529985 results about "Process engineering" patented technology

The invention relates to the technical field of twinborn model simulation, and discloses a twinborn model simulation method and system for hot working of large forgings. The method comprises the following steps: collecting and preprocessing process parameters, quality data and environment information in a multi-source manner, and obtaining hot working characteristic data; performing correlation analysis to construct a process knowledge graph; calculating the distribution of a temperature field, a stress field and an organization field by using a self-sensing multi-field coupling var value neural network; comparing and analyzing to obtain deviation data and correction parameters; adjusting a network parameter optimization prediction result; and executing multi-objective optimization calculation, and generating a whole-process technological parameter and a control instruction. According to the method, full-process multi-physics field coupling calculation from smelting, casting, forging and pressing to heat treatment can be achieved, model parameters are dynamically adjusted according to real-time production data, the optimal process scheme is generated, and therefore the manufacturing quality and efficiency of large forgings are improved.

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

In example embodiments, an industrial technology stack for an industrial environment includes a set of computational resources and a set of layers executed by the set of computational resources, the set of layers including a governance layer, an enterprise layer, an offering layer, a transaction layer, an operations layer, a network layer, a data layer, and a resource layer. In example embodiments, the industrial technology stack may include one or more artificial intelligence models for implementing one or more components of one or more layers of the set of layers.

The invention relates to the field of power equipment control data processing, in particular to a boiler combustion optimization control method based on data driving, which comprises the following steps of: acquiring multi-source data such as temperature field distribution, air and smoke pressure, smoke components and coal quality characteristics, and eliminating noise interference by adopting sliding window mean filtering; generating a standardized feature matrix in combination with principal component analysis and a dynamic time warping algorithm; constructing a dynamic coupling model fusing a gradient boosting decision tree and a long short-term memory network, analyzing a nonlinear relationship between pulverized coal particle size distribution and a wind-coal ratio, and predicting combustion efficiency, pollutant concentration and temperature field uniformity; and model parameter self-correction and weight dynamic adjustment are triggered through actual combustion data feedback, and a closed-loop control link is formed. According to the method, accurate modeling of the multi-physical field coupling characteristic of the combustion system is achieved, the time sequence generalization ability under the dynamic working condition is improved, and the purposes of heat efficiency improvement and pollutant emission reduction are effectively balanced.

In an intelligent pollution and carbon reduction method based on combustion control and load distribution, a data processing layer, a multi-unit load distribution and operation optimization layer and a single-unit boiler multi-objective combustion optimization layer are used. The data processing layer, the multi-unit load distribution and operation optimization layer and the single-unit boiler multi-objective combustion optimization layer are embedded in a power plant information system in the form of modules. A load distribution and operation optimization method for a multi-source fuel blending combustion unit with economy as an objective is provided to overcome the operation optimization difficulty of the key production process of a multi-source fuel blending combustion cogeneration unit, such as sludge drying-steam distribution.

According to the server heat dissipation control method, the electronic equipment and the storage medium, heat dissipation control processing is firstly performed on the server according to the operation data of the server and the heat dissipation control algorithm, and then the server subjected to heat dissipation control is evaluated according to the actual heat dissipation requirement of the server, namely the preset heat dissipation condition; and optimizing a heat dissipation control algorithm according to the evaluation result and the operation data of the server after heat dissipation control. Meanwhile, a deep reinforcement learning mechanism is introduced into optimization of a heat dissipation control algorithm, and the balance problems of difficulty in parameter setting and poor system adaptability in traditional heat dissipation control are solved through cooperation of multiple algorithms and combination with edge calculation, namely, preset edge calculation equipment, so that the problem that the system adaptability is poor in the heat dissipation control of the server can be solved. The problems of difficulty in parameter setting and poor system adaptability are solved, and the technical effect of improving the efficiency and the stability of the heat dissipation system is achieved.

The invention relates to the technical field of production line control, and discloses an automatic control method and system for a plastic processing production line. The method comprises the steps of collecting technological parameters of a plastic processing production line and transmitting the technological parameters to a central control system to generate a database; multi-dimensional parameter correlation analysis is executed, a parameter and quality mapping relation is established through a CNN-LSTM hybrid network, and an optimization model is formed; calculating an optimal control parameter, generating a control strategy and issuing the control strategy to an execution unit; and monitoring a response result, updating the model in real time, and forming closed-loop adaptive control. According to the method, the mapping relation between the process parameters and the product quality is accurately established through the deep learning model, the optimal control parameters are automatically calculated, and closed-loop adaptive control based on production feedback is realized.

Devices, methods, and systems for cons traction resource operations are provided. The method includes receiving a first dataset including one or more first data, entries associated with at least a first construction resource and generating a spatio-temporal sequence for the first construction resource. The spatio-temporal sequence defines one or more operations configured to minimize the construction friction with a project associated with the first construction resource. The method includes outputting at least a portion of the data associated with the spatio-temporal sequence. The method may further include determining an interaction associated with the first construction resource and modifying the spatio-temporal sequence in response to the determined interaction.

The invention discloses a clean workshop production environment quality control method and system, and relates to the technical field of environment control, and the method comprises the steps: constructing a multi-layer sensing network, deploying temperature and humidity, particle concentration, pressure difference and VOC gas sensors, and carrying out the preprocessing data of each node through edge calculation; fusing the data based on a dynamic weight distribution algorithm, adjusting the weight according to the confidence score, and generating an environment quality comprehensive index; an LSTM pollution diffusion prediction model is established, a diffusion path is calculated in combination with airflow field simulation when pollution suddenly occurs, and an emergency response partition strategy is generated; fresh air system control parameters are optimized through reinforcement learning, a dynamic ventilation frequency adjusting model is established based on a real-time environment quality index and historical energy consumption data, and energy consumption is minimized through a Q-learning algorithm on the premise that cleanliness is guaranteed; a double-threshold early warning mechanism is set, local supercharging purification is started when a comprehensive index exceeds a first-level threshold, a second-level threshold is linked with adjacent areas to form a dynamic isolation barrier, and an intervention scheme effect is simulated through a digital twin system.

The invention relates to the technical field of electronic manufacturing equipment, in particular to an adaptive thermal compensation system and method for a high-precision mounting head of a chip mounter, and the system comprises a temperature-deformation sensing unit, a thermal-mechanical coupling analysis unit, a dynamic compensation control unit, and a closed-loop execution unit. The temperature-deformation sensing unit collects temperature and deformation data of multiple parts of the mounting head in real time, the thermal-mechanical coupling analysis unit reconstructs a three-dimensional temperature field based on a finite element algorithm, the thermal expansion distribution quantity is dynamically calculated, the problem of rough model of traditional single-point temperature measurement is solved, and the measurement precision is improved. The dynamic compensation control unit predicts the thermal drift amount in the future 5 ms through online parameter identification and a long-short-term memory network model, a compensation strategy is adaptively adjusted in combination with the motion working condition, the closed-loop execution unit decomposes the compensation amount into displacement and torsion correction instructions, accurate offset of thermal deformation is achieved, and a whole-process thermal compensation closed loop is constructed. The precision stability of the mounting head in a complex thermal environment is improved, and the production efficiency is improved.

The invention relates to the technical field of curved surface machining control, and discloses an intelligent curved surface machining method and system based on a CNC cutting machine tool, and the method comprises the steps: carrying out the laser scanning of a to-be-machined workpiece, obtaining the three-dimensional geometric data of the workpiece, activating a target clamping device, and generating an initial clamping scheme; the target clamping device is controlled to execute multi-area pressure gradient clamping operation, and target position data and clamping pressure distribution data are obtained; performing multi-field coupling dynamic analysis to generate a processing optimization parameter set; executing segmented continuous Hamiltonian path planning and self-adaptive lattice point reconstruction according to the machining optimization parameter set, and generating a tool path data set; according to the method, it is ensured that all the surfaces of the polygon prism workpiece are evenly stressed, the problem of machining deviation caused by edge stress concentration in a traditional method is solved, and the overall machining quality of the workpiece is improved.

The invention provides a multi-target combustion optimization method based on economic predictive control, and aims to solve the problems that the power generation load and the in-furnace coal quality are frequently changed, conventional DCS control has defects in the aspects of air quantity and oxygen quantity optimization, boiler efficiency balancing, NOx emission, steam temperature control and the like, part of boiler parameters are difficult to accurately measure, and the existing combustion optimization technology is limited in applicability. The method comprises the following steps: collecting and preprocessing historical operation data, classifying and screening according to steady-state working conditions, and constructing a correlation model to perform preliminary combustion optimization; a load and coal quality change prediction model and a combustion process dynamic relation model are established according to unit characteristics and historical data; then, in the online secondary optimization stage, an initial parameter combination is selected according to the current state and the prediction state, and the combustion parameter adjustment amount meeting the multiple constraints is calculated through an economic prediction control algorithm and applied and corrected. Experiments show that the method can improve the boiler efficiency, reduce NOx emission, optimize steam temperature control, relieve the labor intensity of personnel and effectively improve the combustion performance.

The invention relates to the technical field of automation, in particular to an automatic monitoring and optimizing system for a fine chemical production process, which comprises the following steps of: extracting time-frequency domain fusion characteristic quantity of stirring torque power time sequence fluctuation data in real time through an incidence matrix construction module, dynamically inverting a thixotropic index by combining a deep neural network model, and optimizing the stirring torque power time sequence fluctuation data; the problem that a traditional method is difficult to perceive material rheological characteristics in real time is solved. The dynamic coupling analysis module analyzes the material viscosity change rate based on the thixotropic index, fuzzy PID control is adopted to generate a stirring speed adjusting instruction dynamically matched with the viscosity and a jacket temperature compensation value, and the defects of uneven mixing and local overheating caused by lagging adjustment of process parameters are overcome; the multi-target collaborative optimization module locks the mass optimization weight in the viscosity sudden change stage, rapidly stabilizes the reaction condition through a feed-forward compensation algorithm, dynamically balances the stirring power consumption and the heat transfer efficiency based on Pareto frontier search in the steady state stage, and solves the conflict between the mass and the energy efficiency target.

The invention relates to a lithium battery energy storage system fire-fighting ventilation and explosion venting safety assessment method based on multi-dimensional simulation. The method comprises the following steps: constructing an energy storage system digital twinborn model fusing structure parameters, material attributes and environmental parameters; generating a multi-mode failure scene set covering multiple temperature domains and aging states through mode recognition; simulating and quantifying dynamic interaction of a temperature field, a flow field and a stress field in the thermal runaway evolution process based on thermal-fluid-solid multi-physics field coupling; constructing a space-time associated dynamic safety evaluation matrix, and combining fuzzy comprehensive evaluation and Monte Carlo sampling to generate risk quantitative indexes; and iteratively correcting parameters of the fire-fighting ventilation and explosion venting system through a multi-objective optimization algorithm to form a graded safety assessment conclusion. According to the method, the technical bottlenecks of environmental parameter splitting and single failure scene in a traditional method are broken through, the thermal runaway suppression efficiency is improved, the combustible gas concentration control error is reduced, and collaborative optimization of explosion venting pressure fluctuation suppression and ventilation response is realized through a closed-loop evaluation mechanism.

The invention discloses a sewage treatment dynamic process simulation prediction method and system based on digital twinning, and relates to the field of digital twinning sewage treatment. By introducing a digital twinborn technology and a simulation prediction method based on advanced data analysis, complex dynamic changes in the sewage treatment process can be reflected in real time, and the defects of a traditional simulation technology in the aspects of treatment of nonlinear disturbance, frequent fluctuation and sudden pollution sources are overcome. Through multi-source data acquisition and time sequence alignment, in combination with nonlinear frequency domain disturbance modeling, noise and disturbance in the data can be effectively suppressed, and the quality and reliability of the data are improved, so that a more accurate basis is provided for subsequent modeling and prediction.

The present invention relates to the technical field of carbon footprint calculation. Disclosed are a method and system for calculating a carbon footprint of a high-energy-consuming product on the basis of electro-carbon coupling. The method comprises: collecting data of electricity consumption and data of a carbon emission factor for electricity generation in the production process of a high-energy-consuming product; constructing an electro-carbon coupling model for describing the relationship between electricity consumption and carbon emission, and performing iterative optimization; using the electro-carbon coupling model combined with the collected electricity consumption data and the carbon emission factor for electricity within a corresponding time period to calculate carbon emissions of energy directly consumed for electricity generation during each phase; and analyzing and calculating indirect carbon emissions of raw materials and energy consumed during the other indirect stages in the production of the product, and verifying the result. Thus, the relationship between electricity consumption and carbon emission is more accurately reflected, the impact of time and place changes on carbon emission factors can be taken into consideration, and a method for comprehensively evaluating an overall carbon footprint of a product is provided, thereby facilitating more comprehensive understanding and management of the environmental impact of the product.

The invention provides an intelligent temperature adjusting method and system for an injection molding mold. The method comprises the steps that real-time multi-source sensor data flow is generated by collecting temperature data and pressure data of all areas of the injection molding mold; performing feature extraction on the real-time multi-source sensor data flow to obtain temperature field distribution features; a temperature field distribution prediction model is obtained through training based on the temperature field distribution characteristics and a differential private Bayesian optimization algorithm, and then a mold temperature prediction result is output through the model; performing feature fusion on the mold temperature prediction result and the pressure data, constructing input features of a neural network controller, training based on the input features and a positive definite curvature learning method to obtain the neural network controller, and generating a power control signal of a heater and a flow control instruction of a water cooling system through the neural network controller, and based on the control signal and the instruction, the temperature field of the mold is dynamically controlled through the adjustable clearance compensation mechanism. According to the invention, the precision and response speed of temperature adjustment are improved.

The invention discloses a distributed real-time monitoring and early warning system for a temperature field of a smelting furnace, and relates to the technical field of industrial process intelligent monitoring. The problems of accumulated measurement errors and non-stationary hotspot escape reconstruction hysteresis caused by static emissivity setting in an existing system are solved. Collecting multiband radiation intensity and voltage signals through time domain alignment of the multispectral sensor array and the thermocouple array; iterating emissivity parameters in real time by adopting a dynamic ash body spectrum ratio algorithm in combination with flue gas absorption characteristics; fusing non-contact and contact temperature measurement data based on weighted Kalman filtering and complementary filtering; constructing a space-time variable covariance function to carry out non-stationary Kriging interpolation; dynamically optimizing the local grid resolution by combining an adaptive grid module; the processing flow is accelerated through the parallel computing module; early warning is triggered based on abnormal probability judgment and is fed back to emissivity correction and grid optimization; according to the invention, the monitoring precision and real-time performance of the temperature field are obviously improved, and the risks of false alarm, missing alarm and equipment melting loss are effectively inhibited.

The invention relates to the technical field of image processing, and discloses a dried persimmon quality detection method and system based on machine vision. The method comprises the following steps: performing multi-angle multi-spectral imaging through an industrial camera array to generate a standard image set; extracting quantitative dried persimmon appearance characteristics; the internal quality is subjected to nondestructive testing by using a spectrum; identifying defects and grading; analyzing a quality trend and establishing an early warning mechanism; and correlating process parameters to optimize the production process, and constructing a quality tracing system. According to the invention, comprehensive nondestructive testing of the appearance and internal quality of the dried persimmons is realized, a closed-loop system of quality early warning and process optimization is formed through data analysis and mining, and the quality stability and traceability of dried persimmon production are improved.

The invention is suitable for the technical field of industrial design, and provides a multi-agent cooperative industrial design method and system for complex engineering, and the method comprises the following steps: constructing a large model agent set for a complex engineering design task; dividing a design task into sub-tasks which can be executed by an intelligent agent based on a task decomposition algorithm of a knowledge graph and an intention recognition mechanism; a Prompt-to-Code enhancement model is constructed, and task planning targets and constraints of multiple agents are converted into CAD modeling or CAE simulation design instructions capable of being directly executed by an industrial design tool through Prompt embedding; constructing a design version evolution tree, and designing a causal reasoning graph structure based on the interactive influence of target features; and inputting an asynchronous task planning strategy, a design instruction and a version evolution tree feedback result into a large model agent for iterative adaptive optimization. And the design efficiency and the scheme quality of a complex engineering design task can be obviously improved.

The invention belongs to the technical field of intelligent control of heating, ventilation and air conditioning systems, and particularly relates to an intelligent optimizing and energy-saving control method for a central air conditioner based on deep learning, which comprises the following steps of: acquiring operation data of a central air conditioning system in real time through an internet of things technology; the operation data comprises operation parameters of cold and heat source equipment, flow and lift parameters of a water pump, fan frequency parameters of a cooling tower, temperature and humidity data of an air conditioner terminal, environment temperature and humidity data, weather forecast data and the like. Through deep integration of Internet of Things perception, deep learning prediction and a multi-objective optimization technology, the limitation of a traditional control framework is broken through, meanwhile, accurate prediction of building cooling and heating loads is realized through construction of a hybrid deep learning model, an optimization objective of a full life cycle perspective is established in combination with an equipment performance degradation model, and the system performance is improved. A federal learning framework is innovatively introduced into region-level energy efficiency management, and the model generalization ability is improved on the premise of ensuring data privacy.

The invention provides a cast workpiece three-coordinate structured light rapid measurement system, and relates to the technical field of industrial automatic measurement, and the system comprises a calibration parameter module which is used for calibrating a standard ball, building an initial conversion relation between a structured light coordinate system and a three-coordinate measuring machine world coordinate system, and collecting temperature, humidity and vibration data in real time; dynamically updating the conversion relation to form calibration parameters; and the measurement path planning module is used for dividing the casting workpiece into a plurality of measurement areas based on the calibration parameters, planning a motion path of the three-coordinate measuring machine and driving the motion platform to move to each measurement position. According to the invention, full-process automation from calibration, measurement to modeling analysis of the casting workpiece and the advantage of comprehensive quality analysis are realized, and a reliable three-dimensional measurement and quality control solution is provided for the casting industry.

The invention relates to the technical field of abalone peptide production, and discloses an abalone peptide production process optimization and intelligent decision-making method and system, and the method comprises the steps: collecting production data through a multi-source sensor, and extracting features through a convolutional neural network; the genetic algorithm optimization model is combined with an adaptive mechanism and a multi-target fitness function to generate a process parameter optimization scheme; and the multi-objective production optimization model outputs optimal production process parameters by means of an improved particle swarm algorithm. In the hierarchical intelligent decision-making model, the planning layer realizes global resource allocation, the scheduling layer adjusts a process in real time based on a Bayesian network and the like, and the operation layer controls equipment through fuzzy logic and constructs quality closed-loop control. According to the method, the problems that parameter control depends on experience, data processing is difficult, optimization is one-sided, decision making is not intelligent and the like in traditional production are solved, data can be accurately processed, the process is optimized, decision making is intelligent, production efficiency, the raw material utilization rate and product quality are effectively improved, cost is reduced, and the application prospect is wide.

The invention provides an intelligent high-performance collaborative welding robot system, which belongs to the technical field of welding robots and comprises a multi-degree-of-freedom welding robot body, an intelligent control system, a high-performance welding power supply and auxiliary equipment. A welding process control module firstly obtains three-dimensional information of a workpiece based on visual inspection to construct a welding path; then selecting matched process parameters according to material characteristics and welding seam shapes; establishing a welding error matrix to monitor the welding process in real time; constructing a parameter error adjustment matrix for dynamic adjustment; analyzing parameter fluctuation by adopting a neural network to identify molten pool characteristics; the pre-trained neural network is used for evaluating the welding seam quality; and finally, a multi-robot collaborative welding strategy is integrated to achieve efficient operation, and the technical problem that in the prior art, welding process parameters in a welding robot are difficult to adjust accurately in real time so as to adapt to complex working condition changes is solved.

The invention relates to the technical field of hot working control, and discloses a hot working management and control method, system and device based on digital twinning and a storage medium. The method comprises the steps of obtaining technological parameters and quality data of smelting, casting, forging and pressing and heat treatment procedures through a multi-sensor fusion internet-of-things data acquisition system, constructing a hot working full-process data set and a multi-dimensional technological data platform, achieving parameter mapping and grid conversion through a self-adaptive interface algorithm, establishing a collaborative simulation platform, and obtaining a multi-dimensional technological data platform. And constructing a digital twinborn model and deploying the digital twinborn model to an edge computing gateway to generate a visual management and control system. And calculating a process parameter combination and a resource priority allocation scheme. According to the invention, on the basis of obtaining a large amount of process data in real time, cross-process collaborative optimization and intelligent decision can be realized, especially under the conditions of multi-physics field coupling and complex time-varying characteristics, how to effectively monitor and adjust process parameters in real time and ensure the optimization of product quality and production efficiency.

The invention relates to the technical field of air conditioner energy-saving operation control, and discloses a central air conditioner energy-saving operation control method and system. The method comprises the following steps: acquiring and processing data through a temperature and humidity sensor to obtain a temperature and humidity distribution index; calculating load values according to the indexes and the personnel density to form a distribution diagram; regulating and controlling cold and heat source equipment according to the distribution map to obtain operation parameters; adjusting the water pump fan based on the parameters to form balance data; adjusting the end equipment according to the data to generate a control instruction; and the system state is adjusted in real time according to instructions and comfort feedback to realize energy-saving operation. The problem of energy waste caused by decentralized control of subsystems of a traditional central air conditioning system is solved. By constructing a complete closed-loop control system from environmental perception, load prediction to equipment regulation and control, the operation efficiency of the system under partial load conditions is improved. And meanwhile, refined control is achieved according to different requirements of different areas, and energy consumption is reduced to the maximum extent on the premise that the comfort level of the user is guaranteed.

The invention provides a coal dressing whole process monitoring decision method and system based on Internet of Things sensing, and the method comprises the steps: firstly obtaining a real-time sensing data set of a plurality of monitoring nodes of a coal dressing process, covering equipment vibration, medium density and process flow data, and then carrying out the feature extraction, the method comprises the following steps of: acquiring equipment state, medium dynamic and flow stability characteristics of each monitoring node, performing abnormal correlation analysis on the characteristics based on a pre-trained monitoring decision model, generating abnormal probability distribution and flow adjustment parameters, determining a priority processing queue according to the abnormal probability distribution, and processing the flow according to the priority processing queue. And finally, the coal dressing process optimization strategy is issued to the edge execution terminal, coal dressing process adjustment operation is triggered, effective monitoring and decision optimization of the whole coal dressing process are achieved, and the stability and efficiency of the coal dressing process are improved.

The present invention relates to a method for constructing a digital twin system for an additive manufacturing process with lifecycle data management. The digital twin system for an additive manufacturing process constructed by the method includes a data acquisition and supervisory control system, a data management and storage system, a manufacturing executing system, a configuration model, a simulation system, and a multi-physics simulation system. The present invention can fully and accurately characterize the molding quality of parts, thereby saving a lot of time required for offline data processing, and achieving real-time acquisition and updating of data.

The invention provides a thermal power generating unit performance optimization control system and method, and the system comprises a data collection module which collects various real-time data in the operation process of a thermal power generating unit; the load prediction module is used for establishing a load prediction model and predicting a load change trend by comprehensively considering a historical load condition, a load change rate, a load change acceleration and an external interference factor; the optimization control module is used for dynamically adjusting fuel, an air door and air quantity according to a control strategy in combination with the load prediction result and the unit operation state; the actions of the regulating valve, the air door actuator and the water supply regulating valve are correspondingly controlled; the monitoring feedback module is used for realizing closed-loop control and self-correction; according to the method, a load prediction model based on the combination of time sequence analysis and a physical mechanism and a control strategy comprehensively considering multi-parameter collaborative optimization are utilized, so that quick response to load change, balance consideration of steam quality and combustion efficiency, self-adaptive stable operation under complex working conditions and improvement of overall performance are achieved.

The invention discloses a wind-light-hydrogen multi-energy complementary system cooperative operation optimization and comprehensive benefit evaluation method, which relates to the technical field of electric energy storage, and comprises the following steps: constructing a hybrid power flow model containing electricity, hydrogen and heat coupling, taking whole life cycle cost and new energy consumption as double targets, introducing grid-connected and off-grid risk constraints, and optimizing equipment capacity configuration; a scheduling baseline and an intra-day dynamic correction strategy are formulated in the day-ahead stage, and hybrid energy storage cooperative control is combined to improve the response speed and flexibility of the system to wind and light fluctuation; a peak regulation and frequency modulation mode is switched according to the power grid state, a power response model is established, fluctuation is stabilized through hydrogen energy storage, the stability of the power grid is enhanced, and auxiliary service benefits are created; the performance of the system is evaluated from the three dimensions of efficiency, economy and interactivity, dynamic weight and Monte Carlo simulation optimization are utilized, system parameters are continuously optimized through a feedback mechanism, and the self-evolution ability is formed. The problem that system instability and economy are difficult to take into account due to wind and light fluctuation is solved.