144 results about "Market dynamics" patented technology

A system and method for multi-scale temporal attention processing in multimodal technology deep learning systems. This system processes time-series, textual, sentiment, and structured tabular data across three hierarchically-organized temporal streams—quarterly, weekly, and intraday levels—with bidirectional cross-temporal information flow. Scale-specific attention mechanisms are optimized for respective temporal granularities, while an adaptive controller dynamically weights each temporal level based on real-time market volatility indicators. A multi-scale fusion processor integrates attention-weighted representations to generate temporally unified representations preserving both short-term market dynamics and long-term trends. This approach enables superior forecasting and risk assessment by leveraging temporal correlations across multiple time scales while automatically adapting to changing market conditions. The system facilitates interpretable AI analysis through attention visualization and enables synthetic scenario generation for model testing.

The invention relates to the technical field of asset transaction risk monitoring, and proposes an asset transaction risk monitoring system based on data analysis, and the system comprises a data obtaining module which is used for obtaining and integrating multi-source heterogeneous data, including but not limited to transaction records, market quotation data, user behavior data, legal contract texts and financial indexes, the data is preprocessed; and the risk assessment module is connected with the data acquisition module through a data transmission technology, and assesses the risk in real time based on a preset rule and a machine learning model. Through timely acquisition and analysis of latest transaction information, market dynamics and user behavior changes, a reinforcement learning technology is utilized, and according to a real-time risk assessment result and a disposal effect, risk monitoring is ensured to be always matched with a market actual condition, so that a financial institution can timely capture a risk signal and take precautionary measures in advance, and the risk monitoring efficiency is improved. And the risk loss is effectively reduced.

The invention provides a supply chain collaborative management method and system based on artificial intelligence, relates to the technical field of artificial intelligence, and quantifies collaborative, substitution and complementary relationship strength among commodities by constructing a commodity association graph network; utilizing a graph attention mechanism to identify a commodity group and calculate a demand influence coefficient, and performing time sequence analysis on historical order data to obtain basic demand prediction; meanwhile, a scene recognition model is constructed by adopting reinforcement learning, a basic prediction result is adjusted according to a current market scene, the demand conduction quantity in the commodity group is calculated in combination with a demand influence coefficient, and final demand prediction is obtained; and finally, based on a prediction result, determining a collaborative decision-making scheme of each participant by applying a cooperative game method, and generating an inventory allocation instruction. According to the method, the complex incidence relation between the commodities and the market dynamic change are accurately captured, the supply chain prediction accuracy and the cooperation efficiency are remarkably improved, and inventory optimization and cost reduction are realized.

The invention provides an inventory management method for e-commerce, which integrates links of multi-source data analysis and prediction, intelligent replenishment, dynamic inventory tracking and return management, constructs a demand prediction model by using historical sales records, user behaviors and market dynamic information, sets a safe inventory level based on a prediction result, and provides an inventory management method for e-commerce. An automatic purchase request and a quick response replenishment mechanism are implemented, real-time inventory synchronization is achieved through an API, inventory changes are monitored through the Internet of Things technology, a commodity processing decision is guided for a returned commodity development prediction model, unsalable commodities are regularly examined, products close to the expiration date are reasonably handled, and a KPI is set and comprises the inventory turnover rate and the order accuracy rate. And closed-loop control of inventory management is realized.

The invention discloses a public opinion risk dynamic early-warning method and system based on a large language model, and the method and system carry out the semantic recognition, event extraction and situational reasoning of unstructured public opinion data through introducing an advanced large language model, and carry out the dynamic early-warning of the public opinion risk in combination with structured emotion indexes and market dynamics. Potential risk factors in social public opinions can be automatically identified, and risk quantitative evaluation is realized in combination with semantic tags, market behavior data and emotion intensity. The system has strong prospective perception ability and event evolution reasoning ability, can provide rapid and accurate early warning information, and further can generate personalized suggestions and decisions in combination with user risk preferences. The method can be widely applied to a plurality of public opinion sensitive fields such as enterprise brand management, government supervision, financial institutions and media transmission control, and the automation and precision capability of dealing with sudden public opinion risks is effectively improved.

An AI-driven system (100) for a personalized chatbot for financial advice using LLMS and real-time data integration, comprising the following: a chatbot module based on a large language model (LLM) that is configured to process user requests and generate personalized financial recommendations by using natural language understanding and historical data patterns; a real-time data processing module configured to retrieve and process financial data from stock exchanges, banking systems, government economic reports and financial news sources, continuously updating relevant financial information such as stock prices, interest rates and economic indicators; a risk assessment module configured to analyze a user's financial profile, including their risk tolerance, investment history and financial objectives, to ensure regulatory compliance and the suitability of investment recommendations in accordance with relevant financial regulations; an adaptive module based on machine learning that is configured to refine and optimize financial recommendations based on user interactions, financial trends, historical data and evolving market conditions, learning from past user behavior and adapting to changing market dynamics; a natural language processing module that facilitates interactive interaction with the user through text, speech, and graphical financial reports, thus enabling multimodal interaction to improve user experience and understanding of complex financial information; a proactive warning module configured to inform users about investment opportunities, market risks, and necessary portfolio adjustments based on real-time market data, trend analyses, and predictive models; a security and compliance module that ensures encrypted data transmission, user authentication and compliance with financial data protection regulations, including the secure storage of sensitive financial data and adherence to industry standards such as GDPR and PCI-DSS.

The invention relates to the technical field of electronic shopping, in particular to a big data analysis method based on an e-commerce platform and an AI system, and the method comprises the following steps: collecting a click rate, a search frequency and a purchase record of a user on the e-commerce platform, carrying out the subdivision of the click rate, the search frequency and the purchase record, and creating a data set; according to the invention, by integrating the equipment fingerprint technology and the social media data, the analysis precision of the user behavior data is improved, and the capabilities of user identity recognition and behavior pattern analysis are further optimized. In-depth analysis is carried out on user interaction data such as click rate, search frequency and purchase records, consumer behavior patterns and preference prediction are refined through clustering and decision tree algorithms, and sentiment analysis and keyword extraction are carried out on social media posting data. Therefore, the enterprise can monitor the market dynamic state in real time and quickly adjust the marketing strategy. And the accuracy of market prediction and the pertinence of marketing activities are improved.

The invention discloses an intelligent management system and method for cable production, and the system builds an intelligent decision-making system for demand prediction through the dynamic fusion of historical sales data and market trend information. Specifically, a time sequence feature coding and semantic embedding technology based on deep learning is adopted, cross-modal association is carried out on a local rule of a historical sales window and market dynamic semantics, and deep collaborative decision response is further carried out on historical information and market trends to obtain historical sales-market trend collaborative decision response coding features; and based on this, short-term prediction of the cable market demand is realized. In this way, the timeliness and accuracy of demand prediction are remarkably improved, the risk of supply chain imbalance caused by sudden demand change is effectively relieved, and then dual optimization of the resource utilization rate and the market response capacity is achieved.

The invention relates to the technical field of electric power systems, in particular to an electric power spot market risk prevention and control method and system considering market subject behaviors. Acquiring operation data of generating capacity, electricity demand, market price and power grid state of the electric power spot market; on the basis of the acquired operation data, a coupling degree evaluation index and a stability evaluation index are introduced to carry out prediction and anomaly detection on a market trend, and influence factors of connection of a large-scale generator set and a small-scale power grid are particularly concerned; analyzing a market subject behavior pattern through transaction frequency, price volatility and market participation degree characteristic quantitative indexes, and identifying market manipulation and violation operation risks; market dynamics are monitored in real time, a risk threshold value is set, and an alarm is triggered when a risk index exceeds a set standard; early warning information of different levels is automatically sent out according to the risk degree, and corresponding countermeasures and suggestions are provided; and a supervision mechanism is helped to make a quick response through a visual interface.

The invention discloses an intelligent financial consultation system and method based on a large language model, and the system comprises the steps: receiving a multi-mode financial consultation request of a customer, analyzing the input of the customer through the natural language processing and computer vision technology, extracting key information and intention, and dynamically constructing and updating a customer portrait. And specific requirements of clients can be accurately identified. And after the identified demands are stored in a problem database, the system performs real-time analysis on historical data, market dynamics and customer behaviors by using a machine learning algorithm, thereby automatically identifying and predicting potential demands and market trends of customers. Afterwards, on the basis of the prediction results, the system uses a large language model to generate personalized and multi-dimensional financial suggestions, including different risk levels and profit expectations, so as to help customers to make better decisions.

The invention discloses a coal market analysis and purchase decision-making method and system based on big data, and belongs to the technical field of coal market data analysis, and the method comprises the steps: generating an integrated market analysis data set through multi-source data collection and data integration and cleaning; based on the integrated market analysis data set, data analysis and mining are carried out through index calculation, correlation analysis and trend prediction, and an analysis result containing market dynamics, price trend and supply conditions is obtained; displaying a preset report template based on the market analysis, the purchase suggestion and the data; and based on an analysis result, generating a coal purchase decision support report in combination with a report template and an automatic filling technology, and outputting the report in a specified format. According to the invention, factors in multiple aspects such as coal quality, purchase cost, transportation mode and supplier capability are comprehensively considered, and accurate and reliable coal market analysis and purchase decisions are provided for users through data acquisition and integration, data analysis and mining and automatic analysis report generation.

The invention provides a supply chain cost anomaly AI positioning system based on multi-mode depth tracing, and relates to the technical field of cost anomaly tracing. According to the supply chain cost anomaly AI positioning system based on multi-modal depth tracing, a three-layer architecture is adopted, a dynamic threshold algorithm and a multi-level association map are combined, real-time detection and root positioning of cost anomaly are achieved, and the three-layer structure is data acquisition, dynamic modeling and visual tracing. The system has the advantages that dynamic adaptability is improved, reasonable fluctuation and abnormal risks are accurately distinguished, the system breaks through the rigid limitation of a traditional static rule, the judgment threshold value is dynamically adjusted by fusing market dynamics (such as bulk commodity prices and policy adjustment) and real-time service data, and a supplier-production-logistics-storage association graph is constructed, so that the risk of abnormal risks is accurately distinguished. And the conduction path of the cost abnormity in each link of the supply chain is visually displayed.

The invention relates to the technical field of e-commerce live broadcast, and particularly discloses a live broadcast data-based e-commerce live broadcast trend analysis system, which is used for extracting live broadcast commodity sales data and historical live broadcast sales data of a target commodity to obtain a live broadcast sales data time sequence set and a plurality of historical live broadcast sales data time sequence sets, performing time sequence feature analysis on the live broadcast sales data time sequence set and each historical live broadcast sales data time sequence set by using an AI-based data analysis and processing algorithm; therefore, the commodity sales data speculation value of the next time interval of the target commodity is intelligently pre-determined according to the selection range ratio dynamic semantic query expression between the live sales data time sequence characteristics and the historical live sales data time sequence characteristics. According to the method, nonlinear and changeable market dynamics can be better captured, so that the model not only pays attention to a short-term trend, but also can understand a long-term mode, and the understanding depth of a sales volume change rule is improved.

The invention belongs to the technical field of advertisement putting management, and particularly relates to an intelligent precise advertisement putting optimization method and system based on dynamic strategy adjustment. The system comprises a user behavior deep insight module, a market dynamic perception module, an advertisement effect intelligent evaluation module, a strategy dynamic generation module, an advertisement intelligent putting execution module and an advertisement putting management end. Various behavior data of users in a network environment are collected and analyzed through the user behavior deep insight module, the market dynamic sensing module monitors and analyzes changes of the market environment in real time, and the advertisement effect intelligent evaluation module evaluates the actual effect of advertisement putting. And the strategy dynamic generation module dynamically generates and adjusts the advertisement putting strategy, the advertisement is accurately put to the target user according to the advertisement putting strategy, the advertisement putting is comprehensively upgraded from multiple dimensions of accuracy, timeliness, effect optimization and user experience, and the increasingly complex and variable advertisement putting requirements are met.

The invention relates to the technical field of agricultural product sales management, in particular to an agricultural product intelligent sales management system based on AI digital humans, and aims to solve the problems that in the prior art, sales volume, price and environmental factors cannot be comprehensively analyzed to construct a sales trend prediction model, a comprehensive view cannot be provided to improve prediction accuracy, and the sales trend cannot be predicted. And enterprises cannot quickly respond to market dynamics. According to the method, the sales trend prediction model is constructed by using historical sales and meteorological data, the method has significant advantages, the sales volume, price and environmental factors are comprehensively analyzed, a comprehensive perspective is provided to improve prediction accuracy, the sales trend prediction feature vector is generated, the module can predict future sales volume change in real time, and the prediction accuracy is improved. And enterprises are supported to quickly respond to market dynamics, and inventory, pricing and marketing strategies are optimized.

The invention relates to the technical field of financial service management, in particular to an intelligent supply chain financial service platform, and the platform comprises a supply chain data analysis module which receives and integrates market dynamics, carries out the format standardization and data cleaning of the market dynamics, and generates a data integration result; and performing dimension reduction on the data integration result by applying principal component analysis, and extracting factors influencing the health degree of the supply chain to obtain a key factor extraction result. According to the invention, standardized integration and data cleaning are carried out on market dynamics, reliability and consistency of supply chain data are enhanced, data dimension reduction is realized in cooperation with principal component analysis, key factors having the most influence on the health state of the supply chain are extracted, and accuracy and sensitivity of supply chain state evaluation are improved; the risk features of the supply chain entity are analyzed by adopting a clustering algorithm, behavior pattern analysis is performed on the basis of risk feature grouping, potential risk points are found in advance, and the risk pre-judgment capability and prevention and control accuracy are enhanced.

The invention provides a real-time commodity price prediction system based on multi-source data fusion, and the system comprises a data integration module which is used for obtaining multi-source heterogeneous data, carrying out the alignment processing of the multi-source heterogeneous data according to the commodity types and timestamps, and generating a standardized fusion data set; the dynamic weight distribution module is used for dynamically calculating the weight coefficient of each data source according to the relevance between each data source in the standardized fusion data set and the commodity category; the model integration module is used for performing weighted fusion on the price trend prediction result output by the time sequence prediction model and the game correction output by the market game model based on the weight coefficient to generate initial price prediction; and the optimization output module is used for performing periodic rule correction and strategy collaborative analysis on the initial price prediction according to the real-time market dynamic data to obtain a predicted commodity price trend. By adopting the system, the commodity price trend can be dynamically predicted based on multi-source big data, an enterprise is helped to optimize a purchasing strategy, and the inventory overstock risk is reduced.

The invention provides an automobile after-service pricing model optimization method under dynamic supply and demand balance, and relates to the technical field of pricing model optimization. The pricing model optimization method comprises the following steps of accessing a government policy issuing platform and an industry association database, capturing related policies and macroeconomic indicators of automobile post-service in real time, performing semantic analysis on financial news and policy interpretation articles, extracting potential factors, and performing classification and analysis on the potential factors. A competitor official website, an e-commerce platform and a social media account are monitored for 7 * 24 hours, a real-time feedback module is embedded in a service platform, customers are encouraged to evaluate services, prices and new product demands in time, a market signal threshold model is established, and the market quality is improved. And early warning thresholds are set for the policy change frequency, the competitor price fluctuation amplitude and the customer demand growth rate index. Through real-time market dynamic monitoring, scientific quantitative analysis, intelligent and accurate decision making and rapid iteration strategies, the enterprise pricing ability and market competitiveness are effectively improved.

According to the intelligent inventory management and automatic replenishment e-commerce operation method, sales data, inventory data, return data, commodity attributes, supply chain data and market environment data (such as social media, weather data, economic indicators and the like) of an e-commerce platform are integrated, and a data preprocessing method is adopted for cleaning and denoising, so that the quality and integrity of the data are ensured. A multi-dimensional data fusion and deep learning algorithm is adopted, multiple factors such as commodity historical sales data, seasonal fluctuation, market dynamics, user behaviors and the like can be fully considered, and therefore compared with a traditional single prediction model, the demand prediction accuracy can be greatly improved, the inventory overstock or stockout risk caused by prediction errors can be avoided, and the user experience can be improved. By designing a mechanism for monitoring and analyzing external events in real time, emergency situations (such as emergency promotion, market demand fluctuation, weather change and the like) can be quickly responded, a replenishment strategy can be timely adjusted, and inventory crisis caused by the fact that the system fails to quickly respond to external changes is avoided.

The invention relates to the technical field of commercial financial information processing, and provides a commodity market dynamic prediction method and system, a medium and electronic equipment. Acquiring real-time market information data of the target commodity and submitting the real-time market information data to a market information staring system; verifying the real-time market data, generating a data set hash value based on the target data passing the verification, aggregating the target data in a distributed hash table, and storing the aggregated data in a block chain based on the data set hash value; formulating a transaction strategy based on the target data, encoding the transaction strategy into a smart contract, and deploying the smart contract on the block chain; and when the market dynamic condition of the target data is predicted to meet the triggering condition of the smart contract, automatically executing the transaction. On the basis of ensuring the data accuracy and security, the transaction is automatically executed when the market information dynamically accords with the intelligent contract, so that the intelligence, automation and high efficiency of transaction decision making are realized, and the response speed and execution precision of commodity transaction are greatly improved.

The invention relates to a differential privacy-based competitive intelligence analysis system and method. The differential privacy-based competitive intelligence analysis system comprises a differential privacy processing and budget management module, a differential privacy feature engineering module, a privacy protection analysis and prediction module and a differential privacy synthetic data generation module. According to the design of the invention, an end-to-end advanced analysis framework with mathematics provable privacy protection capability is constructed, so that accurate, timely and reliable insight of market dynamics and competitor strategies is realized on the premise of strictly protecting enterprise sensitive commercial confidentials.

The invention discloses a trans-provincial and trans-regional electric power medium and long term transaction continuous operation model analysis system, and belongs to the technical field of electric power. A data acquisition and preprocessing module acquires electric power transaction data, new energy power generation data, meteorological data and market rule change information in real time or regularly; the market dynamic monitoring and analysis module dynamically monitors and analyzes the market supply and demand relationship, the price trend and the transaction rule change based on real-time and historical data; the new energy adaptability evaluation module evaluates the influence of the fluctuation and uncertainty of the new energy power generation on the medium and long term transaction of the power by comparing the actual data and the prediction data of the new energy power generation; and the transaction strategy optimization module formulates an optimal transaction strategy and a quotation scheme for two transaction parties by constructing a multi-objective optimization model and synthesizing cost, risk and income. According to the invention, the accuracy of analysis and prediction of medium and long term transactions of trans-provincial and trans-regional electric power in which new energy participates is improved.

Computerized systems and methods are described for automated AI-driven customer and vendor segmentation and personalized insights delivery. The method involves collecting real-time data, including purchasing behavior and market trends, analyzing it using AI / ML algorithms for segmentation, and delivering personalized insights through a user-friendly Single Pane of Glass User Interface (SPoG UI). Transaction details are logged for ongoing enhancement. Effectiveness of segmentation is monitored and refined based on user feedback and evolving market dynamics. Multiple data sources and analytics tools are integrated for comprehensive analysis. Users can customize segmentation parameters, and delivery options include push notifications and email alerts. The system facilitates continuous optimization and adaptation, enhancing relevance and precision.

The invention relates to the technical field of intelligent pricing, in particular to an intelligent pricing management system based on machine learning and data analysis, and aims to improve the purity and consistency of data and lay an accurate data foundation for subsequent analysis through cleaning and format unified processing of market original data and elimination of redundant information. Through a non-dominated sorting genetic algorithm, the influence weight of each data point is analyzed, comprehensive quantitative modeling of price influence factors is realized, a model basis with better interpretation and adaptability is provided for price prediction, and market changes and competition patterns are dynamically captured by combining a linear regression model, so that the price prediction accuracy is improved. And the price is ensured to be dynamically and accurately adjusted along with the market through a fine adjustment strategy, the dynamic matching capability between the price and the market fluctuation is realized, the adaptability of the strategy under different market conditions is verified and optimized through a multi-scene simulation test and market feedback monitoring, and the high efficiency and stability of the price adjustment strategy in practical application are ensured.

The invention discloses a multi-agent collaborative optimization scheduling method and device based on peak regulation market dynamic rule formulation, and the method comprises the steps: carrying out the refined classification of resources in multiple agents according to the physical characteristics and adjustment flexibility of the resources, building a multi-dimensional classification system, and providing a data basis for the dynamic design of rule parameters; an accurate description model is established for each type of resources, and feasible regions and cost characteristics of the resources in a peak regulation market are quantified; establishing dynamic association between resources and peak regulation market rules, embedding rule parameters into the optimization model, and constructing a power market optimal rule model containing multiple agents; and constructing a double-layer collaborative optimization strategy, solving the model and outputting an optimal rule of the current peak regulation market, and realizing market rule dynamic optimization and resource collaborative scheduling. According to the method, the static limitation of a traditional peak regulation market rule is broken through, dynamic matching of rule parameters and resource characteristics is achieved through multi-agent collaborative optimization, and the new energy consumption capacity and the market operation efficiency are effectively improved.

The invention relates to the technical field of product oil management, and discloses a product oil purchase-sale-stock multi-modal data analysis method and system. The method comprises the following steps: acquiring real-time inventory data, transportation state data and sales terminal data in a product oil supply chain to form a multi-modal data set; performing data cleaning on the data, and extracting key inventory characteristics to generate a cleaned data subset; obtaining associated historical purchase data, regional demand prediction data and market price fluctuation data based on the gas station identification information to form a multi-dimensional auxiliary data set; inputting the features into an inventory feature extraction model, generating a multi-feature vector set, and fusing the multi-feature vector set; inputting the fused feature vectors into an inventory state evaluation model set, and outputting inventory early warning level and abnormal type information; and finally, a dynamic allocation scheme is generated by combining real-time inventory data and the like. According to the method, multi-modal data are integrated, so that the comprehensiveness and accuracy of data analysis are improved, and the purchase-sales-stock management of the product oil is assisted to be more suitable for market dynamics.

The invention discloses a power distribution network material cost dynamic calculation system construction method based on multi-dimensional sensitivity analysis. The method comprises the following steps: fusing multi-source heterogeneous data and standardizing business characteristics; carrying out single-factor sensitivity modeling based on dynamic demand elasticity; performing multi-factor coupling sensitivity analysis; constructing a dynamic cost calculation system; and performing economic influence prediction and dynamic verification. According to the method, the problem that a traditional method is insufficient in processing complex multi-source data and coping with market dynamic changes is solved, accurate cost prediction and intelligent decision support are achieved, and the efficiency and economic benefits of power distribution network material management are improved.

The present invention discloses a smart marketing management method and system based on AI artificial intelligence. The smart marketing management system includes a data collection and preprocessing module, a market dynamics prediction module, a competitive product analysis module, an advertising strategy generation module, and a strategy execution and feedback module. The smart marketing management system collects relevant data on marketing advertisements in the market, collects market dynamics data, monitors and updates the advertising strategies and market performance of competitive products in real time, and automatically adjusts advertising delivery strategies through AI artificial intelligence technology based on competitive product analysis results and real-time advertising effect data. Through the introduction of AI technology, market and competitive product data can be analyzed in real time, advertising strategies can be automatically adjusted, advertising content and delivery channels can be optimized, and advertising budgets and time frequencies can be dynamically adjusted. This system is not only highly intelligent and automated in advertising decision-making, but also achieves a reasonable allocation of marketing resources through multi-dimensional optimization strategies, significantly improving the overall effectiveness of advertising and the rate of return on investment.

The invention provides a reinforcement learning dynamic pricing method based on game feedback, and relates to the technical field of online data market dynamic pricing. Through combination of leader-follower strategy interaction of the Stackelberg game and the exploration-utilization balance principle of the dobby machine, the problems of price adjustment lag, large income fluctuation and insufficient strategy stability in a non-stable environment are solved. By introducing a data freshness evaluation mechanism, data weight is dynamically adjusted by integrating data timeliness attenuation and market correlation analysis, it is ensured that data input into a model always has high real-time performance and strong correlation, and the problem of decision lag caused by static data input in a comparison scheme is avoided; the exploration-utilization balance principle and the reinforcement learning model of the dobby machine are fused, the exploration rate is dynamically adjusted, a historical optimal strategy can be fully utilized in a non-stationary environment, a potential better scheme can be explored, and the static optimization bottleneck that a game model is limited to a preset objective function in a comparison scheme is broken through.

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.