81 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 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 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 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 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 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 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 belongs to the field of data monitoring systems or methods, and relates to a platform enterprise entrepreneurship opportunity real-time monitoring method and system based on a digital technology, and the method comprises the steps: firstly, preliminarily obtaining an entrepreneurship opportunity; a new entrepreneurship opportunity is derived through iterative exploration; then data generated in the startup opportunity iterative exploration process serves as new data to be input into the digital intelligence base, the digital intelligence base repeatedly executes the first two steps based on the input data, and startup opportunities are continuously emerged; reasoning the relevance between the continuously emerging startup opportunities to generate a correlative startup opportunity set; and finally, according to the obtained entrepreneurship opportunity set, timely mining and finding entrepreneurship opportunities capable of being applied to the market, and dynamically designing a feasible space of the opportunities based on time lapse and ecological evolution to construct an opportunity map. By adopting the method, it can be ensured that platform enterprises are in industry prospect and leading all the time, the enterprises are helped to stand out in fierce market competition, and sustainable development is achieved.

The invention provides an AI-based product originality automatic generation method and terminal, and the method comprises the steps: predefining a product target, and collecting data from multiple channels according to the product target; respectively carrying out text preprocessing and image preprocessing on the collected text data and image data; training an AI creative generation model based on the preprocessed text data and image data; and automatically generating the final originality of the product in real time by adopting the trained AI originality generation model according to requirements and parameters input in real time. According to the method, inspiration can be automatically drawn from a large number of design cases, and personalized and innovative creative design meeting the market trend and user requirements is automatically generated through the AI creative generation model, so that the innovation efficiency and market competitiveness of product design are improved, the bottleneck of traditional manual creative generation is overcome, time and cost are reduced, and the method is suitable for popularization and application. The speed of responding to market changes is increased, and the systematization and data support capability of product design is enhanced.

The invention belongs to the technical field of stepped pricing systems, particularly relates to a stepped pricing system based on a dynamic packaging specification, and aims to solve the problems that consumers purchase unnecessary commodities, waste is generated and the cost is high due to the fact that flexible matching and corresponding pricing cannot be carried out according to specific requirements of the consumers in the prior art. In order to solve the problems that in the prior art, the market price is limited, and the sales potential and profit margin of merchants are limited, the invention provides the following scheme that the system comprises a user module and a menu analysis module, and the user module is connected with a menu input module, through precise pricing and personalized services, the market subdivision requirements can be better met, more consumers can be attracted, and the market competitiveness is improved. In addition, the system can quickly respond to market changes and optimize product combination and pricing strategies through intelligent design and data analysis capability, so that the system stands out in fierce market competition, and more business opportunities and profit growth points are brought to merchants.

According to the reliability distribution method and system considering the design margin and the development cost, the margin coefficient is introduced in the reliability design stage while economic indexes are considered, the average life-cost model of parts is constructed, and the reliability model of complex equipment is constructed based on the Bayesian network. On the premise that the task reliability requirement of considering the design margin is met, an optimization model of reliability distribution considering the design margin is established by taking the minimization of the total development cost as an optimization target, the optimal balance point of reliability and cost is searched based on the Monte Carlo random algorithm, and the reliability distribution optimization model of the design margin is established. And generating an optimal reliability allocation scheme of the complex equipment which meets the task reliability requirement and has the minimum development cost. According to the method, the overall reliability of the complex equipment is ensured, meanwhile, the proper design margin is reserved to cope with possible improvement or uncertainty factors in the future, effective control over the cost and reasonable distribution of the design margin are achieved, and the market competitiveness of the complex equipment and the use satisfaction degree of users can be improved.

The invention belongs to the technical field of competitive product mining, and discloses an AI intelligent marketing hot selling competitive product mining method based on category price, which comprises the following steps: acquiring competitive product data from a plurality of data sources by using a network data acquisition technology and data interface calling; cleaning and converting the obtained multi-source competitive product data; loading the cleaned and converted data into a database or a data warehouse; performing clustering analysis on the competing product data by using a clustering algorithm; comprehensively considering factors such as category popularity, price competitiveness, sales volume and public praise, and calculating a comprehensive score for each competitive product; and generating a competing product analysis report according to the data analysis and calculation result. The method for mining the hot competitive products shows remarkable advantages in the aspects of accuracy, efficiency and comprehensiveness, and can assist enterprises to more accurately grasp market trends, optimize products and marketing strategies and effectively improve market competitiveness.

The application relates to the technical field of market strategy generation of competitors, and discloses a market strategy generation method and device of competitors, electronic equipment and a storage medium, wherein the method comprises the following steps: through integration of multi-platform real-time data and construction of a dynamic user portrait, the influence of market changes on user behavior can be accurately captured, the limitations of a traditional prediction model can be effectively overcome, a method combining causal reasoning and dynamic modeling is adopted, market competition situations can be intelligently analyzed, an optimal response strategy can be automatically generated, the response time is greatly shortened, and finally, through an intelligent strategy generation mechanism, a target market strategy for the target competitor is generated, and the efficiency and precision of the decision-making process are realized.The application has the beneficial effects that the strategy generation efficiency is significantly improved, the scientificity and applicability of the strategy scheme are ensured, and reliable technical support is provided for enterprises to maintain a competitive advantage in a dynamic market environment.

The present invention discloses a storage method for multi-source heterogeneous target data. The multi-source heterogeneous target data is collected through a web crawler program module, and the image data is converted into text data, unifying the format of the multi-source heterogeneous data. Combining a clustering algorithm and a TextCNN model for target feature extraction, the effective fusion of multi-source heterogeneous data features is realized, and the target feature information is stored, providing real and effective data for the subsequent judgment and analysis model of market competition monopoly behavior.

The invention discloses a multi-agent bidirectional competitive cooperation system and method based on a large language model, and belongs to the technical field of artificial intelligence and computational economics crossing. According to the system, a bidirectional dynamic competition framework between a service agent and an experience agent is constructed, and dynamic gaming between the agents is realized through a resource-influence-information three-dimensional competition space in combination with a strategy generator driven by a large language model and a reinforcement learning optimization mechanism. A dynamic constraint mechanism is introduced into the system and comprises elastic resource supply and a reverse monopoly circuit breaker, so that market competitive health and system stability are maintained. The method can be used for scenes such as platform economic governance and meta-universe business simulation, and a computable theoretical tool is provided for digital social competition behavior research.

The invention relates to a sales follow-up user integration system based on a message queue and multiple threads, and relates to the field of information management. According to the system provided by the technical scheme, efficient transmission and processing of sales data are realized through a message queue technology, and the real-time performance and accuracy of information are ensured; meanwhile, concurrent processing of sales follow-up tasks is achieved through the multi-thread technology, and the processing capacity and the response speed of the system are improved; besides, by integrating sales management and a CRM system, automatic management and efficient follow-up of sales follow-up users are realized, so that enterprises are helped to improve sales efficiency and optimize customer relationships, and dominant positions are occupied in fierce market competition.