154 results about "Fuzzy decision" patented technology

The invention provides an optical storage charging and discharging station aggregation control and optimization method based on a virtual power plant, and aims to solve the problems of multi-target collaborative optimization, dynamic resource response and uncertainty robustness. By introducing a Markov decision process and an adaptive clustering algorithm, the system can dynamically aggregate photovoltaic, energy storage and charging pile resources according to equipment characteristics, and power dispatching is optimized. A multi-objective optimization model is adopted, economical, technical and environmental objectives are combined, a dynamic weight factor is introduced, and optimal scheduling is generated in combination with a fuzzy decision theory. And real-time compensation is carried out by adopting a rolling time domain control framework and deep reinforcement learning, so that the scheduling precision and the response speed are improved. The edge computing and cloud collaboration mechanism reduces the communication load through a lightweight federated learning model, and improves the scheduling response efficiency. According to the invention, the scheduling efficiency of the optical storage charging station can be obviously improved, the operation cost is reduced, the system stability is improved, and the system has good adaptability and expandability.

The invention discloses a numerical control machine tool machining path optimization method based on artificial intelligence. The method comprises the steps that S1, a complete input data set needed by an optimization model is generated; s2, according to the complete input data set, constructing a multi-objective optimization model for numerical control machine tool processing path optimization; s3, forming a fuzzy decision basis of the optimization target; s4, determining an initial processing path optimization solution set; s5, generating an improved optimal solution set of the processing path; and S6, checking the improved optimal solution set of the machining path, checking whether the machining path meets the kinematics limitation of the numerical control machine tool, the precision requirement of the machined workpiece and the process constraint of the dynamic machining environment or not, and generating a machining path instruction of the numerical control machine tool after the machining path is checked to be qualified. According to the method, the integration of checking and instruction generation is realized, and the reliability and accuracy of path execution are effectively improved.

The invention discloses a digital twin-driven bushing operation monitoring and early warning system, which relates to the technical field of digital twin bushing operation monitoring and early warning, and comprises a multi-source acquisition, transmission, analysis, early warning and interaction module, an acquisition module adopts an ad hoc network, a self-powered sensor acquires data in a multi-mode manner, and the sampling frequency is dynamically adjusted. The transmission module guarantees data transmission through LPWAN, satellite communication and quantum encryption, the analysis module builds a multi-scale coupling model to predict the state and the service life, the model can be adaptively adjusted, the early warning module determines the early warning level in combination with a fuzzy decision and a Bayesian network and gives decision suggestions, and the interaction module provides immersive experience and natural interaction based on VR / AR / MR. According to the invention, comprehensive monitoring, stable and safe data transmission, accurate analysis and prediction, scientific and reliable early warning, convenient man-machine interaction, rapid fault diagnosis, cooperation of all modules, real-time and accurate monitoring and early warning, fault reduction, service life prolonging of a sleeve, safety guarantee and benefit and competitiveness improvement are realized through multi-source acquisition.

The invention relates to the technical field of photovoltaic cleaning control, and discloses a spraying cleaning control method of a photovoltaic cleaning unmanned aerial vehicle. The method comprises the following steps: receiving photovoltaic panel surface state data and environmental parameters, comparing dirt grade standards to obtain dirt distribution characteristics and stubborn stain positions, and determining initial spraying parameters through a self-adaptive control algorithm in combination with the environmental parameters; after the spraying executing mechanism is driven to conduct preliminary cleaning, an image collecting device is used for obtaining surface image data, and a deviation value is calculated by comparing a cleanliness threshold value; and in combination with flight attitude data of the unmanned aerial vehicle, a fuzzy decision system is adopted to dynamically adjust initial spraying parameters to generate a real-time control instruction, an execution mechanism is controlled to perform fine cleaning until surface image data accords with a cleanliness threshold, and a final scheme is generated. According to the method, through real-time feedback and dynamic adjustment, spraying parameters are made to adapt to different dirt states and environment conditions, targeted cleaning is achieved, and the intelligence and accuracy of photovoltaic panel cleaning are improved.

The invention relates to the technical field of temperature control, in particular to a temperature controller nonlinear compensation control method and system based on fuzzy logic, and the method comprises the steps: constructing a double-closed-loop control architecture of a temperature control system; main control data is generated through the outer loop fuzzy decision layer, and feedforward compensation and lag compensation processing is carried out on the main control data through the inner loop compensation execution layer to obtain a final control quantity; and adjusting operation parameters of the temperature control system according to the final control quantity. Through a double compensation mechanism of feed-forward compensation and lag compensation, accurate pre-judgment and self-adaptive adjustment of control data are realized, and the problems of control lag and nonlinear instability caused by sensor delay, inertia of an execution mechanism and environmental interference in a temperature control system are systematically solved.

The invention relates to the technical field of power load decomposition, and discloses a non-intrusive power load decomposition method and system based on multi-modal feature learning. The method comprises the following steps: synchronously acquiring electric power parameter data of an intelligent electric meter, environmental parameter data of an environmental sensor and use behavior data of user equipment to obtain multi-modal load monitoring data; performing cross-modal feature extraction through a non-negative matrix factorization layer of the first equipment state recognition model to obtain a multi-modal fusion feature vector; carrying out load mode recognition through a first decomposition layer of the first equipment state recognition model to obtain a first decomposition load matrix; performing clustering optimization through a second decomposition layer of the first equipment state recognition model to obtain a second load decomposition matrix; and executing a dynamic fuzzy decision based on the second load decomposition matrix to obtain an equipment operation state identification result. According to the method, the limitation that a traditional method only depends on a single power signal is broken through, and high-precision and high-robustness non-intrusive power load decomposition is achieved.

The invention discloses a belt type roasting machine operation control method and system. The method comprises the steps that roasting machine operation control parameters including the material level, the fan rotating speed and the air bellow temperature target value are set; collecting the blanking amount, the machine speed, the material level and the temperature measurement values of the roasting section air bellow and the soaking section air bellow in real time; calculating a speed target value based on the blanking amount change rate and the material level error, and dynamically adjusting the speed of the trolley; and a fuzzy control strategy or a regular control strategy is selected according to the blanking fluctuation state, the historical control mode and the fan-speed matching degree. In the fuzzy control mode, slope characteristics are extracted through temperature curve fitting, fuzzy decision is carried out in combination with temperature errors, and the fan rotating speed adjustment amount is output; and in the rule control mode, the fan rotating speed adjustment amount is determined according to a predefined rule table. And finally, the fan target rotating speed is updated according to the fan rotating speed adjustment amount, and the rotating speeds of the main induced draft fan and the backheating fan are adjusted according to the fan target rotating speed. The stability of thermal parameters of the belt type roasting machine can be improved.

The invention relates to the technical field of wireless sensing nodes, and discloses a self-adaptive sleep scheduling method for a wireless sensing node powered by solar energy. According to the method, energy collection data and environmental parameters of a solar cell panel are monitored in real time, an energy prediction model is constructed to generate an energy supply curve, a dynamic sleep time threshold value is calculated according to the energy supply curve and task load priority weight, a multi-stage sleep mode is generated through a fuzzy decision rule, and time-sharing sleep scheduling of communication, sensors and calculation modules is achieved. Meanwhile, a sleep mode can be switched by detecting the emergency degree change of a task queue, an emergency scheduling strategy is started and a standby power supply is activated when energy is insufficient, and data can be recorded to update model parameters to realize self-optimization iteration. According to the method, the energy utilization efficiency and the working stability of the wireless sensing node are improved, and the problems of unstable solar energy supply and unreasonable task scheduling are effectively solved.

The invention provides an AC / DC hybrid microgrid multi-target scheduling method and system based on digital twinning, and relates to the technical field of microgrid scheduling, and the method comprises the following steps: building an AC / DC hybrid microgrid digital twinning model, and dynamically integrating the related data of a power grid; establishing a comprehensive total cost model of the small AC / DC hybrid micro-grid system; according to the method, three power generation modes of photovoltaic power generation, photo-thermal power generation and wind power generation and an energy storage device are combined, and an equipment dynamic efficiency model is established based on the influence of factors such as a local load and an external large power grid environment on the efficiency of AC / DC hybrid micro-grid equipment; various twin data such as a local load and an external large power grid environment are obtained through a digital twin model, a multi-target particle swarm algorithm based on an improved archive maintenance strategy and a fuzzy decision method is proposed with economic benefits and the health degree of the energy storage device as targets, the output condition of the energy storage device is dispatched and optimized in real time, and the output efficiency of the energy storage device is improved. And the multi-energy complementation and conversion capability of the system is improved.

The invention discloses a power system frequency modulation method and system based on flywheel energy storage auxiliary wind power, a flywheel energy storage system is equivalent to a first-order inertia link, an inertia time constant of the flywheel energy storage system is dynamically adjusted, a virtual damping coefficient is optimized in real time in combination with a fuzzy logic controller, and the stability of power grid frequency is remarkably improved. When the frequency of the power grid fluctuates, the flywheel energy storage system can respond quickly, the frequency modulation power close to the rated frequency can be output in an extremely short time, and the steady-state frequency deviation is strictly controlled in an extremely small range. Meanwhile, the fuzzy logic is used for dynamically correcting the power instruction, and the problem of frequency modulation instruction tracking errors caused by wind power output randomness is effectively solved. Besides, after frequency modulation is finished, the damping coefficient is dynamically adjusted through a fuzzy decision mechanism, the flywheel rotating speed recovery time is greatly shortened, secondary frequency disturbance caused by rotating speed lag is avoided, and the recycling efficiency of flywheel energy storage in a continuous frequency modulation scene is ensured.

The invention discloses a multi-target robust model predictive control method for a permanent magnet synchronous motor, which is used for a three-level inverter power supply permanent magnet synchronous motor system with a quadruple BOOST booster circuit, and comprises the following steps: firstly, carrying out system modeling and discretization, then carrying out multi-target cost function design, solving a multi-target optimization problem based on compromise programming and a fuzzy decision method, and finally, carrying out multi-target robust model predictive control on a three-level inverter power supply permanent magnet synchronous motor system with a quadruple BOOST booster circuit. And calculating a delay compensation strategy and a robustness enhancement measure, and finally calculating the output voltage of the quadruple Boost circuit and the output voltage of the inverter. The method improves the DC bus voltage, improves the output power and efficiency, enhances the robustness of the system to parameter change and uncertainty, realizes multi-target optimization control, ensures that the system stably and efficiently operates under complex working conditions, and is suitable for the fields of speed regulation of motors with booster circuits and the like.

The invention relates to the technical field of building engineering, and discloses a building equipment energy consumption data driven self-adaptive energy-saving regulation and control system and method, and the method comprises global state sensing, energy consumption feature extraction, dynamic modeling prediction, multi-objective optimization solution and hierarchical execution control. Time sequence and space data are fused through Kalman filtering, unified modeling of cross-protocol equipment data is achieved, equipment operation, environment, weather and control instructions are integrated according to a space-time state input matrix, a multi-dimensional decision basis is formed, a Pareto optimal solution set is solved based on an NSGA-II algorithm, a compromise solution is selected through a fuzzy decision method, and the optimal solution set is obtained. According to the method, conflict targets of energy consumption, comfort and equipment life are balanced, a priority arbitration problem is solved by adopting dynamic weight allocation, meanwhile, a security fault-tolerant mechanism is established, a mobile security boundary model and a self-adaptive retry mechanism are introduced, an operation threshold is dynamically adjusted, and the robustness of the system is remarkably improved.

The invention relates to the technical field of lower limb rehabilitation training, and discloses an intelligent load lower limb rehabilitation training system fused with a multi-source sensor. The system comprises a multi-source sensing module, a motion intention recognition module, a self-adaptive track generation module, a real-time load regulation and control module and an actuator driving module. The multi-source sensing module synchronously collects plantar pressure, joint angles and electromyographic signals during training of a user through a plantar pressure sensor array, a knee joint angle sensor and a surface electromyographic sensor; the motion intention recognition module extracts time domain and frequency domain features from the multi-source data, and obtains a real-time motion intention through classification; the self-adaptive track generation module generates an exclusive three-dimensional motion track in combination with the real-time intention and historical rehabilitation data of the user; the real-time load regulation and control module generates a self-adaptive load control signal through a fuzzy decision algorithm; the actuator driving module adjusts the magnetic powder brake through PWM current, outputs corresponding real-time resistance torque, and assists in optimizing the rehabilitation training effect.

The invention provides a multi-modal data security analysis technology based on a depth fuzzy wavelet learning model. A deep fuzzy wavelet learning model is widely applied to the field of security analysis due to the self-adaptive capability of the deep fuzzy wavelet learning model in a complex data environment. According to the method, a deep fuzzy wavelet learning framework is improved and is applied to multi-modal data security analysis. According to the model, firstly, multi-modal data (texts, voices, videos and images) are subjected to feature decomposition and noise reduction processing by using a wavelet transform and fuzzy clustering combined method, key semantic, time sequence and spatial features are extracted, and high-dimensional information expression is provided for subsequent safety analysis. Secondly, constructing a deep fuzzy wavelet neural network, inputting the extracted features into an adaptive fuzzy decision layer, and capturing space-time correlation features in combination with a wavelet learning model, thereby improving the perception ability of the model to a complex attack mode; and finally, a security analysis result is enhanced by adopting a robust and spatial search optimization algorithm, so that malicious samples are effectively detected, backdoor attacks are identified, the attack defense capability is improved, and finally accurate multi-modal data security assessment is realized.

The invention relates to the technical field of intelligent window scheduling, in particular to a multi-objective optimization intelligent window dynamic scheduling method and system combined with business rules. The method comprises the following steps: acquiring reservation queuing data, customer demand characteristics and window state data associated with each service window in real time; based on real-time collected data, a multi-objective optimization function including global average waiting time minimization, overall service efficiency maximization of all windows and global resource utilization balance degree maximization is constructed; solving the multi-objective optimization function based on a sorting genetic optimization algorithm; selecting an optimal scheduling strategy through a multi-factor fuzzy decision maker; and dynamically allocating window service resources according to the optimal scheduling strategy, and adjusting a window service queue in real time. By constructing the multi-objective optimization function, the resource waste problems of partial window congestion and partial idle windows caused by traditional fixed rule scheduling are effectively avoided, and the overall rationality and efficiency of window scheduling are improved.

The invention discloses an adaptive modulation coding method and system based on fuzzy logic, and belongs to the field of wireless communication, and the system comprises a dynamic window module which dynamically adjusts the window size of a fuzzy set according to the received signal strength indication and the change trend of the packet reception rate; the input fuzzification module takes the actual received signal strength indication and the packet reception rate as input and converts the actual received signal strength indication and the packet reception rate into fuzzy variables through fuzzification processing; the fuzzy reasoning module is used for defining different membership functions, mapping continuous received signal strength indications and packet reception rate measurement values into a fuzzy set, considering membership value combinations of all RSSI and PRR fuzzy categories, calculating a weighted total score and outputting a fuzzy decision value; and the defuzzification module is used for converting into an actual modulation coding selection scheme through a defuzzification process. By adopting the method and the system, the adaptability and the response speed of the system to complex and changeable channel conditions are improved, and the accuracy and the reliability of adaptive modulation coding selection are improved.

The invention belongs to the technical field of energy storage regulation and control, and particularly relates to an energy storage cluster multi-modal optimization regulation and control method and system based on wide-area measurement data, and the method comprises the steps: collecting the power data of each measurement point, unifying the data of each measurement point to a common time reference system, and then carrying out the preprocessing of the data of each measurement point; aiming at the energy storage system, constructing a ramp rate model, an efficiency characteristic model and a capacity attenuation model, and establishing an energy storage state index system; establishing a multi-mode regulation and control strategy for an energy storage operation state, and dynamically selecting an optimal control mode according to different operation scenes and energy storage states; according to the method, a scheduling framework with day-ahead-intra-day time layering and superior-subordinate space grading is constructed, a day-ahead scheduling model and an intra-day scheduling model are established accordingly, the day-ahead scheduling model and the intra-day scheduling model are rapidly solved based on fuzzy decision, and global optimization of cluster resources is achieved. According to the method, global collaborative optimization of energy storage resources is realized by fusing high-precision wide-area measurement data processing and a multi-mode regulation and control strategy.

The invention discloses a distributed waterlogging and ponding digital map prediction system based on edge calculation, and relates to the technical field of urban flood prevention intelligent decision making. The system comprises a distributed intelligent acquisition module, an edge integrated processing module, a multi-stage collaborative analysis module and a visualization and decision output module. Collecting multi-source heterogeneous data through multiple types of sensing equipment; calling a local digital elevation map slice and a lightweight hydrological model, generating local waterlogging state information and self-calibrating model parameters; key information is screened based on double threshold values of information entropy change and accumulated water depth change rate, multi-monitoring-point data and a drainage pipe network topological relation are fused, and regional-level inland inundation situation analysis and accumulated water and recessive water trend prediction are achieved; and generating and updating a whole-region distributed ponding digital map, and outputting graded early warning and auxiliary decision information. According to the method, distributed acquisition and edge calculation are combined, the monitoring real-time performance and precision are improved, and the problems that traditional monitoring is high in delay, low in precision, fuzzy in decision and the like are effectively solved.

The invention discloses a multi-dimensional comprehensive evaluation method for a highway intelligent capacity expansion control strategy effect. A dynamic analysis database is constructed based on real-time traffic data of a target road section; a dynamic weight adaptive traffic state quantification mechanism is provided, and historical data and real-time fluctuation characteristics are combined to generate a comprehensive congestion index so as to improve scene adaptability of different time periods; designing a capacity expansion strategy simulation framework, optimizing and reconstructing road network capacity parameters through physical lane adjustment and an intelligent system, and generating traffic response data after capacity expansion; a multi-dimensional evaluation model is provided, the traffic efficiency improvement effect, the safety risk coefficient and the environmental influence index are fused, and a three-stage coupling analysis chain is constructed; and dynamically correcting the evaluation weight based on a fuzzy decision algorithm, and outputting the comprehensive optimization priority of the capacity expansion strategy. According to the method, the technical bottlenecks of a traditional method in the aspects of dynamic traffic response, multi-dimensional index collaborative evaluation and real-time data driving are solved, and a scientific and quantifiable evaluation system is provided for intelligent expansion of the expressway.

The invention discloses a multi-mode cooperative control method for transformer area energy storage equipment, and the method comprises the steps: collecting the operation data of a transformer area in real time, so as to recognize the operation mode of the current transformer area in real time. Thirdly, a multi-objective optimization function is created, the minimum transformer area network loss, the minimum voltage deviation, the minimum power consumption cost and the optimal energy storage life loss serve as optimization objectives of the multi-objective optimization function, and then a weight coefficient of the multi-objective optimization function is generated; and then, dynamically selecting a combination of a dominant control mode and an auxiliary control mode by using a fuzzy decision algorithm based on the obtained weight coefficient, generating a multi-mode cooperative control instruction based on the combination of the dominant control mode and the auxiliary control mode, and distributing the charging and discharging power of the energy storage equipment and the reactive output quantity of the converter. Therefore, the control of the energy storage equipment is more adaptive.

The invention relates to the technical field of integrated circuits, and provides an electronic component type selection method and device, electronic equipment and a storage medium. A component parameter library is obtained and multi-dimensional normalization processing is performed on the component parameter library to obtain a standardized parameter tensor, so that principal component analysis is performed on the standardized parameter tensor to obtain a dimensionality reduction feature space, and spectral clustering analysis is performed on the dimensionality reduction feature space to obtain a plurality of functional equivalence classes. And performing reliability degradation modeling on the function equivalence class to obtain an MTBF prediction matrix, performing multi-target optimization on the MTBF prediction matrix to obtain a Pareto leading-edge solution set, and finally performing fuzzy decision on the Pareto leading-edge solution set to obtain a model selection scheme of the electronic component. According to the method, a comprehensive component type selection process is formed through multi-dimensional normalization processing, dimension reduction feature space analysis, spectral clustering analysis, reliability modeling, multi-objective optimization and fuzzy decision, so that components meeting requirements are accurately and efficiently selected.

The invention belongs to the technical field of active power distribution network optimization scheduling, and discloses an active power distribution network day-ahead scheduling method, system and device based on an improved NSGA-II algorithm, and a medium, so as to solve the problem that the existing scheduling method is difficult to consider multiple targets such as operation economy, new energy consumption and voltage safety at the same time. The method comprises the following steps: establishing a day-ahead scheduling model taking minimization of the total operation cost of the power distribution network, minimization of the new energy abandoned electric quantity and minimization of the system voltage deviation as optimization objectives, and setting corresponding power grid operation constraint conditions; carrying out iterative solution on the day-ahead scheduling model by adopting an improved NSGA-II algorithm to obtain a Pareto solution set; and on the basis of the Pareto solution set, a compromise solution is selected through a fuzzy decision method to serve as a day-ahead scheduling scheme. Compared with a traditional single-target or empirical scheduling method, on the premise that the voltage safety is ensured, the system operation cost can be remarkably reduced, and the utilization rate of renewable energy sources can be improved.

The present invention provides a method and system for generating a microgrid energy-saving scheme based on energy storage optimization scheduling, which relates to the technical field of energy storage optimization scheduling, including collecting historical operation data, real-time operation data and external environment data of a microgrid system, and performing multi-scenario prediction through a Monte Carlo simulation method to generate multi-scenario prediction results; based on the multi-scenario prediction results, and using fuzzy decision theory and hierarchical analysis method, selecting an optimal microgrid energy-saving scheme from the solution set; converting the optimal microgrid energy-saving scheme into an intelligent energy-saving operation instruction set, and using blockchain to record the microgrid operation status, energy storage scheduling results and energy transaction information, calculating comprehensive performance indicators based on the data recorded in the blockchain, using a federated learning method to perform distributed evaluation based on the comprehensive performance indicators, and continuously optimizing the microgrid energy-saving scheme according to the evaluation results of the comprehensive performance indicators.

The invention relates to a fuzzy decision-based small spacecraft prediction control method and system. The method comprises the following steps: S1, establishing a tracking spacecraft orbit attitude integrated dynamic model and a target spacecraft dynamic model; s2, on the basis of the established tracking spacecraft orbit attitude integrated dynamic model and the target spacecraft dynamic model, combining a future predicted state with a difference value of an expected position, a speed and an attitude and a control quantity into an optimization problem cost function, and realizing trajectory tracking of the target spacecraft through rolling optimization; and S3, fuzzy logic is added, the relative distance between the tracking spacecraft and the target spacecraft is fuzzified, then the weight matrix parameters of the MPC are adjusted, and trajectory tracking of the target spacecraft is further optimized. According to the method, the weight problem of position, speed and thrust conversion when the spacecraft is switched from a long distance to a short distance is automatically controlled and tracked, so that the safety of the spacecraft is remarkably improved, and meanwhile, an expected control target is achieved.

The invention discloses a multi-attribute decision-making method and system based on normal swing hesitant fuzzy information and a foreground theory. The method comprises the following steps: constructing an initial decision-making matrix and carrying out normalization processing; generating a multi-dimensional hesitant fuzzy decision matrix by calculating the Hamming distance, the chi-square distance and the trigonometric function distance between each scheme and the positive ideal solution, and converting the multi-dimensional hesitant fuzzy decision matrix into a normal swing hesitant fuzzy decision matrix; the comprehensive hesitant fuzzy entropy method and the logarithm percentage change driven target weighting method are fused to determine the comprehensive weight of the index; mapping the negative ideal solution distance into an income value and mapping the positive ideal solution distance into a loss value in combination with a foreground theory, and generating a foreground value decision matrix and normalizing the foreground value decision matrix; and calculating the correlation degree between the scheme and the dynamic ideal solution through a normal swing hesitant fuzzy weighting correlation coefficient, thereby realizing scheme sorting. According to the method, various kinds of uncertainty information and incomplete psychological characteristics of decision makers can be fully considered, and more accurate index weights and decision results are provided.

An electric vehicle regenerative braking control method based on a fuzzy neural network belongs to the technical field of new energy vehicle control, and comprises the following steps: collecting vehicle operation state parameters, and preprocessing input variables; a fuzzy controller is constructed, the vehicle state parameters serve as input, a preliminary regenerative braking target value is output, and fuzzy decision of the braking strength is achieved; a neural network optimization module is designed, fuzzy controller output and vehicle response parameters serve as input, a control result is optimized in real time through an error back propagation algorithm, and dynamic self-adaptive adjustment of regenerative braking force is achieved; according to the optimized control quantity, a motor braking instruction is generated and executed, and needed regenerative braking output is achieved; meanwhile, feedback information is collected and used for training a neural network model, and a closed-loop learning mechanism is formed; and through continuous iterative optimization, comprehensive optimization of regenerative braking control in the aspects of energy recovery efficiency, driving comfort and safety is finally achieved, and a stable and effective regenerative braking adjustment scheme is formed.

The invention provides a park integrated energy optimization scheduling method and system, and relates to the technical field of energy scheduling, and the method comprises the steps: obtaining the state information of a park integrated energy system; establishing an energy scheduling model according to the state information; determining a constraint condition and a target function of the energy scheduling model; solving the energy scheduling model through an epsilon-constraint algorithm under the constraint of a constraint condition by taking the minimization of the objective function as a target, and generating a scheduling solution set; processing the scheduling solution set in a fuzzy decision mode to generate an optimal scheduling solution; and taking the optimal scheduling solution as a scheduling scheme, and carrying out optimal scheduling on the park integrated energy system. The energy utilization efficiency can be improved, and the energy safety and flexibility of the park are enhanced.

The invention discloses a control method and system based on offline fuzzy judgment, and relates to the technical field of electric digital data processing. Comprising the steps of collecting physical quantity data of a target scene through a data collection module; performing integration processing on the collected data according to a preset scene rule to generate prompt words; performing fuzzy decision operation on the cue words by using the off-line large model, and outputting control suggestions; and converting the control suggestions into specific instructions by utilizing semantic analysis and an instruction conversion rule base, executing the specific instructions, feeding back actual state data, and optimizing parameters according to the fed-back data. The problems that in the prior art, data leakage risks exist, multi-source data fusion is difficult, and analysis depends on manual parameters are solved, and the purposes that the control precision is high, the system adaptability is enhanced, stability and independence are improved, and expandability and compatibility are good are achieved.

The invention provides a multi-network collaborative coal mine integrated energy system scheduling method and system under virtual energy storage support, and the method comprises the steps: building a coal mine heat supply network operation model containing multiple heat sources and a coal transportation network operation model, and obtaining a multi-network coupling relation of power supply, heat supply and coal transportation through analysis; constructing a coal mine multi-network collaborative optimization operation model; the method comprises the following steps: analyzing flexible operation characteristics of multiple links of coal mine drainage, coal transportation and heat supply from coal mine production and energy supply link processes, and establishing a multiple virtual energy storage fusion operation model of coal mine drainage, coal transportation and mine heat supply; constructing a multi-objective optimization scheduling model by taking the minimum comprehensive energy consumption cost of the coal mine as an economical objective and the minimum node voltage deviation as a safety objective; and solving the multi-objective optimization scheduling model by using a normalization normal constraint method to obtain a uniform Pareto leading-edge solution, and deciding a compromise solution from the obtained Pareto leading-edge solution through fuzzy decision to obtain an optimal operation scheme of the system.

The invention discloses an automobile low-voltage storage battery intelligent electricity supplement model based on multi-physics field coupling analysis, which mainly realizes accurate state evaluation and dynamic electricity supplement strategy optimization of a low-voltage storage battery by establishing a nonlinear state space equation and a fuzzy decision rule. The system specifically comprises a multi-parameter coupling modeling module used for evaluating the terminal voltage of the storage battery, a battery state evaluation module used for combining the terminal voltage to evaluate the charge state of the storage battery, a fuzzy decision module used for determining the charging voltage and current according to the charge state, and a control module used for determining the charging current and the charging voltage in the charging process through the charging voltage and the charging current. And the self-adaptive correction module is used for identifying the internal resistance of the battery on line and carrying out dynamic voltage compensation. According to the method, all key factors can be comprehensively integrated, accurate estimation of the capacity of the low-voltage storage battery is realized, so that the electricity supplementing accuracy is improved, the service life of the low-voltage storage battery is prolonged while the power consumption is reduced, and the method is suitable for accurate evaluation of the battery state and optimization of the electricity supplementing strategy when the vehicle is in a dynamic working condition.