33 results about "Market response" patented technology

The invention discloses a multi-modal resource collaborative optimization scheduling method, system and device for a virtual power plant, and relates to the related technical field of virtual power plant optimization scheduling, and the method comprises the steps: carrying out the unified modeling and dynamic aggregation of diversified distributed resources in the virtual power plant, and constructing a standardized resource pool; based on the external market and environment information, generating a prediction sequence of various future scenes; forming a state observation space, making a decision by using a deep reinforcement learning agent, and synchronously generating a real-time scheduling instruction and a joint bidding strategy; and executing a scheduling instruction and submitting market bidding, and performing continuous iteration and optimization according to actual market response and environment feedback. The technical problems that in the prior art, multi-modal resource collaboration is insufficient, response evaluation and scheduling are disjointed, and a multi-market collaboration mechanism is lacked are solved, and the technical effects that the collaborative scheduling instruction and the multi-market joint bidding strategy are generated through the deep learning agent, and the economical efficiency, the reliability and the market competitiveness of virtual power plant operation are improved are achieved.

The invention provides a data element value measurement and pricing control method, system and device, and belongs to the technical field of data asset value evaluation and management. Pricing information similar to data product fields is collected; analyzing the data element supermarket, and recording data quality, data volume, data timeliness and data application scene information at the same time; constructing a preliminary pricing model, and performing a simulated pricing test on the fields of the known prices by using the preliminary pricing model; determining a final price of the data product; formulating and executing a data directory putting mode; a putting monitoring mechanism is established, and the page view, the click rate and the inquiry amount of the data catalog are tracked in real time to know market response; setting an order quantity monitoring index and a market comparison analysis dimension; and collecting order quantity data, sorting and analyzing to generate a monitoring report. Through a value measurement and pricing management and control method, data products are priced by comprehensively considering various factors, and reasonable flow and configuration of data elements are promoted.

The invention discloses a coal type sales planning and sales time collaborative management and control method and system based on a coal enterprise, and relates to the technical field of coal sales management. Multi-source data are integrated through technologies such as a data fusion algorithm, the problems of data dispersion and different formats are solved, high-quality data support is provided for follow-up analysis, the sales trend and price fluctuation are accurately predicted by applying a long-short-term memory neural network and a regression analysis model, enterprises are assisted in optimizing sales strategies, the inventory overstock risk is reduced, and the sales quality is improved. By means of linear programming and reinforcement learning algorithms, sales tasks are dynamically adjusted in combination with real-time data, market response speed and sales efficiency are improved, scientificity and flexibility of sales management of coal enterprises are effectively improved, and market competitiveness is enhanced.

The invention discloses a park flexible load aggregation regulation capability quantification and collaborative optimization method and system. The method comprises the following steps: constructing a dynamic coupling feature space of a load group and a multi-element market ecological symbiotic relationship; quantizing the deterministic adjustment capacity, stripping the non-deterministic response spectrum, and quantizing the interaction strength of the physical constraint and the random game to form a reference portrait of the current aggregation adjustment capacity; deducing and ascertaining a performance boundary and an instability critical point through prospective simulation, and generating a dynamic margin map; and when the output dimension of the dynamic margin map is smaller than a preset safety threshold and the adjustment capability toughness is indicated to be insufficient, autonomously deciding a cross-market joint clearing strategy by taking the maximization of the long-term co-evolution value of the park ecology as an optimization target, so as to improve the regulation capability. And extracting load combination and market response in a disturbance scene to reversely reconstruct the dynamic coupling feature space. According to the method, the self-evolution of prospective risk management and decision can be realized, and the long-term collaborative value of the ecology of the park is maximized.

The invention belongs to the technical field of virtual power plants, and relates to a power spot market oriented virtual power plant resource dynamic aggregation method. The method comprises the following steps: extracting a distributed resource feature vector and mapping the distributed resource feature vector into a response type label; determining a price fluctuation period according to a spot price sequence, calculating a time sequence matching degree between each resource and a market rhythm by combining the feature vector, and constructing a comprehensive distance by fusing a clustering weight and a response type label; dividing the resource cluster into dynamic aggregation units by taking the variance of the minimum comprehensive distance as a target; a resource aggregation strategy is generated based on the aggregation unit, a control instruction is issued, and clustering weights and aggregation parameters are iteratively updated according to the clearing deviation rate fed back by market clearing. According to the method, the technical problems that the market dynamics is ignored, the clustering model is easy to merge heterogeneous resources and the closed-loop optimization is lacked in the existing static division are solved, and the effects of improving the market response capability of the virtual power plant, the control consistency of the aggregation unit and the adaptive optimization of continuous transactions are achieved.

The invention discloses a large-scale power dispatching optimization method and system based on agent collaboration, and the method comprises the steps: collecting parameters such as power generation side unit output, power transmission side line transmission, power utilization side load, market side electricity price and the like through an energy Internet intelligent decision platform, and carrying out the dynamic processing of the parameters, and obtaining a power utilization main body market response coefficient; combining the coefficient and a multi-side parameter construction model to determine a multi-agent initial bargaining scheme, inputting the scheme into a reinforcement learning model, and setting a state, action and reward related element training optimization game strategy; and adjusting parameters according to the optimization strategy to generate scheduling plans such as unit start-stop, output distribution and line power flow control, feeding back the scheduling plans to a platform for simulation, outputting indexes such as a network loss rate and a load satisfaction rate, and performing iterative adjustment if a threshold value is not met. According to the system construction method, corresponding functional units operate cooperatively, multi-side parameters can be fully integrated, a closed-loop optimization process is formed, market adaptability and scheduling accuracy are improved, and safe and stable operation of a large-scale power system is guaranteed.

The invention relates to the technical field of power dispatching, in particular to a bidding method, device and equipment for collaboratively participating in a power market based on a double-layer game. Specifically, an upper-layer optimization model and a lower-layer optimization model are constructed; the upper-layer optimization model optimizes day-ahead market bidding and energy storage protocol transaction parameters by taking photovoltaic aggregator expected revenue maximization as a target; and the lower-layer optimization model optimizes charging, discharging and energy states based on an upper-layer result by taking energy storage operator interest arbitrage income maximization as a target. And through iterative optimization to Nash equilibrium, a scheduling plan is determined. According to the method, the problems of limited photovoltaic income and imperfect cooperation mechanism are solved, market rules and physical constraints are considered, the method can be expanded to multi-type resource scenes, and the distributed resource market response and income level is improved.

The invention relates to the field of cross-border e-commerce, and discloses a cross-border e-commerce multi-platform multi-account one-key goods-selling intelligent processing method, which comprises the following steps: S1, inputting goods information and selecting cross-border e-commerce multiple platforms to upload goods; s2, calling an API interface of each e-commerce platform according to the commodity information, and obtaining an API data interface and a platform rule of the platform; and S3, on the basis of the platform rules and the commodity information, constructing a multi-dimensional model for commodity uploading and sales optimization, optimizing a pricing strategy and an inventory scheduling scheme of the commodities on multiple platforms by adopting a nonlinear optimization algorithm, and maximizing the sales potential and the inventory turnover efficiency of the commodities. Through intelligent automatic uploading, real-time data feedback and a multi-dimensional optimization algorithm, collaborative synchronization and dynamic optimization of commodity information, inventory and pricing strategies between cross-border e-commerce platforms are realized, and the operation efficiency and the market response capability are improved.

The invention discloses a machine learning-based cross-border e-commerce self-operation product selection dynamic adjustment method and system, relates to the technical field of e-commerce product selection adjustment, and aims to construct a product selection adsorption quantity model, evaluate the sales attraction of different products in different market situations, analyze the market response condition of each product and improve the product selection efficiency. After the commodities are screened in combination with the output result of the commodity selection adsorption quantity model, the commodities are adaptively adjusted, the racking sequence is determined, the market response condition analysis result is sent to the dynamic optimization module, and based on the market response condition analysis result of the commodities, the discount strength of the commodities is dynamically adjusted by simulating the market dynamic state; according to the analysis of commodity sales data and market feedback, the shelf sequence and marketing promotion strategy of commodities are optimized. According to the adjustment method, through introduction of a machine learning technology and combination of a data driving mode, commodity sales data and market environment changes can be analyzed from multiple dimensions, and commodity selection and adjustment efficiency of a cross-border e-commerce platform is significantly improved.

The invention provides a generator set scheduling method and system in an electricity-carbon market, and the method comprises the steps: obtaining a market response model, a current decision, and a current adjustment range based on the data of the electricity-carbon market and a historical optimal reference point; based on the current decision and the current adjustment range, executing an accelerated solution step to obtain an optimal decision; based on the optimal decision, scheduling the target generator set to obtain a clearing result; the acceleration solving step comprises the following steps: based on a market response model, obtaining trust region sub-problems in a current decision and a current adjustment range, and then carrying out dimensionality reduction solving to obtain a decision adjustment step length; obtaining an update decision amount and an update adjustment range based on the decision adjustment step length; if the updating decision gradient vector is greater than the preset convergence threshold value, executing an accelerated solution step based on the updating decision and the updating adjustment range; otherwise, the accelerated solution is terminated, and the optimal decision is obtained. The scheduling efficiency of the generator set can be improved.

The invention relates to the technical field of cross-border trade process optimization, and discloses a cross-border trade process optimization method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining cargo information and customs clearance information of a to-be-cross-border trade; generating a plurality of preset dimensions based on the cargo information and the customs clearance information; collecting a dimension value corresponding to the preset dimension from a preset knowledge graph to form a state space; wherein the state space is a space corresponding to all preset dimensions; and performing strategy optimization on the state space through a preset learning algorithm to obtain an optimized customs clearance process. The method has the advantages that the cross-border trade process is more flexible and higher in adaptability, the overall operation efficiency can be improved, the customs clearance time can be shortened, an enterprise can optimize resource configuration, the operation cost is reduced, and finally the overall market response ability and customer satisfaction are improved.

The invention discloses an industrial and commercial power consumer energy control method, and the method comprises the steps: building a day-before-day multi-time scale collaborative optimization framework, enabling a declaration electricity price function of each time period of a power spot market to be embedded into a core variable of an optimization target, enabling an energy storage charging and discharging strategy to actively respond to the real-time electricity price fluctuation instead of a fixed peak-valley time period, and enabling the energy storage to be more stable. The mode upgrading from traditional peak-valley arbitrage to spot price arbitrage is realized; an intra-day electric charge model which is periodically executed in a rolling manner in a day is operated, actual settlement data of an executed time period is locked, only an unexecuted time period is optimized, a prediction error cross-cycle cumulative effect is eliminated, and an industrial control system with real-time market response capability is formed. The technical problem that a traditional energy management system cannot participate in spot market dynamic optimization is solved.

The invention discloses an electricity transaction agent market prediction method and system based on graph deep learning, and relates to the technical field of electricity transaction, and the method comprises the steps: constructing an electricity market transaction graph comprising a node set and an edge set; performing node and structural feature coding on the electricity market transaction graph, and extracting global embedded representation of the market; and modeling market subjects corresponding to the nodes into power transaction agents, and executing multi-subject strategy interaction learning among the power transaction agents by using global embedded representation and a historical strategy sequence of the power transaction agents. Balance evolution is carried out in the graph reinforcement learning process to dynamically update the strategy weight and the market response threshold value of the power transaction agent; and performing behavior simulation deduction by using the updated power transaction agent to establish a time sequence prediction result. The technical problem that the market price fluctuation prediction precision is insufficient due to the fact that the electricity market transaction relation is complex and the subject behavior is difficult to accurately model in the prior art is solved, and the technical effect of improving the market prediction precision is achieved.

The invention belongs to the technical field of tourism e-commerce, and particularly relates to an artificial intelligence-based tourism e-commerce big data mining method, which comprises the following steps of: obtaining user behaviors, cross-platform product supply, historical transaction characteristics and social public opinion emotion, and constructing a tourism interest dynamic evolution graph; quantizing a time sequence drift and community diffusion rule of user interests through a time sequence diagram neural network model, constructing a supply-demand matching degree prediction model based on cross-platform product supply and user behavior data, and combining with the updated evolution graph and through a generative adversarial network to obtain a supply-demand matching degree prediction model. And generating a personalized tourism product recommendation list and a dynamic pricing strategy, carrying out intelligent diversion and resource pre-distribution, synchronously collecting user interaction feedback and market conversion efficiency data, and through an online incremental learning algorithm, optimizing a time sequence diagram neural network model, and generating a tourism demand mining and market response evaluation report. Therefore, the problems of insufficient user interest dynamic feature capture, weak user demand mining ability and the like in the prior art are solved.

The invention relates to the technical field of marketing platform management, and discloses a commodity management system of a network marketing platform, which comprises a multi-source information fusion module, a dynamic attribute tag module, an association recommendation module, an inventory and market linkage module and a feedback optimization module. Through cooperation of multi-source information fusion and a dynamic attribute label system, full-dimension integration and real-time updating of commodity information are realized, limitation of single environmental protection attribute or user behavior analysis is broken through, commodity cross-sales efficiency is improved through scene-based association recommendation, and commodity cross-sales efficiency is improved. Static attribute presentation of commodity management is upgraded to dynamic market response, and the control ability and marketing accuracy of the platform for the whole view of the commodity market are remarkably enhanced.

The present invention is configured to instruct the at least one processor to perform at least one operation, where the at least one operation may include: providing advertisement data corresponding to each of a plurality of minimum feasible products (MVPs) corresponding to a business model to a social networking service platform server by connecting with an external social networking service platform server; market response data corresponding to the advertisement data are collected from the social network service platform server; based on the collected market response data, using generative artificial intelligence to generate market test result data of a minimum feasible product corresponding to the advertisement data; selecting at least one of the plurality of minimum feasible products based on the marketability test result data; automatically generating a market verification route map of the minimum feasible product based on the market test result data of the at least one minimum feasible product; and providing the marketability verification route map to a client terminal.

The invention relates to the technical field of factory management systems, and particularly discloses a feed factory production management system, which comprises a full-process multi-dimensional data acquisition module, a full-process digital twinning construction module, a multi-target comprehensive efficiency evaluation module, a dynamic closed-loop optimization decision module, a full-chain quality tracing module and an equipment intelligent operation and maintenance module. According to the system, the transformation of feed production from traditional experience driving to data intelligent driving is realized through fusion application of technologies such as digital twinning, Internet of Things and big data analysis; due to full-process digital management and control, the production efficiency is improved, the energy consumption and the operation and maintenance cost are reduced, and the product quality stability and the market response capability are enhanced; meanwhile, production data accumulated by the system can be further used for process optimization, equipment improvement, market demand prediction and the like, data support is provided for long-term development of enterprises, feed factories are assisted to occupy advantages in large-scale and intensive competition, and the overall core competitiveness is improved.

The invention discloses a product customization method, platform, system and device, a storage medium and a program product, the product customization method is applied to an online customization sales platform, and the product customization method comprises the following steps: the online customization sales platform obtains and loads a product processing image and product processing information; constructing a product model according to the product processing image and the product processing information; and carrying out analogue simulation on the constructed product model to obtain an optimized product model. Product customization and sales are achieved through the online customization sales platform, and the problems that due to the fact that product customization cannot be achieved, the inventory cost is high, the cost is difficult to control, the market response is slow, the production efficiency is low, and the production period is long are solved.

The invention discloses a personalized credit product intelligent design system and method based on generative AI, and relates to the technical field of financial credit product design. The system comprises a data acquisition and processing module, a generative AI product design module, a market response simulation module, a default probability prediction module and a product scheme output module. Intelligent integration of multi-source data is realized through a dynamic weight fusion algorithm based on a Bayesian network; generating a personalized credit product scheme by adopting a conditional generative adversarial network and a variational automatic encoder; constructing a three-layer coupling market simulation model to predict market response; accurately predicting the default probability by using a time-varying t-Copula model and Monte Carlo simulation; and outputting an optimal scheme through a multi-objective optimization algorithm. According to the method, the data accuracy is improved to 98.7%, the product design efficiency is improved by 99.9%, the default prediction KS value reaches 0.75, the transformation of credit products from template design to intelligent and personalized design is realized, and a complete technical solution is provided for digital transformation of financial institutions.

The invention provides a business decision-making method and equipment for performing beauty makeup business multi-mode price comparison based on historical prices. The method comprises the following steps: collecting and preprocessing product historical prices, attributes and market trend data; and constructing a multi-modal price comparison model, and performing time sequence analysis by using the data to predict the price trend. And identifying similar products, comparing prices by using the model, and generating a report. And evaluating suppliers, and formulating a purchasing strategy in combination with a service strategy. And formulating a sales strategy according to the market trend and the price comparison report. And performing inventory management according to the price trend and the sales strategy. According to the method, multi-source data are integrated, and decision comprehensiveness and accuracy are improved. The model is combined with a time sequence and deep learning, the prediction precision is improved, the decision risk is reduced, the full-chain strategy is optimized, and the market response and operation efficiency are improved.

The invention discloses a panoramic digital operation management system and method for enterprise operation management, and particularly relates to the technical field of operation management, and the system comprises a database module, an enterprise operation data collection module, a market response module, a strategic decision module, an execution module, an execution monitoring module, and a continuous optimization module. According to the invention, by collecting enterprise operation data, calculating market demand indexes and matching market demand commands, accurate market dynamic information is provided for enterprises; a decision scheme is made based on the market demand command, and a decision execution effect is tracked through an execution monitoring module, a risk index is calculated, and potential problems in operation are identified; according to the risk level, the continuous optimization module automatically generates an intelligent optimization scheme through a machine learning algorithm, and feeds back the intelligent optimization scheme to the decision module to promote continuous optimization of the decision; according to the method, the intelligence and refinement level of enterprise operation is improved, and the problems of response lag, low efficiency, inaccurate decision and the like in the prior art are solved.

The invention belongs to the technical field of integrated energy system scheduling, and particularly relates to a user privacy protection-oriented integrated energy system game and deep reinforcement learning collaborative optimization scheduling method and a controller. Comprising the following steps: constructing a user privacy protection-oriented multi-agent game model; constructing a strategy optimization model based on deep reinforcement learning; and based on the multi-agent game model and the strategy optimization model, three-stage collaborative scheduling is executed, and the three stages comprise a bidding stage, a rescheduling stage and a decomposition stage, so that full-process collaborative control of flexible resources from market response to equipment execution is realized. According to the method, the privacy protection game and the deep reinforcement learning are fused, so that the distributed collaborative optimization scheduling of the integrated energy system in the information asymmetric environment is realized.

The invention discloses a flow operation tuning method based on AI automation, and relates to the technical field of artificial intelligence and electronic commerce, and the method comprises the steps: building a dynamic pricing model based on user features, carrying out the scene recognition through employing the dynamic pricing model, and adjusting the price of a product according to a scene recognition result. According to the adjusted product price, defining a multi-target reinforcement learning environment; based on a multi-target reinforcement learning environment, constructing a multi-target balance decision model, and selecting a product optimal price adjustment strategy; and based on the product optimal price adjustment strategy, identifying the dynamic association between the user characteristics and the price sensitivity, and generating and executing a flow regulation and control instruction. According to the method, the analysis accuracy is improved through data preprocessing, the market response capability is enhanced through scene recognition and price adjustment, a multi-target reinforcement learning environment is finally defined to realize intelligent decision making, automatic adjustment and optimization of flow operation are realized, and the operation efficiency, the user experience and the enterprise income are improved.

The invention relates to a science and technology enterprise research and development vitality excitation method based on a dual-drive mechanism. The method comprises the steps that firstly, internal innovation power data and external market feedback signals are obtained, the innovation activation insufficiency degree is calculated after fusion preprocessing, and if the innovation activation insufficiency degree exceeds a threshold value, trigger condition features are extracted to generate dynamic adaptation requirements; determining a resource allocation optimization direction through a clustering algorithm, constructing a demand and market response mapping relation, analyzing a demand disjunction risk in combination with an internal innovation dynamic index, and determining an activation mechanism adjustment scheme if the demand disjunction risk exceeds a threshold value; environment signals are screened to obtain a collaborative problem relieving path, and indexes and optimization directions are integrated to form a dynamic adaptation framework; market feedback update data is obtained based on the framework, a correction effect is verified through a decision tree, model parameters are adjusted when necessary, and finally a research and development mechanism optimization result is obtained. According to the method, the research and development optimization direction is accurately positioned, resource and demand matching can be guaranteed, the research and development deviation risk is reduced, and the research and development vitality and core competitiveness of enterprises are stably improved.

The invention discloses a steel grain slag market prediction system. The system comprises the following related systems and models: a global intelligent sensing system, a space-time big data analysis system, an integrated learning model, a knowledge graph and causal reasoning module, a meta-learning and adaptive adjustment module and a supply chain intelligent decision module. The beneficial effects are that the method collects multi-source data through a global intelligent sensing technology, captures the space-time correlation between market demands and production conditions through a space-time big data analysis technology, carries out the fusion analysis of the data through an integrated learning model, forms an accurate market prediction model, combines a knowledge graph and a causal reasoning technology, and achieves the real-time prediction of the market demands. The influence of external factors on the market is analyzed, the prediction result is further optimized, the meta-learning and evolutionary neural network technology is adopted, the prediction model is adaptively adjusted, the prediction precision is improved, dynamic scheduling and optimization of the steel granulated slag supply chain are achieved through an intelligent decision-making system, and the resource allocation efficiency and the market response speed are improved.

The invention belongs to the fields of artificial intelligence, federal learning, brain-like calculation, supply chain optimization and international trade digitalization, and aims to solve the problems of demand information lag, high customer acquisition cost, slow market response, data islands, difficulty in privacy compliance, low supply and demand matching precision, lack of continuous learning mechanism and the like of Chinese industrial product enterprises. And proposing a implicit demand perception and adaptive supply chain optimization system based on federal brain learning. According to the system, privacy protection is embedded in perception, learning and transmission processes of federal edge nodes, and a safety aggregation mechanism of a central core, a brain-like heuristic learning method and a vertical large model analysis capability are combined, so that collaborative perception and intelligent optimization of hidden demand intentions of global industrial products are realized, and the problem of data privacy compliance is thoroughly solved; and the sea-going efficiency and success rate of Chinese enterprises are obviously improved.

The invention relates to the technical field of pastry conveying, and provides a pastry processing and conveying mechanism which comprises a left conveying channel and a right conveying channel installed on one side of the left conveying channel. A track changing mechanism is arranged between the left conveying track and the right conveying track, a limiting structure is installed at one end of the track changing mechanism, and a single-side conveying track is installed at one end of the left conveying track and one end of the right conveying track. Through the design of the track changing mechanism, pastries can be automatically and accurately changed from the left side conveying track to the right side conveying track or vice versa under the conditions that the physical layout of a production line is not changed and manual intervention is not needed, and the defect that a process route is solidified is effectively overcome; when different pastries need to be combined with processing procedures on another line, the mechanism can realize quick switching and endow a production line with high process recombination capability, so that a single production line can flexibly meet the production requirements of multiple varieties and customization, and the market response speed is remarkably increased.

The application discloses a coal type sales planning and sales time collaborative control method and system based on a coal enterprise, and relates to the technical field of coal sales management.The application integrates multi-source data through a data fusion algorithm and other technical means, solves the problems of data dispersion and different formats, provides high-quality data support for subsequent analysis, accurately predicts sales trends and price fluctuations by using a long short-term memory neural network and a regression analysis model, helps enterprises optimize sales strategies, reduces inventory backlog risks, dynamically adjusts sales tasks by means of linear programming and reinforcement learning algorithms in combination with real-time data, improves market response speed and sales efficiency, and effectively improves the scientific nature and flexibility of coal enterprise sales management and enhances market competitiveness.

The invention relates to the technical field of resource data processing, in particular to an enterprise resource plan analysis and management system, which comprises a data acquisition module used for acquiring resource management data; the data extraction module is used for constructing a fusion feature matrix for the resource management data, extracting key feature information in the fusion feature matrix and constructing the key feature information into a deep feature vector; and the data processing module constructs a market demand prediction model based on the deep feature vectors to obtain a market demand change trend. According to the method, real-time dynamic matching of production, supply chains and market demands is realized by predicting the market demands, excessive production when the market demands are low is avoided, and capacity distribution is adjusted in time when the market demands are sharply increased, so that the market response speed is increased; in addition, by optimizing a production plan and a switching sequence and reasonably arranging time, resources and production lines, enterprise profits are improved to the maximum extent, and product consistency and quality are ensured through accurate switching and a stable production process.

The application discloses a park flexible load aggregation regulation capacity quantification and collaborative optimization method and system, and the method comprises the following steps: constructing a dynamic coupling characteristic space of a load group and a multi-element market ecological symbiosis relationship; quantifying a deterministic regulation capacity and stripping a non-deterministic response spectrum, quantifying the interaction intensity of physical constraints and stochastic games, and forming a benchmark image of current aggregation regulation capacity; deriving and proving a performance boundary and an instability critical point through forward-looking simulation, and generating a dynamic margin map; when the output dimension of the dynamic margin map is less than a preset safety threshold, indicating that the regulation capacity is insufficient in flexibility, taking the maximization of the long-term collaborative evolution value of the park ecology as an optimization target, autonomously deciding a cross-market joint clearing strategy, and extracting a load combination and a market response under a disturbance scenario to reversely reconstruct the dynamic coupling characteristic space. The application can realize self-evolution of forward-looking risk management and decision-making, and maximize the long-term collaborative value of the park ecology.