method comprises the steps: collecting the real-time operation state data of power grid operation equipment in a target centralized control region, covering the operation parameters of a power grid and a parameter fluctuation range, and carrying out the fuzzy membership processing of the data according to the parameter fluctuation range, and generating a fuzzy rule set containing a plurality of fuzzy rules and parameter dynamic adjustment logic corresponding to each fuzzy rule, optimizing a current control strategy by applying fuzzy logic based on the fuzzy rule set, and generating an optimized control strategy containing a control instruction and a trigger condition thereof, and then the strategy is sent to execution equipment according to a trigger condition, execution feedback data is monitored, the validity of the strategy is verified according to a matching result of the execution feedback data and a preset threshold value, the control strategy is dynamically adjusted and optimized, a centralized control automation strategy can be effectively optimized, and the power grid operation control effect is improved.

The present invention relates to the technical field of lithium battery state estimation, and in particular to a fuzzy EKF-AH algorithm-based state of charge (SOC) estimation and correction method for a lithium iron phosphate battery. The method comprises: by taking a certain startup of an energy storage device as a start, a BMS reading an SOC and a state of health (SOH) at the previous shutdown, and on the basis of a standby time, selecting an open circuit voltage (OCV) or an EKF algorithm to correct the SOC; using an EKF-AH algorithm to estimate the SOC, establishing, on the basis of fuzzy control, a fuzzy rule library associated with the SOC and the SOH, and dynamically adjusting a weight and a measurement noise deviation of the EKF-AH algorithm; and calculating estimation differences between the EKF algorithm and an ampere-hour integration method, and if the sum of the estimation differences is greater than a corresponding threshold, issuing an SOH correction warning. The present invention improves the estimation accuracy of the entire life cycle of the lithium iron phosphate battery, and assists the correction of the SOH.

The invention discloses an electric forklift multi-mode driving switching method and system based on multivariable cooperation, and relates to the technical field of industrial vehicle intelligent control. The method is used for solving the problems of one-sided working condition sensing, switching oscillation and safety fault tolerance deficiency in multi-mode driving. The method comprises the following steps: firstly, generating a dynamically weighted working condition feature vector through multi-source sensing data fusion, and quantifying a stability risk, an energy consumption constraint and a power demand; secondly, based on a stability risk trigger steering safety envelope, arbitrating a multi-mode priority and correcting a torque upper limit in combination with a fuzzy rule to form an anti-conflict execution sequence; then, torque distribution and a steering angle acceleration instruction are optimized in a rolling mode in the safe envelope boundary, center-of-gravity shift is synchronously restrained, the track precision is improved, and energy consumption is reduced; finally, the control authority is transferred adaptively according to the operation intensity of a driver, when the decision is abnormal, the load-ramp rule base is degraded, an instruction is transferred through an S-shaped curve, and a torque balancing strategy is executed at the high position of a pallet fork to maintain stable.

The invention discloses an intelligent heat supply two-network balance control method and system based on fuzzy reinforcement learning, and the method comprises the steps: obtaining real-time operation parameters which comprise the return water temperature, the heat load and the environment temperature; based on the real-time operation parameters, preliminarily generating a preliminary adjusting quantity of the opening degree of the valve; operation data are analyzed in real time, fuzzy rule parameters are adjusted according to feedback of the reinforcement learning module, and the fuzzy rule parameters comprise priority weights and membership functions of return water temperature deviation; and according to the preliminary adjustment effect, a deep learning algorithm is utilized to construct a state-action space equation, an energy consumption design reward function is considered, the priority weight is further optimized and fed back to a dynamic optimization module of a fuzzy rule, and secondary adjustment of the valve opening degree is output to an execution mechanism. The problems that a traditional fuzzy control method cannot adapt to complex working condition changes in the operation of the heat supply two-network, the adjusting strategy lags behind, the adjusting effect is not accurate enough, and the adjusting efficiency and energy consumption are affected are solved.

The invention discloses an equipment abnormity monitoring method and system based on the Internet of Things, and relates to the technical field of intelligent operation and maintenance of the Internet of Things, and the method comprises the steps: collecting monitoring data to generate a high-dimensional original matrix, carrying out the optimization through employing a GCN model and combining with ACO, carrying out the updating through a comparison learning model and an FCM algorithm, and carrying out the searching of global optimum through employing a VAE model and combining with a PSO algorithm. The method comprises the steps of performing classification optimization based on K-means clustering and BSO, performing MLE calculation, updating dynamic causal KG through Granger causal test, generating a multi-modal result array through NSM, a scoring formula, a naive Bayesian model, a Mahalanobis distance formula and a logistic regression model, and performing optimization by using a fuzzy rule and GWO. According to the method, the GCN model is combined with the adaptive optimization algorithm, the precision and response speed of anomaly monitoring are improved, optimization is carried out by using the fuzzy rule and introducing the GWO based on multi-modal causal reasoning, and the reliability and efficiency of anomaly monitoring are improved.

The invention discloses a three-temperature test method and device for a semiconductor chip, and relates to the technical field of semiconductor chip testing, and the method comprises the steps: inputting an initialization parameter set into an impedance temperature mapping model, carrying out the phase angle analysis and multi-band feature fusion through a broadband impedance spectrum analysis method, obtaining a chip junction temperature instantaneous value, and carrying out the measurement of the junction temperature instantaneous value. A PID controller is utilized to dynamically compensate a junction temperature instantaneous value of a chip, a fuzzy rule base inference engine is utilized to map the junction temperature instantaneous value into an execution instruction set, the execution instruction set is converted into a bidirectional voltage signal through pulse width modulation, a semiconductor chilling plate is driven to perform bidirectional temperature rise and drop control in a three-temperature environment, and real-time temperature change test data is synchronously acquired. According to the method, the junction temperature instantaneous value of the chip can be more accurately obtained by combining broadband impedance spectroscopy analysis with a multi-band characteristic fusion technology, and meanwhile, the PID controller is combined with the fuzzy rule base inference engine, so that a response can be quickly made under the condition that the temperature is quickly changed, and the response capability is greatly improved.

The invention relates to a city planning checking method and system based on natural language processing, and the method comprises the steps: receiving a user request instruction, carrying out the analysis, and obtaining the checking intention information and target region information; retrieving and matching a corresponding region rule set based on the target region information, and generating a checking task list and a data demand list; obtaining multi-source heterogeneous data and preprocessing the multi-source heterogeneous data to obtain standardized check data and historical fusion features; executing a preset differentiation checking process according to the task type; generating a checking report based on the differentiated checking process result; in conclusion, according to the method, the user request is processed and analyzed through the natural language, the planning knowledge base is constructed, deterministic and fuzzy rule tasks are processed in combination with the differentiated checking process, cooperation of rule terms and case features is achieved, the problems that a traditional checking method is low in automation degree and insufficient in fuzzy term processing capacity are solved, and the efficiency of checking is improved. The method has the effects of improving the checking efficiency and enhancing the interpretability of decision suggestions.

The invention relates to the technical field of battery management, and discloses a battery management system with an active monitoring function, which comprises a processor, a battery state monitoring module, an active equalization control module, a performance evaluation module and a fault early warning module. The battery state monitoring module collects various parameters of a battery at different stages, and generates an abnormal signal by using a dynamic threshold algorithm. The active equalization control module adjusts an equalization strategy based on a fuzzy logic algorithm, and a fuzzy rule base can be updated. And the performance evaluation module tracks and evaluates an equalization process, analyzes equalization efficiency and quality, and optimizes an equalization strategy accordingly. The fault early warning module displays abnormal signals in a grading mode and carries out early warning, and the processor is further connected with the aging feature extraction module and the thermal runaway prediction module to achieve battery life prediction and thermal runaway early warning. According to the invention, comprehensive monitoring, efficient equalization control, accurate performance evaluation and reliable fault early warning of the battery are realized, the safety of the battery is improved, and the service life of the battery is prolonged.

The invention provides an energy storage system power grid allocation method based on a dynamic space-time diagram convolutional neural network. The energy storage management method comprises the following steps: S1, acquiring time sequence data of a power grid node; s2, sequentially executing a graph convolution layer operation and a time convolution operation by using a dynamic space-time graph convolution neural network, and extracting space-time features of the time sequence data; s3, calculating attention weighted features of the spatio-temporal features; s4, processing the new energy output uncertainty based on a fuzzy rule by using the spatial-temporal characteristics and the attention weighted characteristics; s5, realizing distributed model training based on a federated learning framework, and dynamically updating model parameters through an attention mechanism; s6, integrating the geographic positions and the connection relations of the nodes, and outputting enhanced features; and S7, the enhanced features pass through a full connection layer and an activation function, a power grid load prediction result is output, and a charging and discharging strategy of the energy storage system is dynamically regulated and controlled. Power supply and demand dynamic balance in a new energy high-permeability scene is realized, and the operation cost and the environmental influence are reduced.

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 relates to the technical field of automatic control, in particular to a multi-parameter cooperative control method and device for a clamping system of a precision boring and milling machine. According to the method, an industrial camera is used for collecting and processing a workpiece image and extracting visual features, and the visual features are fused with process parameters of a workpiece and geometric distribution parameters of a workpiece key area obtained from a process database; generating a comprehensive state vector; based on the vector, outputting a target clamping force adjustment coefficient by a pre-trained MLP decision model, and calculating a final target clamping force in combination with a safe clamping force range; in the main clamping stage, vibration, temperature and pressure signals are synchronously collected, multi-modal disturbance characteristics are extracted, observation vectors are constructed, the observation vectors are input into a preset fuzzy rule base for reasoning, and compensation activation factors are obtained; and finally, a current correction instruction is dynamically generated according to the clamping force deviation and the compensation factor, and an electro-hydraulic proportional valve is driven to achieve accurate compensation. According to the invention, the stability and the machining reliability of the clamping system are obviously improved.

The invention discloses a multi-drive cooperative control method for high-precision electric drive assembly equipment, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of insufficient cooperative control precision of a multi-axis system under parameter mismatch and disturbance. By constructing a multi-source data fusion and dynamic coupling analysis mechanism, the system collects the motion state and control parameter data of multiple driving shafts, generates a multi-shaft collaborative dynamic response sequence, and calculates the dynamic coupling degree between the shafts to identify the uncertainty mode of the system; positioning a performance degradation shaft section by combining the temperature field and the vibration signal characteristics, and generating a system stability evaluation coefficient; and establishing a space mapping relation to construct a dynamic compensation factor matrix, and generating a control parameter adjusting quantity meeting a collaborative matching condition through fuzzy rule reasoning. According to the method, control instability caused by traditional global adjustment is avoided, precise cooperative control of the multi-axis system is achieved, and the control precision, operation stability and reliability of equipment under the dynamic working condition are remarkably improved.

The invention discloses a parking space recommendation and reservation method, which comprises the steps of normalizing acquired multi-source heterogeneous data to generate parking lot static attribute data, real-time parking space state data and dynamic user request data; constructing an objective basic potential field by using fuzzy reasoning according to a fuzzy rule set capable of performing adaptive evolution based on performance feedback; modeling long and short term preferences of the user by adopting methods such as hierarchical Bayesian and an attention mechanism so as to generate a user subjective preference modulator; an objective potential field, a subjective modulator and a multi-agent rejection potential field with dynamically adjustable parameters are fused, a total potential energy function of the system is constructed, and a collaborative guidance strategy for all users and conflict avoidance is determined and output by solving the minimum value of the function. According to the invention, efficient and personalized parking guidance with macroscopic cooperation capability can be provided, and the user experience and the operation efficiency of the parking lot are significantly improved.

The invention discloses a road risk grading early warning method and system based on a Beidou satellite system, and the method comprises the steps: firstly carrying out the real-time positioning of a vehicle through Beidou dual-frequency signals, inertial navigation data, road side unit differential data and vehicle-mounted sensor data, and obtaining the precise position information; fusing the real-time position of the vehicle with meteorological data, vehicle-mounted OBD parameters and social media public opinion data to generate a dynamic risk factor matrix; a fuzzy rule base is constructed based on expert experience, the fuzzy weight of each risk factor in a matrix is obtained, meanwhile, the time sequence weight of each factor is predicted by means of an LSTM model, and a final road risk score is obtained through dynamic weighting. And performing graded early warning on the road risk in combination with the driver portrait, and feeding back and updating the fusion parameter, the fuzzy rule base or the LSTM model parameter according to the early warning effect to form a closed-loop optimization mechanism. Dynamic coupling analysis of multi-dimensional risk factors is realized, and the real-time performance and accuracy of road risk early warning are improved.

The invention relates to the technical field of environment monitoring, in particular to an intelligent water quality monitoring method and system for collaborative sampling of a bionic fish school and an unmanned aerial vehicle and computer equipment. The method comprises the following steps: dividing monitoring grid units by using GIS data, establishing a layered communication architecture, dynamically deploying a bionic fish school to carry out real-time water quality monitoring, and scheduling and optimizing resource allocation through a fuzzy rule; historical pollution event data and real-time sensing data are integrated to construct a water quality evaluation index system, and a Nash equilibrium strategy is adopted to determine an optimal weight to generate a dynamic pollution situation cloud chart; constructing an inferior fingerprint database to realize rapid identification of pollution events and trigger a two-stage response mechanism; and finally, combining the bionic fish school sampling data and the unmanned aerial vehicle pre-diffusion coordinate to dynamically simulate the effect of the treatment measure. Air-water integrated efficient monitoring is achieved, the accuracy and timeliness of water quality anomaly detection are improved, and comprehensive technical support is provided for water area environment protection.

The invention discloses a power equipment fault diagnosis method based on adaptive fuzzy reasoning. The method comprises the following steps: S1, constructing a multi-source normalized feature vector set; s2, constructing a power system diagram; s3, executing an attention graph convolution operation, and outputting a node embedding state vector set; s4, obtaining an edge synapse updating weight matrix; s5, obtaining a fuzzy rule base; s6, executing a multi-stage fuzzy reasoning process, and outputting a fault diagnosis judgment result; s7, writing a fault diagnosis judgment result into a state memory unit of a corresponding node to form a node state memory record and an edge state evolution record; and S8, when the confidence coefficient of a fault diagnosis judgment result is lower than a set threshold value, calling a node state memory record and an edge state evolution record, executing time sequence sliding window backtracking processing, updating a node embedding state vector set, and repeating the step S6 to execute re-judgment. According to the invention, the graph neural network and adaptive fuzzy reasoning are fused, and intelligent diagnosis of power faults is realized.

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.

A threat behavior detection method based on a fuzzy enhanced polynomial neural network comprises the steps of combination of fuzzy C-means clustering and the polynomial neural network, design of an adaptive optimization mechanism, introduction of an entropy activation function, realization of dynamic edge weighting and fuzzy enhanced feature extraction. And a self-adaptive optimization algorithm for threat behavior detection is designed. According to the method, the efficient threat behavior detection model is constructed by designing the adaptive fuzzy enhanced polynomial neural network, precise recognition of complex threat modes in network traffic is realized, and the detection precision and efficiency are effectively improved. In addition, through generation of the fuzzy rule, visualization of the thinking process of the neural network is realized, and transparency and interpretability of the system are improved. Compared with a traditional method, the method has more advantages in the aspects of detection accuracy and adaptability, the recognition capability on a complex attack mode is improved, and the method is suitable for different network environments and is suitable for threat detection scenes in complex network traffic.

The invention provides a JKW control method based on adaptive fuzzy control, which belongs to the technical field of reactive power compensation of a power system, and comprises the following steps of: interacting reactive power compensation states of JKW devices in adjacent regions based on an edge physical agent, constructing a regional reactive power topological graph in combination with local power grid topological data, generating a regional coordination instruction according to a supply-demand relationship, and establishing a regional coordination graph according to the regional coordination instruction; the method comprises the following steps: acquiring proportion deviation, power factor deviation and reactive power change gradient of a mutual inductor, constructing a standard fuzzy input vector in combination with a regional cooperation instruction, mapping the standard fuzzy input vector into a fuzzy linguistic variable through a dynamically updated fuzzy rule base, and reasoning and outputting an initial switching strategy; and comparing and analyzing the proportion deviation and the power factor deviation through a hardware comparator, determining a trigger condition and existence of a fault, and dynamically optimizing an initial switching strategy by combining a regional reactive topological graph to realize the control of the JKW. And the operation stability of the power grid is obviously improved.

The method comprises the following steps: judging whether each wheel clamping force sensor works normally, if so, directly obtaining a brake clamping force as a final brake clamping force, if not, obtaining a first brake clamping force through a motor model, and if not, obtaining a second brake clamping force through a motor model; second brake clamping force is obtained through the brake friction plate model, and fusion is conducted through the fuzzy logic weight algorithm to obtain final brake clamping force; judging whether the wheel has a locking risk or not, if not, calculating the current ground longitudinal force of the wheel according to a kinetic model of the wheel, if so, triggering longitudinal force locking logic, and acquiring the current locking ground longitudinal force as the current ground longitudinal force of the wheel; calculating the current utilization adhesion coefficient of each wheel in combination with the vertical force; and determining the road surface type of the vehicle by using a fuzzy rule based on the adhesion coefficient of each wheel. According to the invention, the problem of low identification precision is solved.

The invention provides a full-closed-loop control method and system for a multi-station servo feeding manipulator, and the method comprises the steps: firstly, detecting the thickness of a plate in real time, querying a preset thickness-pressure relation, and determining a target pressure value when the multi-station servo feeding manipulator carries out the grabbing of the plate; a real-time pressure value is then generated through a dynamic pressure regulation mechanism. And the system compares the real-time pressure with the target pressure to obtain a deviation value, dynamically adjusts proportional, integral and differential parameters in combination with a fuzzy rule base, and calculates and generates a dynamic pressure compensation amount by using the adjusted parameters. And the compensation amount and the target pressure are superposed to form a comprehensive control instruction, and the comprehensive control instruction is fed back to the execution mechanism to realize closed-loop adjustment, so that the pressure stability and the position fixation of the plate in the whole grabbing and transferring process are finally kept, and the slippage phenomenon is effectively eliminated. The pressure control precision and the anti-sliding stability in the plate transferring process are improved.

The invention discloses a refrigerator defrosting control method based on BP (Back Propagation) neural network and fuzzy control coupling, which is characterized by comprising the following steps: realizing deep perception of dynamic characteristics of a refrigerating system through data-driven modeling: learning actual data signals of a compressor and an evaporator by the BP neural network, and indirectly estimating recessive parameters such as refrigerant charge and frost layer form distribution; and the fuzzy controller dynamically adjusts a defrosting strategy according to a neural network prediction result, and collaborative optimization of refrigeration cycle parameters and the frosting process is achieved. Environment parameters are learned online through a neural network, and the fuzzy rule base is optimized in real time; the neural network directly maps bottom layer physical characteristics such as a compressor operation mode and evaporator structure parameters, and outputs a dynamic compensation signal; and the model is continuously iterated by accumulating historical data to adapt to regional / seasonal differences.

The invention provides a high-performance shield intelligent synchronous grouting control system based on solid waste residue soil resource utilization, a grouting material and a preparation method, and relates to the technical field of shield engineering, the high-performance shield intelligent synchronous grouting control system comprises a parameter obtaining module used for obtaining key parameters, the actual grouting amount and the actual grouting pressure; the error calculation module is used for calculating a grouting amount error and a grouting pressure error; the error fuzzy classification module is used for fuzzy grade classification; the membership calculation module is used for calculating the membership of the grouting amount error and the grouting pressure error to each fuzzy grade category; the fuzzy rule base construction module is used for establishing an initial fuzzy rule base and adjusting and optimizing the initial fuzzy rule base to obtain a final fuzzy rule base; and a grouting process adjusting module. The solid waste muck is used for replacing bentonite and part of sand aggregate, so that the material cost is remarkably reduced, outward transportation and landfill of waste are reduced, and the burden on the environment is reduced.

The invention discloses an electric vehicle charging and discharging method, device and equipment based on fuzzy logic reasoning and price excitation demand response and a medium. The method comprises the steps that modeling is conducted on loss of an energy storage battery of an electric vehicle; in combination with a membership function and a fuzzy rule, based on a Mamdani fuzzy logic algorithm, according to the charging time anxiety degree, the charging cost reduction degree and the residual SOC of the user to which the electric vehicle belongs, determining the intention of the user to participate in charging and discharging scheduling; and in consideration of the intention that the user participates in charge and discharge scheduling, combining a price demand response mode and an excitation demand response mode of the electric vehicle to optimize the operation state of a power grid which is used for charging the electric vehicle. The invention belongs to the field of electric vehicle charging and discharging. According to the method, a corresponding charging and discharging strategy scheme can be accurately formulated according to the requirements of an electric vehicle user, the peak-valley difference of a power grid is reduced while the charging cost and the charging time comprehensive cost of the user are reduced, and the load fluctuation is reduced.

The invention relates to the technical field of power electronics and power system control, and discloses an intelligent control method and system for a multi-network converter, and the method comprises the steps: collecting the frequency deviation, the power change rate and the voltage amplitude of a power grid in real time, carrying out the filtering and feature extraction, and analyzing the coupling relation, thereby obtaining the parameter coupling strength. And if the intensity exceeds the threshold value, an inertia adjusting signal is generated through a fuzzy logic controller. And calculating a deviation correction value by combining the state evaluation response performance of the converter, and generating an optimization instruction by fusing the frequency deviation. And solving a voltage stability threshold based on the instruction, updating a coupling index, and issuing and executing after verification. And finally, updating the fuzzy rule base by adopting neural network iterative learning according to feedback data, and forming an inertia support frame with self-learning capability. According to the method, the inertia response speed and the operation stability of the multi-network converter system under high-proportion new energy access can be effectively improved, and adaptive cooperative control is realized.

The invention discloses an intelligent building fault prediction method based on an adaptive algorithm, and the method comprises the steps: obtaining operation state data collected by a plurality of sensor nodes, carrying out the normalization preprocessing, inputting an improved adaptive prediction model, and obtaining the implicit state representation; an initial fuzzy rule set is generated through fuzzy rule induction, a real-time evolution strategy is introduced, fuzzy rules and neural connection weights are dynamically adjusted according to error feedback, and a self-adaptive prediction model updated through evolution is formed; and then performing multi-step prediction on the key operation parameter time sequence by using the updated model, comparing a prediction result with a dynamic fault threshold value to generate a candidate fault indication, and determining the position and category of a fault to be pre-warned in the building system through time sequence stability verification and space consistency analysis. The method can significantly improve the accuracy and real-time performance of intelligent building fault prediction, and has a good application prospect.