53 results about "Market competition" patented technology

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 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 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 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 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 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 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 the technical field of electric vehicle charging and discharging scheduling, and discloses a multi-body double-layer game electric vehicle charging and discharging optimization scheduling system and method, and the method comprises the steps: an upper layer bidding module carries out the modeling of a non-cooperative game among a plurality of electric vehicle aggregators and an electric quantity distribution strategy of a power distribution network operator, so as to describe the competitive behavior of a market side; and the lower-layer scheduling module constructs an evolutionary game model of a large-scale electric vehicle user to simulate the dynamic response of a user side to an electricity price signal, and the system drives the upper-layer module and the lower-layer module to carry out closed-loop iterative solution by taking the charging and discharging electricity price and the total aggregation power as interaction information. According to the method, the game relationship among the independent benefit subjects is described, the behavior characteristics of market competition and user bounded rationality are reflected, and on the premise of protecting user privacy and deciding autonomy, a system stability equilibrium point is found, and an optimized scheduling scheme giving consideration to the benefits of all parties is formed, so that the electric vehicle cluster is guided to be charged and discharged orderly, and the scheduling efficiency is improved. And the stability and economy of power grid operation are improved.

The invention is suitable for the technical field of artificial intelligence, and provides a content creation method and system based on new media marketing, and the method comprises the steps: firstly obtaining the evaluation information of a plurality of users of a target commodity, then accurately determining the information of a content theme set according to the evaluation information of the plurality of users, and carrying out the recognition of the content theme set. And finally, quickly generating marketing content information according to the content theme set information and a preset video material library. The marketing content can be automatically generated on the premise of fully considering market feedback and consumer evaluation, the creation content is ensured to be closer to the demand and expectation of the target audience, the correlation and effectiveness of the content are improved, the market competitiveness of the brand can be improved, the time and labor cost are greatly saved, and the market competitiveness of the brand is improved. And the marketing content of the enterprise can more flexibly respond to the market change and the dynamic adjustment of the consumer demand.

The application relates to the field of intelligent price adjustment of commodities, and discloses an intelligent price adjustment method, device, medium and product for commodities with material specifications. The method comprises the following steps: acquiring a pre-set commodity price adjustment target mode; monitoring and collecting related commodity information of competitive products in real time; identifying whether a price adjustment event is triggered; if the price adjustment event is triggered, outputting a price adjustment decision matched with the price adjustment target mode according to the price adjustment target mode corresponding to the price adjustment event, so that the commodity with the material specification executes the price adjustment decision. The technical problems of the prior art in the aspects of response speed, competition strategy, price stability and risk control in the face of complex and changeable market competition environment can be solved.

The application provides a marketing strategy dynamic simulation optimization system data processing method and device, and relates to the field of data processing. The method comprises the following steps: establishing an industry market demand prediction model with a specified period based on industry characteristic data; establishing a market competition situation and market influence model based on historical market data, and determining the competition type of each market participant in the market competition structure in the target industry through the market competition situation and market influence model; establishing a production capacity and energy storage model based on the production process characteristic data, the upper limit of production capacity data and the inventory turnover level data of the target industry; constructing a market dynamic twin engine based on the production capacity and energy storage model, the market competition situation and market influence model and the industry market demand prediction model; and dynamically simulating the marketing strategies corresponding to different competition types through the market dynamic twin engine to obtain optimal marketing strategy data with a specified period.

The invention relates to the technical field of internet advertisement services, in particular to a time-sharing price adjustment method for agricultural e-commerce advertisements, which comprises the following steps of: periodically acquiring prices and inventory state indexes of similar agricultural products of main competitors; according to the method, the market competitive advantage index is established by periodically obtaining, comparing and analyzing the agricultural product prices and inventory levels of the competitor and our party, so that the advertisement bidding decision can respond to the instantly changing market supply-demand relationship and pricing game, and the traditional orientation mode only depending on wide user portraits is exceeded; meanwhile, the vitality of the advertisement creativity is diagnosed, the attenuation trend of the click rate is measured, the narrowing degree of user interaction behavior diversity and the saturation effect caused by repeated exposure of a single user are analyzed, and a multi-dimensional advertisement creativity attenuation index is constructed; and continuous investment on advertisements which have caused aesthetic fatigue of the user is avoided.

This application relates to the field of information technology and discloses a method and apparatus for product selection and advertising in cross-border e-commerce based on regional popularity analysis. The method integrates multi-channel data on regions, products, and users to construct a multi-granularity regional market knowledge graph and extract regional popularity vectors for candidate products. It mines user characteristics and calculates geographical distance weights to quantify the compatibility of products with regional culture, mines product association strength, and calculates dynamic recommendation scores. A decision-making system is built to generate regional product selection lists and personalized recommendation lists, and strategies are dynamically adjusted through anomaly detection. This method abandons subjective experience and the limitations of single data sources, accurately captures differences in regional market popularity, achieves deep matching between product selection and regional needs and user preferences, solves the problem of supply and demand imbalance, ensures accurate and flexible product selection, helps enterprises improve capital turnover efficiency and market competitiveness, and promotes the large-scale and refined operation of cross-border e-commerce.

The application relates to the technical field of transportation, in particular to a layout planning method and device for a component type mobile charging facility, wherein the method comprises the following steps: acquiring actual distribution conditions and actual charging standards of charging equipment of multiple operators; initializing the actual distribution conditions and the actual charging standards to obtain initial distribution conditions and initial charging standards, so as to calculate total revenue of the multiple operators based on the initial distribution conditions and the initial charging standards; and based on the total revenue, iteratively optimizing the initial distribution conditions and the initial charging standards by using a particle swarm algorithm and an evolutionary game strategy to obtain target distribution conditions meeting a preset iteration stop condition. Thus, the problems in the related art that due to the lack of effective pricing strategies, the fierce market competition cannot be effectively responded to, the market share is easily lost, the profit is reduced, and due to the unreasonable distribution of charging facilities, the utilization rate of the charging equipment is possibly reduced and the user experience is affected are solved.

The invention discloses a method for constructing a value network model of an integrated energy system, and the method comprises the steps: firstly dividing the integrated energy system into four parts, namely a source part, a network part, a load part and a carbon emission part, for carrying out the operation cost accounting based on an economics value chain theory; constructing a value allocation matrix based on a cost accounting result, and quantitatively calculating the value increment of each node and the contribution of the value increment to the total value of the system through the matrix; and a clear value network model is constructed, and accurate traceability and quantitative analysis of the value flow of the integrated energy system are realized. The invention aims to provide a scientific basis for the value allocation and pricing decision of the integrated energy system in the energy transaction market, and improve the economic operation efficiency and market competitiveness of the energy system.

The invention relates to the technical field of enterprise management, and discloses an artificial intelligence-based enterprise value evaluation method, device and equipment, and the method comprises the steps: obtaining and carrying out the structural management of the historical data of a target enterprise, forming basic evaluation information, analyzing the characteristics of the enterprise, selecting a key competition object, and collecting the asset data of the competition object. According to the method, an industry competition simulation environment is constructed, industry financial data and web texts are analyzed, emotional tendency is extracted, brand competition values are evaluated, potential value characteristics are mined, comprehensive analysis is carried out in combination with the brand competition values, enterprise value evaluation information is obtained, and comprehensive and accurate enterprise value evaluation is realized by integrating various data analysis technologies. According to the method, multiple dimensions such as financial performance, market competition and brand emotion can be considered, the accuracy and practicability of evaluation are improved, and the problem that the efficiency of evaluating the enterprise value in the prior art is low is solved.

This invention provides a method, apparatus, and electronic device for dynamic pricing optimization in a market with rigid demand, relating to the field of data processing. The method includes: establishing a market forecasting model for rigid demand based on industry characteristic data of the industry; establishing a market competition situation evaluation model based on historical competition data within the industry; converting specified production capacity data and relevant parameters of specified industry safety and environmental standards into quantitative production capacity constraints; establishing an evaluation model of the impact of market prices and freight costs in neighboring regions based on market prices and freight costs in the corresponding neighboring regions; fusing the market forecasting model, the market competition situation evaluation model, and the market price and freight cost impact evaluation model to construct a multi-factor, multi-dimensional spatiotemporal model; determining the optimal market price through the multi-factor, multi-dimensional spatiotemporal model according to a specified optimization objective function and quantitative production capacity constraints, and using the optimal market price as the result of dynamic pricing optimization in the market with rigid demand.

This invention discloses an artificial intelligence-based industrial automation production scheduling system and method, belonging to the field of automated production scheduling technology. The invention includes the following steps: obtaining historical production records of each production line from a database and analyzing the maximum capacity of each production line; obtaining real-time production records of each production line and, combined with the maximum capacity of each production line, analyzing the expected completion date and surplus characteristics of each production line; analyzing whether urgent orders can be placed in place based on the products and quantities of urgent orders, combined with the surplus characteristics of each production line in the first production line set; if urgent orders can be placed in place, analyzing the placed orders and their allocation characteristics based on the urgent delivery date of the urgent orders, combined with the surplus characteristics of each production line in the first production line set. This invention achieves accurate assessment of production line capacity, improves the accuracy and real-time performance of scheduling, and flexibly responds to urgent production needs, shortens delivery cycles, and improves customer satisfaction and market competitiveness.

The invention belongs to the technical field of multi-factor models, and particularly relates to a multi-factor model-based reinsurance pricing calculation method, which comprises the following steps of: S1, determining a target: determining the purpose of reinsurance pricing calculation, and evaluating the rationality of a reinsurance contract or comparing the advantages and disadvantages of different reinsurance schemes; s2, factors are selected, wherein key factors influencing reinsurance price comparison are selected, and the factors comprise insurance company underwriting risk characteristics, historical claim settlement data, market environment and reinsurance market supply and demand conditions; according to the method, the accuracy of reinsurance pricing calculation can be improved by comprehensively considering various key factors, the model utilizes historical and actual data and adapts to different market environments, the accuracy is ensured through cross validation and back test, detailed reports and analysis support decisions are provided, and reinsurance arrangement optimization, cost reduction and market competitiveness improvement are facilitated.