122 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 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 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 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 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 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 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 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.

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.

The present application belongs to the field of comprehensive energy planning, and provides a kind of comprehensive energy system planning robust optimization configuration method and system.The method comprises, obtaining the resource condition and load demand parameter of comprehensive energy system, establish the IES optimization configuration model with the maximum internal rate of return as optimization target;Establish the uncertain set model of wind and light output and cold and heat load demand parameter, based on IGDT combined with IES optimization configuration model, construct IES robust optimization model facing investment income;Simplify IES robust optimization model, solve the maximum uncertainty fluctuation radius and the uncertain scene parameter of the corresponding worst scenario;Under the condition that the uncertain scene parameter is determined, the optimization configuration scheme of maximizing investment income is solved;According to different expected income deviation calculation forms IES optimization configuration scheme set, adopt fuzzy decision, select optimal scheme.Maximize the investment rate of return as optimization configuration target, and based on information gap decision, under the premise of reaching expected income, solve configuration scheme.

The application provides an AC / DC hybrid micro-grid multi-objective scheduling method and system based on digital twinning, relates to the technical field of micro-grid scheduling, and comprises the following steps: establishing an AC / DC hybrid micro-grid digital twinning model and dynamically integrating grid related data; and establishing a comprehensive total cost model of a small AC / DC hybrid micro-grid system; the application combines photovoltaic power generation, photo-thermal power generation and wind power generation, and an energy storage device, establishes a device dynamic efficiency model based on the influence of local load, external grid environment and other factors on the efficiency of the AC / DC hybrid micro-grid equipment, obtains various twinning data such as local load and external grid environment through the digital twinning model, takes economic benefits and the health degree of the energy storage device as the target, proposes a multi-objective particle swarm optimization algorithm based on an improved file maintenance strategy and a fuzzy decision method, and optimizes the output of the energy storage device in real time, thereby improving the multi-energy complementation and conversion capacity of the system.

This invention relates to the field of power load decomposition technology, and discloses a non-intrusive power load decomposition method and system based on multimodal feature learning. The method involves: synchronously collecting power parameter data from smart meters, environmental parameter data from environmental sensors, and user equipment usage behavior data to obtain multimodal load monitoring data; extracting cross-modal features through the non-negative matrix decomposition layer of a first equipment status recognition model to obtain a multimodal fusion feature vector; identifying load patterns through the first decomposition layer of the first equipment status recognition model to obtain a first decomposed load matrix; performing clustering optimization through the second decomposition layer of the first equipment status recognition model to obtain a second load decomposition matrix; and performing dynamic fuzzy decision-making based on the second load decomposition matrix to obtain the equipment operating status identification result. This invention overcomes the limitations of traditional methods that rely solely on a single power signal, achieving high-precision and highly robust non-intrusive power load decomposition.

The invention discloses a small-curve-radius heavy-load steel rail collaborative maintenance method based on abrasion rate dynamic regulation and control, and belongs to the technical field of railway track maintenance. The core of the method lies in that a closed-loop control system with an abrasion rate threshold value (0.5 mm / ten million ton passing total weight) as a decision center is established. According to the method, the side abrasion amount of the steel rail is monitored regularly, the abrasion rate is calculated, a calculation result is compared with a preset threshold value, and a dynamic decision is made and a lubrication strategy is executed; designing an asymmetric polishing target profile based on the lubrication state; performing staged fine grinding; and differential maintenance is carried out in a long-term service period. According to the method, a maintenance decision is upgraded from a fixed period to a precise state response, deep coordination of lubricating and grinding is achieved, the problems that in the prior art, decision basis is fuzzy, and a coordination mechanism is lacked are solved, the service life of the steel rail can be remarkably prolonged, and the maintenance cost of the whole life cycle is reduced.

The application provides a park comprehensive energy optimization scheduling method and system, and relates to the technical field of energy scheduling. The method comprises the following steps: obtaining state information of a park comprehensive energy system; establishing an energy scheduling model according to the state information; determining constraint conditions and an objective function of the energy scheduling model; under the constraint of the constraint conditions, solving the energy scheduling model by an epsilon-constraint algorithm to generate a scheduling solution set, processing the scheduling solution set by a fuzzy decision method to generate an optimal scheduling solution, and taking the optimal scheduling solution as a scheduling scheme to optimize the park comprehensive energy system. The application can improve energy utilization efficiency and enhance park energy security and flexibility.

The present application belongs to the technical field of frequency modulation control, and particularly relates to a heat supply unit integrated auxiliary frequency modulation intelligent control method and system. The method obtains unit and heat supply subsystem operation parameters, combines a seasonal collaborative prediction coupling model with a built-in Bayesian model, obtains a seasonal prediction frequency modulation boundary space and accuracy, generates a seasonal load regulation distribution value based on the prediction result and frequency modulation evaluation rules using a multi-dimensional fuzzy decision optimization algorithm, obtains frequency modulation result deviation and decision success rate through real-time simulation and evaluation, and dynamically feeds back and optimizes the seasonal collaborative prediction coupling model or updates the control strategy list according to the evaluation result. The present application realizes intelligent collaborative control of the unit and the heat supply system, and significantly improves the response accuracy of the AGC load instruction and the system frequency modulation economy.

This invention discloses a method and system for autonomous decision-making control of coal-fired power units under all operating conditions. The method includes: real-time acquisition and intelligent fusion of multi-source heterogeneous data; identification of monitoring blind spots and execution of blind spot data completion or validity verification; construction of an intelligent control system architecture covering all operating conditions of the unit, decentralizing the functions of the plant-level monitoring information system to the production control area; acquisition of electromagnetic radiation interference characteristic parameters and environmental interference characteristic parameters, and implementation of electromagnetic radiation interference suppression or environmental interference correction based on judgment conditions; fusion of a large-scale artificial intelligence model with a dynamic feature decoupling network and a decision knowledge graph to form a quasi-expert fuzzy decision-making system, executing an integrated control strategy of intelligent control, cruise control, and monitoring; and construction of a high-fidelity digital twin parallel system for offline verification and iterative optimization of the control strategy. This invention achieves autonomous decision-making and near-zero human intervention for coal-fired power units under all operating conditions, improving operational safety and economy.

The invention relates to an intelligent decision-making and control method and platform for a multi-energy heterogeneous coupling energy system of a coupling electric power spot market. The method comprises the following steps: step 1, performing multi-source data fusion and multi-time scale prediction; step 2, generating a multi-objective optimization strategy; step 3, performing large model enhanced fuzzy decision; 4, hierarchical real-time coordination control is carried out; and 5, closed-loop feedback iterative optimization is carried out. The platform comprises a data access and prediction module, a multi-objective optimization engine module, a large model decision agent module, a real-time coordination controller module and a feedback learning module. According to the method, a prediction-optimization-decision-control-learning closed-loop mechanism is constructed by utilizing the deep reasoning capability and multi-time scale prediction data of a large model agent and combining real-time information of the power market and the operation state of equipment, so that the limitation of insufficient adaptability of a traditional method to a complex power market environment is effectively solved.

The invention discloses a fuzzy logic-based rolling optimization integrated energy system energy scheduling method, which comprises the following steps of: improving an evolutionary fuzzy reasoning system, and solving a plurality of fuzzy decision parameter sets by a plurality of possible scenes generated in a day-ahead stage; selecting the fuzzy decision parameter set obtained in the day-ahead stage by using a model predictive control framework in the intra-day stage, and making a decision on the current time period through a fuzzy reasoning system corresponding to the selected fuzzy decision parameter set, thereby realizing economic scheduling of the integrated energy system; a rolling optimization energy scheduling method based on fuzzy logic is applied to economic scheduling of the integrated energy system, and the loss of an energy storage unit and other energy costs are fully considered. According to the method, the problem of negative influence caused by prediction error accumulation in the real-time prediction process is effectively solved through fuzzy logic, rolling prediction is performed by using the model prediction framework, the overall operation cost of the integrated energy system is reduced, and economical scheduling of the integrated energy system is realized.

A man-machine collaborative quality detection and early warning system for intelligent factories, comprising an edge visual acquisition terminal, a cognitive anchor generation module and a hybrid intelligent inference engine, the hybrid intelligent inference engine extracts a high-dimensional feature vector of a workpiece image using a residual network and calculates a Mahalanobis distance. When the distance is in a fuzzy decision interval, a man-machine collaboration request is triggered, and a feature heat map is displayed on the terminal. The cognitive anchor generation module receives defect key pixel points labeled by an operator, maps them to a high-dimensional feature space to construct a feature repulsion sphere, and adds the feature repulsion sphere as a regularization term to the loss function of the residual network for online fine-tuning. The scheme corrects the feature boundary in a targeted manner without changing the weight distribution of the model main body, overcomes long-tail defect missed defects, and improves the feature boundary stability of the detection model under continuous production conditions.