102 results about "Market competition" patented technology

The invention discloses an electric power spot day-ahead market auxiliary quotation method, and relates to the technical field of electric power system automation, and the method comprises the steps: collecting power grid operation, weather, market and carbon market data through multi-source data fusion preprocessing, and carrying out the processing through a space-time generative adversarial network and a natural language processing model, so as to form a multi-dimensional data set; embedding a power grid power balance constraint by using a graph neural network to carry out topology modeling, and extracting spatio-temporal characteristics by combining a time sequence neural network and an attention mechanism; and a double-layer optimization model is constructed to realize collaborative decision-making of futures and spot markets, and carbon-electricity joint optimization and a dynamic shipping space adjustment mechanism are synchronously integrated. According to the method, the problems of data isolation, inaccurate prediction, insufficient risk control and the like of a traditional quotation method are solved, the quotation precision and the market competitiveness are improved, an auxiliary quotation scheme giving consideration to benefits and risks is provided for power generation enterprises, and the method is suitable for quotation strategy optimization of the current market of electric power spot goods.

The present invention provides an intelligent marketing method and system based on user portraits; first, basic information and browsing behavior data of users are collected through an authorization agreement to establish an initial user portrait. Further, by comparing the basic information in the user portrait with a pre-set similarity vector, users with similar characteristics are grouped into a similar user group and share a set of similar product tags. Finally, personalized marketing information is pushed according to the similar user group where the user belongs, and at the same time, the product tag set is dynamically adjusted and optimized according to the feedback of newly launched users to ensure the continuity and effectiveness of marketing activities. This method effectively integrates user data, optimizes marketing strategies, improves the targeting and conversion rate of advertisements, and thus significantly enhances user satisfaction and the market competitiveness of enterprises.

The invention relates to the technical field of feed production, and discloses a feed production control method and system based on deep learning. Aiming at the problems of low production efficiency and unstable product quality caused by uncertain factors such as raw material quality fluctuation, equipment state change and the like which are difficult to deal with by a traditional feed production method, an improved CrossFormer deep learning model is adopted, and feed production process data are acquired and preprocessed in real time; and inputting the preprocessed data into a model to carry out validity evaluation on the current production strategy, and giving an adjustment instruction. And automatically adjusting a feed production strategy according to an output result of the model and an adjustment instruction, and recording all implemented adjustment measures, production data comparison before and after adjustment and equipment monitoring information to form a production optimization log. According to the method, the intelligent level of feed production is remarkably improved, the production cost is reduced, and the product quality and the market competitiveness are improved.

The invention provides a prediction method and system for quantity report, quotation and bidding by a building load aggregator, and the method comprises the steps: collecting bidding historical data, and carrying out the preprocessing of the bidding historical data; inputting the preprocessed bidding historical data as an input item into a pre-trained quantity and quotation behavior prediction model to obtain a bidding action probability distribution result of the competitor; and based on the bidding action probability distribution result of the competitor, in a preset bidding decision simulation environment, predicting an optimal quantity report quotation bidding strategy of our party as a building load aggregator, and outputting a final prediction result. According to the method, the market competition pattern can be dynamically predicted, and the optimal quantity and quotation strategy is generated, so that the bid winning rate and the economic benefit of the building load aggregator in the demand response real-time market are improved.

The invention relates to the field of marketing, and discloses a commodity selling point generation system and method, equipment and a medium. According to the scheme, firstly, market competition hotspot analysis and user demand analysis are carried out on multi-modal information of a target commodity; on the basis of a market competition hotspot analysis result, obtaining a demand value model dimension to which key propaganda characteristics of an industry benchmark brand for the target commodity belong; secondly, obtaining a demand value model dimension to which a demand feature of a user for the target commodity belongs based on a user demand analysis result; and then performing difference market opportunity calculation on the demand value model dimension to which the key propaganda features of the industry benchmark brands for the target commodity belong and the demand value model dimension to which the demand features of the user for the target commodity belong so as to obtain selling point information of the target commodity. According to the selling point generation scheme, the input-output ratio of the target commodity in the aspect of network marketing is improved.

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.

The invention discloses an artificial intelligence-based electronic product sales data prediction method and system, and relates to the technical field of data prediction, the method comprises the following specific steps: S100, data collection and preprocessing: collecting data from an e-commerce platform, social media and a market investigation channel. Model parameters and structures are updated in real time, so that the models can continuously learn from newly added data and optimize themselves, and the method can more accurately capture dynamic features of market changes, including subtle changes of consumer behaviors, sudden turning of market trends and instant adjustment of competitive product strategies; therefore, the prediction accuracy and timeliness of the electronic product sales data are remarkably improved, an enterprise can formulate a more scientific and reasonable sales strategy and inventory management plan based on a more accurate prediction result, the market risk is effectively avoided, and the market competitiveness is improved.

The invention discloses a capacity configuration method and system based on participation of energy storage in spot joint market income, and the method comprises the steps: building a capacity planning model framework based on participation of an energy storage power station in electric power spot joint market income based on consideration of uncertainty conditions of wind power output; establishing a bidding model that the energy storage power station participates in the day-ahead energy-frequency modulation combined market under a master-slave game bidding strategy, and calculating the optimal configuration of an economic benefit guide energy storage power station capacity planning model; converting the bidding model into a single-layer equilibrium constraint mathematical programming problem according to a strong duality theory and a KKT condition; and solving the global energy storage power station capacity planning model by adopting a mode of nesting a CPLEX solver in a particle swarm algorithm. The method has the beneficial effects that the economic benefit of the energy storage power station can be maximized through scientific capacity configuration and a market bidding strategy, the market competitiveness of the energy storage power station is effectively improved, and wide application of an energy storage technology in a modern power system is promoted.

The invention relates to the technical field of market analysis, in particular to a footwear market trend prediction system and method, and the system comprises a sales volume change analysis module, a time period influence analysis module, a promotion influence analysis module and a sales volume prediction evaluation module. According to the method, the change rate of the sales volume of the shoes can be effectively evaluated by analyzing the association between the sales volume data and the economic indexes, the specific influence of seasons and festivals on the sales volume is considered, the sales volume prediction is more suitable for the actual market condition by calculating the time period influence factors, and the sales promotion activity effect is analyzed. According to the method, the influence of the sales promotion activity on the sales volume can be evaluated, data support is provided for making an effective sales promotion strategy, the sales volume change trend is predicted through multi-dimensional analysis, the accuracy of market trend prediction is improved, resource allocation is optimized, an enterprise can more flexibly cope with market changes, and thus the market competitiveness and profitability are improved.

The invention discloses an electric power spot market transaction decision-making method and system based on multi-factor dynamic analysis, and the method comprises the steps: building a power generation power prediction model based on historical power generation data, meteorological data and time information, predicting the power generation power of a future time period based on the power generation power prediction model, and carrying out the uncertainty analysis of the power generation power; establishing a market price prediction model based on the multi-source data, performing prediction based on the market price prediction model to obtain a market price in a future time period, and performing uncertainty analysis of the market price; building a transaction decision optimization model by taking profit maximization as a target and considering power generation cost, market risk and new energy consumption based on the power generation power and the market price of a future time period; and solving the transaction decision optimization model by adopting an improved genetic algorithm to obtain an optimal transaction decision scheme. Scientific, accurate and efficient transaction decision support is provided for the new energy power station in the electric power spot market, and the economic benefits and market competitiveness of the new energy power station are improved.

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 relates to the technical field of consumption behavior analysis, in particular to a user consumption data analysis method and system based on big data, and the method comprises the following steps: collecting and sorting the consumption data of a user, including purchase time, price, preference and product availability, calculating a correlation coefficient of the purchase time and the price, and calculating the consumption data of the user; and analyzing the correlation degree of the preference data and the product availability to obtain a correlation evaluation result. According to the invention, the consumption model is constructed through the differential equation, and the consumption behaviors at different time points are simulated through the Euler method, so that the consumption trend change can be predicted in a complex market environment, the flexibility and adaptability of data analysis are improved, the formulating and adjusting capabilities of the market strategy are effectively enhanced, and the market experience is improved. The method plays an important role in mastering consumption peaks and low ebbs, helps enterprises to occupy advantages in fierce market competition, optimizes resource allocation, and improves customer satisfaction and loyalty.

The invention discloses an e-commerce intelligent integrated management and accurate analysis system. According to the method, the collaborative filtering recommendation module in the precise recommendation module recommends the commodities which the users may be interested in to the users by analyzing the behavior similarity between the users or the relevance between the commodities, and the method does not need to depend on historical data of the users and can discover hidden preference relations. And the content recommendation module is used for matching interest portraits of the users based on feature information of the commodities, such as description, categories, tags and the like, and providing recommendation with strong correlation. The hybrid recommendation module combines the advantages of collaborative filtering and content recommendation, and improves the recommendation accuracy and coverage through a weighting or model fusion technology. Through continuous iteration and optimization of the algorithm optimization module, it is ensured that the recommendation system is dynamically adjusted along with user behaviors and market changes, the recommendation result always keeps high correlation and timeliness, and the operation efficiency and market competitiveness of the platform are improved.

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 relates to the field of multi-currency quotation, in particular to a multi-currency quotation calculation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining an original data set related to quotation; acquiring instant and historical exchange rate data of the transaction currency to form a market data set; calculating a cost currency estimated value of each cost composition item and summarizing to obtain a direct cost total amount; analyzing the research data to obtain a risk level, and providing a corresponding insurance cover scheme; calculating a cash flow estimated value under the influence of the exchange rate by combining the cash flow, the historical exchange rate and the insurance cover scheme, generating and sorting a contract quotation amount sample set according to a preset gross profit rate, determining the minimum contract quotation by combining the confidence coefficient, and obtaining the final contract quotation amount according to the premium parameter. According to the method for calculating the multi-currency quotation, the problems that the traditional quotation exchange rate has no standardized calculation logic and the decision is lack of quantitative support are effectively solved, accurate cost accounting and risk management and control are realized, and enterprise profit and market competitiveness are balanced.

The invention belongs to the technical field of power system automation and energy storage regulation and control, particularly relates to a thermal power energy storage frequency modulation auxiliary service benefit maximization method, and aims to solve the problems of disjunction between technical response and economic benefit, large equipment loss and poor market adaptability in a traditional frequency modulation strategy. According to the method, a multi-time-scale instruction analysis model is constructed, thermal power coal consumption and energy storage loss characteristics are fused, a dynamic cost-income coupling function is established, and thermal storage power is distributed in real time based on a rolling time domain optimization algorithm; and dynamically correcting model parameters by executing feedback and an online learning mechanism, and synchronously generating a market bidding strategy and benefit pre-judgment report. The system supports state-of-charge boundary adaptive adjustment and equipment collaborative aging evaluation, and is deployed at an edge computing node to realize millisecond-level response. According to the invention, the economic benefit of frequency modulation is obviously improved by 15-25%, the score of frequency modulation performance is more than 95, the energy storage life is prolonged by more than 30%, the fluctuation of unit income is reduced by 40%, and full-link closed-loop intelligent regulation and control and market competitiveness enhancement are realized.

The invention discloses a service platform for completing bidding document production based on automation technology assistance, and belongs to the technical field of bidding services, and the service platform specifically comprises the steps that a user behavior collection module collects operation behavior data of a user in the bidding document production process; the decision preference analysis module processes the operation behavior data through an analysis model to generate user decision preference parameters; the strategy generation module generates a candidate bidding strategy set based on historical cases; the scene simulation module constructs a bidding scene simulation environment and records market competition data in a strategy execution process; the strategy evaluation module calculates a strategy competitiveness index according to the market competition data; the strategy execution module screens an optimal bidding strategy and converts the optimal bidding strategy into a specific bidding file making scheme; through intelligent analysis of user behaviors and dynamic simulation of a bidding environment, the accuracy and competitiveness of bidding document production are effectively improved.

The invention relates to the field of enterprise product quotation management, in particular to a negotiable enterprise product quotation method, which comprises the following steps of: deploying a distributed co-governance product quotation negotiation platform comprising a product side enterprise, a product demand customer and a third-party evaluation mechanism; an incentive integral system is arranged in the platform, the user increases or deducts the integral according to the bargaining behavior, and the higher integral obtains the additional bargaining right; the enterprise divides the product quotation into a fixed part and a derivative part, and the fixed part and the derivative part are respectively calculated by an algorithm, combined and reported to the bargaining platform; the user in the platform carries out product bargaining, if the product bargaining passes, the transaction is concluded, if objection exists, the enterprise carries out secondary quotation and submits the quotation to the platform again, and a repetition threshold value is set to determine the operation execution frequency; and if the quotation is passed, the cooperation is achieved and is stored and recorded, and if the bargaining fails, the cooperation is abandoned. According to the invention, accurate, transparent and flexible product pricing is realized, the benefits of enterprises and customers are balanced, and the market competitiveness of enterprise products is improved.

The invention belongs to the field of artificial intelligence, and relates to an AI-based code generation method and device, computer equipment and a storage medium, and the method comprises the steps: analyzing and defining a data model, and converting a business demand into a data structure; constructing a code knowledge base, matching the business requirements with the code knowledge base, and identifying missing function modules in the code knowledge base; according to the missing function module, identifying a process logic in the business demand, and mapping a business process to a technical service orchestration; generating a basic code framework according to a mapping result from the business process to the technical service orchestration; according to the basic code framework, generating an implementation code of a business service, serializing a business process into an executable code, and generating a code interacting with a user interface; and the generated codes are optimized. The method has the advantages of high efficiency, accuracy, easy expansion and the like, and can improve the development efficiency, shorten the product marketing period and enhance the market competitiveness.

The invention relates to the technical field of electric power market bidding decision making, and discloses a market dynamic game bidding decision making method based on multi-stage deep reinforcement learning. According to the method, a market participant bidding behavior data set is obtained through an electricity market data center, and a reference bidding strategy is obtained through historical data trend analysis; according to market subject types, quotation elasticity and market power characteristics of each type are extracted; performing multi-stage deep reinforcement learning training on the features by using a reference bidding strategy to generate an initial bidding strategy matrix; and in combination with the matrix and the corresponding subject type, controlling the bidding decision agent to generate a dynamic bidding sequence in the feasible strategy set. And during market clearing, real-time data is collected to determine a feedback value, and an intelligent agent bidding strategy is adjusted according to the feedback value and the initial strategy value. According to the method, multi-stage learning and dynamic gaming are fused, main body features and real-time states are accurately captured, the adaptability and pertinence of a bidding strategy are improved, and participants are assisted in coping with complex market competition.

The invention belongs to the technical field of enterprise management, and particularly relates to an enterprise decision support system and method based on big data analysis. According to the invention, through integration of multi-source data, business dynamics are comprehensively mastered, an accurate decision basis is provided for an enterprise from the perspective of data driving, business changes can be quickly responded through real-time monitoring of business changes, the feasibility of business changes is evaluated in combination with market popularity information, the enterprise is helped to quickly grasp market opportunities or avoid potential risks, and the enterprise quality is improved. Through automation, the time and cost of manual analysis in traditional decision making are reduced, human prejudice is avoided, data-driven objective suggestions are provided, through business cost and market popularity data, an enterprise can more reasonably allocate resources, and the enterprise can quickly adapt to market changes by using a big data analysis technology, so that the enterprise efficiency is improved. And the market coping ability and decision-making efficiency are improved, so that the market competitiveness is enhanced.

The invention discloses an intelligent wealth management system. The system comprises a user portrait construction module, a dynamic asset configuration module, an intelligent investment adviser decision module, a risk control module, a transaction execution module, a user interaction terminal and a compliance supervision chain which are operated cooperatively. The system has the beneficial effects that through the integration of modular innovation and interdisciplinary technology, the comprehensive improvement of user portrait precision, asset configuration income, risk control capability and compliance efficiency is realized, the fundamental problems of response lag, risk blind area and user experience separation of a traditional system are solved, and the system has remarkable industrial application value and market competitiveness.

The application discloses a bidding opportunity prediction and collaborative decision-making method based on multi-source announcement data, relates to the technical field of intelligent bidding, and comprises the following steps: collecting bidding announcement data from no less than two information sources, extracting core information after format conversion, and generating a structured field in JSON format; carrying out integrity and consistency verification on the extracted structured field in sequence, and completing unit and format standardization modification of digital fields; based on the standardized field data, calculating project scale scores and market competition scores respectively, comprehensively obtaining opportunity scores, and dividing potential levels; collecting enterprise multidimensional internal resource data to calculate bearing capacity scores, combining the opportunity scores to calculate bidding scores, and making collaborative decisions.

The invention belongs to the technical field of data mining, and discloses a standard necessary patent prediction method based on a large language model and a related device. The method comprises the following steps: acquiring a feature group of a patent text to be predicted; inputting the feature group of the patent text to be predicted into a pre-trained large language model for prediction, and obtaining a standard necessary patent prediction result based on the large language model; wherein the pre-trained large language model is obtained by training a general large language model based on a plurality of standard necessary patents. According to the model, a domain knowledge enhanced large language model and a dynamic time sequence analysis framework are fused, and patent domain pre-training and multi-source heterogeneous data fusion are introduced, so that the patent text understanding precision of the model is improved; and meanwhile, a dynamic prediction mechanism is constructed in combination with industrial standard evolution data and technology development trend data, so that prospective pre-judgment of standard necessary patents is realized, and powerful support is provided for enterprise patent strategy formulation, technology research and development decision and market competition.

The invention discloses a bidding strategy generation method for an electricity-carbon combined market and related equipment, and the method comprises the steps: obtaining real-time market data and historical market data in a preset simulated market environment corresponding to a main intelligent agent and a plurality of other intelligent agents as competition objects, obtaining a power market predicted clearing electricity price and a carbon market predicted price based on historical market data through the prediction model; obtaining subject state information of a subject agent and behavior characteristics of other agents, and obtaining strategy elements based on the obtained data through a trained bidding strategy model; and obtaining a power market quotation strategy and a carbon market transaction strategy based on the strategy elements, and forming a bidding strategy of the power-carbon combined market. According to the method, the bidding strategy of the power generation manufacturer can be gradually optimized by integrating the market data and the competitive behavior characteristics, so that the income capability and the market competitiveness in the electricity-carbon combined market are improved.

The application provides an e-commerce demand analysis method and system based on a large language model, which optimizes product characteristics and market strategies by analyzing user-generated content (UGC). The method includes: first, collecting UGC data such as product reviews and user Q&A from e-commerce platforms. Then, using a large language model to deeply analyze these data, extract product attributes and sentiment information, and convert them into structured data. Next, generate a dual importance matrix at the market level to evaluate the importance of product attributes in purchase decision and post-purchase satisfaction. Further, for the products of the merchant, calculate the actual performance value of the attributes, generate a dual importance-performance matrix at the product level, and identify optimization needs. Finally, the analysis results are visualized and a report is generated to provide market insights and decision support for merchants. This method helps merchants accurately position product optimization direction and improve market competitiveness.

The invention relates to the technical field of coal price prediction, in particular to a coal price prediction system based on a multi-feature fusion LSTM network, which comprises a central prediction module and at least one local data acquisition module in communication connection with the central prediction module, any local data acquisition module is used for acquiring a to-be-predicted coal price data set in a first preset area and sending the to-be-predicted coal price data set to the central prediction module; the central prediction module is used for inputting a coal price data set into a preset coal price prediction model when receiving the coal price data set sent by any local data acquisition module, obtaining a corresponding coal price prediction result, and sending the coal price prediction result to the corresponding local data acquisition module. And the local data acquisition module is assisted to make business decisions. The method has the beneficial effect that the dominant position and risk response capability of an enterprise in market competition are remarkably improved.

The invention discloses a vacuum copying price automatic calculation method and system, and the method builds a total price formula in vacuum copying production based on the actual demands in production, and builds a vacuum copying price calculation system according to the total price formula, so that a quotation engineer does not need to carry out the complex calculation, and the calculation efficiency is improved. According to the method, the final quotation result can be obtained through the vacuum copying price calculation system only by inputting the basic parameters, the product processing quotation efficiency and accuracy are remarkably improved, powerful support is provided for enterprises in fierce market competition, and meanwhile economic losses caused by inaccurate quotation are reduced.

The invention discloses a supply chain management system-based goods in-time early warning method and system, and the method comprises the steps: constructing a multi-dimensional dynamic feature vector through obtaining key features of goods in a supply chain system, carrying out the standardization processing of the multi-dimensional dynamic feature vector, and encoding the multi-dimensional dynamic feature vector into quantum bits; constructing an energy function by using a quantum Isin model, solving and screening out a candidate solution set through a quantum annealing algorithm, and generating a goods in-time early warning threshold value; the threshold is applied to the lightweight neural network model, model switching is realized through loss function comparison, and the calculation overhead is reduced; through feature dimension reduction and selection, dimension disasters are avoided, the model training complexity is reduced, the real-time response capability is improved, and meanwhile, model parameters are dynamically optimized to enhance generalization performance and adapt to market environment changes, so that the goods immediate risk is reduced, the inventory loss is reduced, and the economic benefits and market competitiveness of enterprises are improved.

The invention provides a marketing full-link analysis method, device, equipment and medium based on artificial intelligence, and the method comprises the steps: carrying out the automatic processing and analysis of a large amount of sales information through an analysis model based on time series data, and carrying out the normalization processing and threshold comparison, and carrying out the recognition of the sales information when an indication parameter is abnormal. A new simulation product vector is input into the model, parameters are adjusted, a reference product vector is compared with a target simulation product vector, and the marketing strength of various products is set according to a comparison result. The method has the beneficial effects that a commercial tenant can formulate a personalized marketing strategy for each type of products, and the pertinence and effectiveness of marketing are improved. By accurately analyzing consumer demands and market trends and formulating personalized marketing strategies, merchants can better meet the consumer demands, and brand images and market competitiveness are improved.