15 results about "Price difference" patented technology

Methods and apparatus are disclosed regarding an e-commerce system that promotes sales of products based on product comparisons. Some embodiments may present a marketing message that justifies a price difference between presented products based on attribute differences between the presented products.

The invention relates to an electricity market price difference prediction method and system based on dynamic similar day matching. The method comprises the steps of obtaining electricity transaction data; processing the power transaction data to obtain a bidding space index and a price difference direction index; calculating the similarity between the day-ahead electric power spot price data and the real-time electric power spot price data; calculating a comprehensive similarity index according to the similarity; screening out data of the comprehensive similarity index in a set range to form a similar day set, and training a price difference classification prediction model by using the similar day set; and predicting the spot price difference direction of a prediction day by using the price difference classification prediction model. According to the method, training data samples are screened through a similarity algorithm, the over-fitting risk caused by invalid data volume is reduced, then the influence of extreme prediction of an individual algorithm is reduced through model diversity, and finally a robust probabilistic price difference direction prediction result is output.

The invention discloses an intelligent analysis system and method for market price estimation of products produced by an industrial enterprise, and the method comprises the steps: obtaining first public opinion data of the products at a plurality of collection times, the first public opinion data being data which is extracted from public opinion information of the products collected from a plurality of data sources and affects the price of the products; selecting second public opinion data of which the credibility meets the requirement from the first public opinion data; relates to the technical field of price prediction, and solves the technical problems that information sources of different prices of each material have different quotations due to different updating time nodes and the like, and the inaccurate quotation of the material easily affects the estimation of the product price. The data of one material is analyzed and compared through a plurality of data sources, and the first public opinion data is screened according to the credibility, so that the situation that the price difference given by the quoted data sources has a large change, and consequently, the estimation of the product price has a large deviation is avoided.

The invention relates to the technical field of electric power spot market day-ahead declaration, and discloses a day-ahead declaration strategy generation method, system and device, and the method comprises the steps: obtaining training data which comprises a day-ahead price, day-ahead new energy prediction data and a day-ahead real-time price difference; performing equal-width division on day-ahead new energy prediction data in the training data, and dividing the training data into a plurality of sub-boxes according to an equal-width division result; generating a target price difference and a target price of the target day according to the day-ahead real-time price difference and the day-ahead price of the target sub-box; generating a quantity reporting coefficient according to the target price difference; generating a declaration price according to the target price, the target price difference and the predicted price of the target day; and generating a declaration strategy based on the quantity report coefficient, the declaration price and the predicted electricity consumption of the target day, thereby solving the technical problem that manual transaction wastes time and labor in related technologies, and achieving the technical effects of automatically realizing multi-section quantity report quotation and improving the working efficiency.

This invention discloses a method and system for intelligent matching of preferred products based on user profiles, specifically relating to the field of e-commerce data processing technology. The method includes obtaining user account rights transaction records, filtering transaction lines based on payment completion markers, using the receiving field as the object primary key, forming an occupancy interval based on the start and end times of rights, and attaching the redemption time and purchase price to output a successful purchase table; merging the successful purchase tables by object primary key and forming an observation sequence based on transaction time, forming a preference continuation factor based on the primary key, a demand occupancy factor based on the intersection of intervals, and a price response factor based on the price difference following the purchase result, outputting a factor graph; extracting the object primary key, occupancy interval, purchase price, and redemption feedback from successful transaction records, constructing a factor graph, and separating the preference retention state and demand extinction state using a factor hidden Markov model algorithm, and then generating a purchase suppression set and a purchase strategy sequence using a particle learning algorithm and a strategy price determination process.

The application provides a power transaction risk monitoring method and system based on a multi-source signal mechanism and a sparse mixed expert model, which comprises the following steps: collecting historical day-ahead power market price data and original influence factor data, performing data preprocessing, generating a multi-source fusion signal by constructing a price difference signal and a bidding space signal; performing feature screening by using a random forest algorithm; taking the bidding space signal as a benchmark to construct a similar day data set and incorporate it into a training set; taking the price difference signal and the bidding space signal as targets, training different prediction models by using a sparse mixed expert model as a model framework; searching for an optimal bidding curve based on a northern hawk owl optimization search algorithm by predicting the price difference signal and the bidding space signal and taking a short-term power prediction curve as a benchmark. The application effectively solves the problem of insufficient model generalization ability, realizes power spot transaction risk monitoring, improves the accuracy of power spot transaction risk assessment, and improves the stability of a power system.

The invention relates to a calculator system and method for predicting stock buying and selling strategies. The calculator system comprises a parameter input unit, a core processing unit and a result output unit. The core processing unit executes a bidirectional closed-loop prediction algorithm based on an initial price, a principal, a fractionation number, a price difference, a non-linear growth rate, a complete transaction cost and financing and coupon fusion parameters input by a user: generating a buying sequence with a declined price and a non-linearly increased number in a buying direction; the total cost including the financing cost and the accurate local selling price are synchronously calculated; and generating a selling sequence of which the price is increased progressively and the number is increased nonlinearly in the selling direction, and synchronously calculating the total income and the reinvestment potential containing the voucher fusion cost. All calculations are integrated with an accurate cost and interest model in real time, an interface supports parameter linkage modification and result instant visual updating, and through combination of a bidirectional closed-loop prediction algorithm and a nonlinear transaction model, risk boundaries of buying and selling decisions are preposed and quantified, so that dynamic controllability of transaction strategies is realized.

This invention provides a joint inventory control method for recommending the availability of high-end and low-end substitutes in a pre-positioned warehouse, relating to the field of inventory management technology. This invention takes into account the order fulfillment time and price differences between the high-end and low-end products when purchasing two different products. E-commerce platforms can more accurately predict demand, rationally allocate inventory, and achieve supply and demand balance. By optimizing order quantities, platforms can reduce the risk of inventory backlog, improve capital utilization efficiency, and thus achieve cost control and profit maximization.

The invention discloses a financial data calibration method and device, electronic equipment and a storage medium, and relates to the field of artificial intelligence or other related technical fields, and the method comprises the steps: obtaining a real-time price difference sequence and a historical price difference sequence, and collecting event mark data of a target time period; performing feature extraction and vector coding on the real-time price difference sequence, the historical price difference sequence and the event mark data to obtain a price difference feature vector; the price difference feature vector is input into a price difference calibration model, a price difference sequence of the target time period is output, and the price difference calibration model comprises a long and short term memory model and an autoregression difference moving average model; and calibrating the basic financial data through the price difference sequence of the target time period to obtain calibrated financial data, and displaying the calibrated financial data to the client. According to the method and the device, the technical problem of inaccurate client financial data display caused by switching the overseas exchange quotation mode in the closed city interval of the domestic exchange in the related technology is solved.

The invention discloses a product conversion method and device, equipment, a storage medium and a product, and relates to the technical field of product transaction, and the product conversion method comprises the steps: calculating a dynamic price difference between a to-be-converted product and a target product; when the dynamic price difference exceeds a price difference threshold value, calculating the total cost of product conversion; and if the total cost is smaller than a preset cost critical value, converting the to-be-converted product into the target product in batches. According to the method, the dynamic price difference between the to-be-converted product and the target product can be calculated, and the to-be-converted product can be converted into the target product in batches only when the dynamic price difference exceeds the price difference threshold value and the total cost of product conversion is smaller than the preset cost critical value, so that seamless conversion of domestic and foreign markets can be realized; and the efficiency of financial product transaction is improved.

The invention discloses a new energy station bilateral time-phased transaction decision-making method, device and equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the historical quantity and price data of the bilateral time-phased transaction of a new energy station in a preset period, the historical price data comprises historical transaction electric quantity, historical transaction price and historical comprehensive income; based on the historical quantity and price data, analyzing the influence of the time-phased transaction quantity and price on the comprehensive income of the new energy station, and establishing a theoretical optimal comprehensive income typical database under historical typical quantity and price; based on a Pearson's correlation theory, calculating a correlation coefficient of the time-phased bilateral transaction volume price and the comprehensive return rate, and constructing a correlation analysis index; based on an Euclidean distance algorithm, calculating the similarity between the price difference of the centralized bidding transaction and the price difference of the spot transaction, and generating a predicted value of the transaction ratio of the centralized bidding transaction through linear regression; based on the probability distribution characteristics of the transaction ratio prediction value and the clearing price prediction, a random forest regression algorithm is adopted to construct a new energy bilateral time-phased transaction decision model; training the transaction decision model, adjusting model parameters and optimizing the prediction precision; and based on the optimized transaction decision model, generating a bilateral time-phased transaction decision result of the new energy station, and reporting the result to a preset transaction execution platform.

Methods and apparatus are disclosed regarding an e-commerce system that promotes sales of products based on product comparisons. Some embodiments may present a marketing message that justifies a price difference between presented products based on attribute differences between the presented products.

This invention discloses an intelligent order dispatching method and system based on multi-product combination price difference identification, belonging to the field of computer technology. The method includes: acquiring multi-product order data; generating a combination price difference representation vector based on a price difference identification rule base; acquiring a processing party capability profile; performing multi-dimensional matching degree calculation and dynamically adjusting weights; selecting the optimal processing party for order dispatch; and updating the rule base and profile based on feedback data. The system includes corresponding functional modules, supporting price difference identification, capability profile construction, dynamic matching, and online learning. This invention aims to solve the problems of unreasonable order allocation, low efficiency, and reduced revenue caused by the inability of existing order dispatching mechanisms to accurately identify multi-product combination price differences. Through value-driven intelligent matching, it significantly improves order dispatching efficiency, resource utilization, and operational revenue.

The invention provides a control method of an automatic vending cabinet. The control method mainly comprises the steps that a user submits a goods returning request and places goods in a goods returning window; an identification module verifies whether the commodity belongs to a returned commodity category; if the verification is passed, transferring the commodity and updating inventory information; recommending alternative commodities to the user; executing price difference processing operation according to the price difference between the alternative commodity and the original commodity; therefore, the goods return request can be automatically processed, the goods can be intelligently recommended, and price difference processing can be quickly executed, so that the automatic vending cabinet can efficiently process the goods return request of the user, and the user satisfaction and the shopping experience are improved.

The invention relates to an intelligent period and current fusion automatic purchasing system and method, and the method comprises the steps: collecting the real-time market data of a futures market and the quotation data of a spot market according to the collection frequency, and carrying out the preprocessing of the collected data; the period-present analysis module calculates key indexes, judges the market state, identifies whether an arbitrage opportunity or abnormal price fluctuation occurs, and marks the arbitrage opportunity if a period-present price difference rate stock investment threshold alpha; the intelligent decision engine generates a purchase strategy according to the market state and the enterprise demand, and performs risk inspection on the generated purchase strategy through the risk control module; and executing a purchase instruction after all the risk checks pass, monitoring the order state in real time, and finally storing the complete record of the purchase. Real-time synchronization and centralized display of futures and spot market data are realized, purchasers can acquire comprehensive market information on a single interface, the information acquisition efficiency is improved by more than 80%, and decision errors caused by information lag are effectively avoided.