10 results about "Product pricing" patented technology

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 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 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 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 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.