25 results about "Product pricing" patented technology

The invention provides an enterprise product pricing system and method based on hybrid multiple models, the pricing system comprises a support layer, a data layer, a model layer, a decision layer and an application layer, the support layer deploys a server and a storage device to provide underlying resource support for the system; the data layer completes data acquisition and preprocessing, deploys a distributed database, divides the distributed database into an original database module, a process database module, an achievement database module and a rule database module, stores data in a classified manner, deploys a block chain system and stores the data in a chaining manner; the model layer designs 11 product pricing models, calls data calculation of the data layer to output results respectively, and outputs final prices of products after weighted fusion by using a machine learning algorithm; the decision-making layer generates a product price report and formulates a final product price strategy; and the application layer displays the final pricing result for the user. According to the invention, flexible pricing decision is realized through enriching pricing models, the process is transparent and efficient, and price maximum rationalization of enterprise products under various market environment requirements is realized.

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 discloses a live broadcast e-commerce product promotion method, and relates to the technical field of live broadcast e-commerce, and the method comprises the steps: calculating the popularity of a product through a TF-IDF method based on the combination of an influence maximization algorithm and a collaborative filtering technology, quantifying the scarcity of the product and the preference of a user, reflecting the real preference of the user for the product, and improving the user experience. And through user multi-dimensional feature similarity, inaccurate recommendation when user data is sparse is avoided, a product promotion user group is refined and a user personalized product promotion strategy is designed in combination with the calculated user with the best propagation capability, and meanwhile, product promotion strategy implementation data is collected and analyzed, so that the recommendation accuracy of the product promotion strategy is improved. Market demand prediction, cost change analysis and a particle swarm optimization algorithm are combined with a dynamic adjustment pricing strategy, and real-time product pricing optimization is achieved.

A product pricing system may include at least one shopper device associated with a respective shopper and a product pricing server. The server may be configured to obtain geographic location data from the at least one shopper device and determine current store foot-traffic associated with a store at a given time based upon the geographic location data. The server may also be configured to adjust a current price of a given product for purchase at the store based upon the current store foot-traffic and cooperate with the at least one shopper device to display the adjusted current price of the given product for purchase at the store. The server may also be configured to lock the adjusted current price of the given product for purchase from further adjustment for a threshold time period based upon a selection input to the at least one shopper device.

[Problem] To provide an article supply device that has improved convenience and can easily handle changes in product pricing. [Solution] An article supply device 1 comprises: an insertion part 5 into which coins C can be inserted; a first retention part 25a and a second retention part 25b which are capable of retaining the inserted coins C; and a distribution part 26 which is provided between the insertion part 5 and the first retention part 25a / second retention part 25b and is capable of distributing the transfer destination for the coins. The first retention part 25a and the second retention part 25b include a setting part 28 capable of setting the number of coins that can be retained in each of the retention parts. The distribution part 26 is configured so as to, using the fact that a set number of coins are being retained in the first retention part 25a, perform distribution such that the coins inserted thereafter are retained in the second retention part 25b.

This invention relates to the field of intelligent product pricing technology, and discloses a method and system for generating intelligent product pricing strategies by integrating multi-factor analysis. The method involves: calculating a weight set and multiple first pricing parameters based on the historical pricing records of each tenant in the rights and benefits platform; performing a weighted operation on the multiple first pricing parameters based on the weight set to obtain the tenant's second pricing parameters; multiplying the second pricing parameters by the category benchmark price to generate M first candidate prices; verifying each of the M first candidate prices, selecting N second candidate prices that satisfy all constraints, calculating the comprehensive score of each second candidate price, and selecting the second candidate price with the highest comprehensive score as the target price and storing it in the tenant pricing matrix. This invention solves the technical problem in existing technologies where all tenants share the same pricing weights, leading to an inability to adapt to differentiated business needs, by integrating multi-factor analysis, and achieves intelligent generation of product pricing strategies.

The application discloses a data product pricing method and device based on reinforcement learning, and a medium. The method comprises the following steps: obtaining the cumulative total cost and the total number of calls of a target data product, and calculating a break-even bottom price of the target product; determining a value adjustment coefficient through a preset parameter mapping relationship according to multi-dimensional value evaluation parameters, and adjusting the break-even bottom price to obtain a benchmark price; calculating a price deviation value between the benchmark price and a market median price; when the price deviation value exceeds a preset threshold, generating an expected selling price based on the current operating state parameters of the market through a reinforcement learning agent; performing a gray-scale test on the expected selling price to determine a full-amount effective state; and based on the effective expected selling price, performing the verification and cancellation of the number of calls through a blockchain in response to the number of calls paid by a buyer. The cost and value are anchored, the reinforcement learning dynamic pricing and delivery feedback closed loop are linked, and the pricing can be adjusted according to market and quality changes.

The invention discloses a product pricing method, system and device based on data analysis, and the method comprises the steps: obtaining a product related data set, and obtaining a product feature data set through analysis and feature extraction; a comprehensive feature data set is obtained through the comprehensive feature data model and the product feature data set, and comprehensive feature data comprises sales comprehensive feature data, price comprehensive feature data, customer comprehensive feature data and inventory comprehensive feature data; establishing a plurality of product pricing pre-training models, and analyzing the product feature data set and the comprehensive feature data set to obtain product pricing prediction data; obtaining product pricing data through model training and model reasoning; and comprehensively analyzing the product pricing data to obtain a product pricing result. According to the invention, through comprehensive analysis of product related data, dynamic pricing of products of different time, markets, clients and categories is realized, the products are accurately priced in real time, and market demands are met.

The invention relates to the technical field of product quotation systems, and particularly discloses a quotation system based on a WeChat applet, which comprises a front-end module and a rear-end module, and is characterized in that the front-end module comprises a registration and login unit, a product quotation unit, a news display unit, a user information unit and a help link unit; and the back-end module comprises a product management unit, a news management unit and a user management unit. According to the quotation system based on the WeChat applet, a transaction and service platform with high cost effectiveness and high operation efficiency can be provided for merchants, and meanwhile, more convenient and personalized shopping experience is brought to consumers.

The invention discloses a three-level fresh food cold chain pricing decision analysis method and system. According to the method, for a supply chain formed by a manufacturer, a third-party logistics provider and a retailer, block chain adopting models without block chains and under different leading parties are established respectively, and a cold chain input level, a block chain traceability level and a consumer dual preference coefficient are introduced by constructing a consumer utility function and a market demand function, so that the market demand function is established; and establishing a multi-main-body profit function containing loss rate adjustment. And solving the Stackelberg game model by adopting an inverse method to obtain an optimal equilibrium solution of the retail price, the wholesale price, the transportation cost, the cold chain investment and the block chain traceability level. The influence of key parameters on the profit relationship and an optimal dominant structure are determined through sensitivity and numerical analysis, and a pricing and input strategy is output in a visual form. According to the method and the system, dynamic collaborative optimization of supply chain members under a multi-power structure can be realized, and the pricing scientificity of fresh products and the overall profit level of the supply chain are improved.

The present invention relates to the technical field of e-commerce platforms, and specifically relates to an intelligent guidance method for an e-commerce platform with a deep learning multi-dimensional user portrait. By constructing demand tags and interest tags for users, combining with the behavioral data of the target user group, it accurately predicts user demands and conducts personalized product recommendations. Demand tags and interest tags are constructed through deep learning algorithms. By calculating the correlation degree of the target user group, a demand budget for the user group is constructed, and different levels of interest guidance are carried out according to the interest intensity of users in specific products or services. The system also makes dynamic adjustments based on market feedback, such as adjusting product pricing and promotion strategies through positive feedback. This method can effectively improve the user purchase conversion rate, enhance the profitability of the platform, and has a high level of intelligence, and is applicable to the application of e-commerce platforms in personalized marketing.

The invention discloses a personalized pricing method and device and a computer readable storage medium. The method comprises the following steps: under the condition that a pricing request is received, analyzing the pricing request to obtain a pricing formula and a parameter matching strategy corresponding to the pricing request; determining a pricing parameter required in the pricing formula as a target pricing parameter; performing data matching in a database according to a parameter matching strategy to determine a parameter value of the target pricing parameter; and the parameter value and the adjustment value are substituted into a pricing formula for calculation, a pricing result of pricing the pricing product is obtained, the pricing of the pricing product is adjusted in real time according to the pricing result, and the adjustment value is a value for determining the pricing result by adjusting the parameter value based on the pricing request. The technical problem that the pricing mode is difficult to adjust flexibly due to the fact that most of traditional product pricing models in the prior art adopt a single pricing formula and a parameter matching rule is solved.

The application belongs to the technical field of artificial intelligence, and discloses an insurance product price forecasting method, device, equipment and medium based on data mining, which is applied to the fields of financial technology and medical health. The method comprises the following steps: obtaining a historical insurance policy dataset; performing clustering analysis on the historical insurance policy dataset by using a clustering algorithm; based on the result of the clustering analysis, screening a feature set related to premium data from dimensional feature data and claim data, which satisfies a preset condition; constructing a price forecasting model according to the feature set; in response to a price forecasting request for a target object, obtaining enterprise attribute information of the target object and its associated historical business data; inputting the enterprise attribute information and the historical business data into the price forecasting model, and outputting reference pricing information of the target object. The application solves the problems existing in the prior art of insurance product price forecasting methods.

This invention relates to an ontology-based method and system for petrochemical product pricing data processing and logical backtracking. The method includes: constructing an ontology model for the petrochemical pricing domain; performing real-time acquisition and semantic mapping of multi-source heterogeneous pricing data; constructing a pricing logic execution snapshot; and performing multi-dimensional logical backtracking and audit verification. The system includes: an ontology modeling module, a data mapping module, a pricing snapshot module, and a backtracking engine module. This invention also provides a corresponding system and computer-readable storage medium for implementing the above method. This invention achieves a semantically explicit expression of pricing logic, ensuring the integrity and non-repudiation of the evidence chain, and providing full lifecycle logical backtracking capabilities.

The invention discloses a quotation subscription method and device, equipment, a storage medium and a product, and relates to the technical field of data processing, and the quotation subscription method comprises the steps: subscribing a product quotation from a message queue at regular time, and judging whether the product quotation is abnormal or not, the product quotation in the message queue is obtained by real-time sending based on a main quotation application and a standby quotation application, and the standby quotation application is used for supplementing the quotation of the main quotation application; if the product quotation is abnormal, a correct product quotation is obtained through reverse checking from a quotation application; and if the product quotation is not abnormal, reserving the product quotation sent by the main quotation application. According to the invention, when the product price acquired by the message queue is abnormal, the product quotation can be reversely checked from the quotation application, and the standby quotation application can supplement the quotation of the main quotation application, so that the problem of reliability of a quotation publishing and subscribing system caused by service interruption and quotation data can be avoided.

The invention discloses a product pricing optimization method and system considering a carbon quota, relates to the technical field of carbon emission analysis, and solves the problem that the prediction accuracy is limited due to the fact that most carbon transaction price prediction models in the prior art depend on a single historical carbon price time sequence. According to the method, the carbon emission of a production unit product is obtained from a database, and the product wholesale price given by a manufacturer is marked as a decision variable; respectively constructing a demand function of a retailer and a profit function of a manufacturer based on the decision variables; obtaining the optimal demanded quantity of the retailer according to the decision variable; constructing a decision optimization model of the manufacturer according to the optimal demand of the retailer; obtaining a profit value of the manufacturer through a decision optimization model of the manufacturer; calling a quantum behavior particle swarm algorithm, and taking the profit value as a fitness value of particles; obtaining the number of iterations and an optimal pricing decision based on a quantum behavior particle swarm algorithm; according to the invention, the accuracy of predicting the carbon transaction price is improved.

The invention discloses a metal product quotation method and system, and relates to the field of metal product quotation, and the method comprises the steps: obtaining a plurality of webpage segments related to the product value in a current metal product and a market similar product, and determining a plurality of first viewpoints based on text mining analysis according to the plurality of webpage segments; according to the plurality of first viewpoints, screening to obtain a plurality of feature categories influencing the product value of the current metal product; wherein the feature categories comprise product specifications, market similar product pricing and user satisfaction; based on word frequency analysis, according to the plurality of first viewpoints, determining association degrees of the plurality of feature categories; determining a prediction dependent variable group corresponding to the first factor according to the correlation degree, and determining a first quotation of the current metal product according to the prediction dependent variable group in combination with a multivariable quotation prediction model; wherein the first factor is determined based on a user's selection of the plurality of feature categories. According to the invention, the technical problem of insufficient quotation accuracy of existing metal products can be solved.

The invention is suitable for the technical field of live broadcast goods carrying, and provides a differentiated product pricing and live broadcast strategy implementation system in a live broadcast goods carrying mode, and the system comprises a manufacturer module which is used for determining the value difference of two heterogeneous products, and selecting an optimal live broadcast strategy according to the value difference; the anchor module is used for receiving a live broadcast strategy of a manufacturer and a selected product, and executing a live broadcast task; the consumer module is used for determining a purchase behavior according to the live broadcast information and the product utility function; according to the differentiated product pricing and live broadcast strategy implementation system in the live broadcast delivery mode provided by the invention, a manufacturer can accurately evaluate the value difference of two heterogeneous products, and then, according to the value difference and the interaction effect between the products, the product pricing and live broadcast strategy implementation method can be applied to various strategies such as single-product live broadcast, double-product live broadcast and a traditional sales mode. The optimal sales scheme is selected, the sales income is improved, the problem of low benefit caused by blind follow-up live broadcast is avoided, and the competitiveness in the live broadcast goods-carrying market is enhanced.

The invention provides a self-adaptive rapid development self-service cashier method and system, a medium and a program product, and relates to the technical field of intelligent self-service cashier, and the method comprises the steps: firstly obtaining the information of a commodity photo and a retail scene photo, determining a current sales scene through a deep learning training model, obtaining the information of hardware equipment to determine a payment mode, and carrying out the self-service cashier. A commodity pricing mode is determined in combination with a sales scene and a commodity photo, a self-service cashier system infrastructure is determined according to the sales scene, a payment mode and the pricing mode, and a commodity display module, a payment processing module and a pricing calculation module are included. And finally, according to a user instruction of a cashier display end, modifying an interface style, color matching and button layout by using the determined system infrastructure. Therefore, the self-service cashier can automatically adapt to different retail scenes, and the flexibility of the self-service cashier system is improved.

The invention discloses an external pricing model construction method. The model construction method comprises the following steps: confirming an external interest rate curve model; establishing a pricing engine model; establishing a scheme model according to the scheme condition; establishing a business detail data model; and establishing a final result model. The method solves the problem that business personnel do not know how to price products for customers, pricing principles, pricing mechanisms, parameters required for pricing and other information, and can quickly obtain guidance prices by obtaining information according to actual conditions of the customers through the use of an external pricing model and method, so that the practical value is very high, and support is well provided for business influence.

The invention relates to the field of big data processing, in particular to a cloud-based self-service machine cross-regional multi-machine collaborative dynamic price adjustment method and system, and the method comprises the steps: firstly obtaining the real-time sales data of various commodities in a plurality of remote self-service vending terminals, inputting the real-time sales data into a pricing model which pays attention to the local reference price, sales volume and inventory, and carrying out the real-time sales data processing; and finally, the commodity prices are distributed to the corresponding remote self-service vending terminals, so that the corresponding commodity prices in the remote self-service vending terminals can be updated in real time. Compared with the prior art, the method provided by the invention realizes cross-regional intelligent price differentiation, is more in line with an objective cost evaluation rule, has higher universality and higher data processing efficiency, and can improve the profit rate of an automatic vending platform.

The invention relates to the field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a product dynamic pricing method, device and equipment and a medium, and the method comprises the steps: collecting multi-dimensional data related to a to-be-priced product from a plurality of channels; performing data preprocessing and feature extraction on the multi-dimensional data, and extracting key features for dynamic pricing from the multi-dimensional data; inputting the key features into a pre-trained pricing model, and generating a corresponding initial pricing result according to the key features; and obtaining risk monitoring data and client interaction data in a preset time period, adjusting the initial pricing result according to the risk monitoring data and the client interaction data, and generating a dynamic pricing result. The multi-dimensional data is automatically priced through the pricing model, and the pricing result is dynamically adjusted by acquiring risk monitoring and customer feedback, so that the product pricing efficiency and flexibility are improved, and the requirements of customers and markets are quickly and dynamically met.

The application relates to the technical field of data product pricing, and discloses a data product pricing method based on utility evaluation and Bayesian response. The method first extracts features such as data quality, function coverage, reliability, service capability, maintenance cost and market scarcity, and calculates a product comprehensive utility value; then combines service cost, customer acceptance capability, historical transaction price and competitor price to generate candidate prices; further uses historical feedback and pricing feedback to update the transaction probability, demand and renewal possibility of each candidate price; finally, the risk-corrected expected income is calculated in combination with market fluctuation factors to determine an optimal pricing scheme. The application can improve the scientificity, rationality and stability of data product pricing.