63 results about "Purchasing decision" patented technology

The invention discloses a clothing supply chain transparent purchasing and collaborative management method and system based on the Internet of Things and a block chain. The method comprises the following steps: monitoring and collecting state data of raw materials in real time through Internet of Things equipment; when the state data meets a preset purchase triggering condition, a purchase demand transaction is automatically generated and broadcasted on the alliance block chain network; a purchase smart contract deployed on the alliance block chain network responds to the purchase demand, screens candidate suppliers, and quantitatively scores the candidate suppliers based on multi-dimensional historical performance data obtained from a distributed account book to automatically determine a target supplier; the purchase smart contract automatically generates a purchase order transaction and performs consensus evidence storage on the alliance block chain network; and continuously tracking order state update on the alliance block chain network, and automatically executing a settlement process when a preset payment condition is satisfied. According to the invention, the problems of opaque information, lagging purchasing decision, low process efficiency and difficulty in multi-party collaboration of the traditional clothing supply chain are solved.

A metaverse-based luxury experience system and method are disclosed. The system includes a luxury 3D scanner that scans the shape of luxury items and a metaverse platform server that implements a virtual environment to display and sell 3D luxury items. The server enables a 3D user avatar to wear or experience these items using metaverse interface goggles and gloves. Additionally, users can scan themselves to create proportionate 3D avatars, ensuring accurate representation of luxury items. This allows users to virtually try on luxury items, assess fit, and make purchase decisions without physically wearing them.

The invention relates to an inventory threshold and purchase quantity calculation processing method and system based on AI prediction, and relates to the field of supply chain management, and the method comprises the steps: collecting historical data, real-time inventory data and external parameter data; preprocessing the historical data to form a training data set and an input data set; using the training data set to train the long-short-term memory network model to obtain a prediction model; inputting the input data set into a prediction model for analysis and prediction to determine a future demand trend; determining a material inventory threshold value according to the future demand trend and preset industry characteristic parameters; obtaining a basic purchase quantity according to the future demand trend, the real-time inventory data and the external parameter data; a material purchase amount is generated based on the external parameter data, the material inventory threshold, and the base purchase amount. The method and the system have the effects of intellectualization and accuracy of purchase decision.

Systems and methods for an e-commerce health care marketplace are provided. In one embodiment, a method comprises, responsive to a query from a user, retrieving from a database a list of responses comprising one or more health care providers offering one or more health care services at a provider-supplied price; filtering the list of responses based on a geographic location included in the query; providing, for display, the filtered list of responses; receiving a payment from the user for a response selected from the filtered list of responses; automatically sending a notification of the payment to a provider associated with the selected response; and responsive to receiving an order fulfillment notification from the provider, automatically providing the payment to the provider. In this way, health care consumers may make informed purchasing decisions when shopping for health care services and health care providers may receive prompt payment for services delivered.

The invention discloses a power plant fire coal matching and purchasing decision-making method and system, relates to the technical field of power plant fuel management, and aims to solve the problems of high purchasing cost and low decision-making efficiency caused by low coal source and unit demand matching degree and dependence on artificial experience of an existing power plant fire coal purchasing decision-making method. The method comprises the following steps: acquiring multi-source heterogeneous data; processing the multi-source heterogeneous data to generate a standardized feature data set; inputting the standardized feature data set into an AI fusion analysis model fusing a gradient boosting tree and an attention mechanism, and outputting a cost prediction result and a key index weight; and adopting an improved hybrid particle swarm optimization algorithm, and iteratively determining an optimal solution of the four-dimensional variable of the multi-objective function through an inertia weight dynamic adjustment strategy to obtain a target purchasing scheme. The power plant fire coal matching and purchasing decision-making method provided by the invention is used for improving the matching degree of the coal source and the unit demand in the fire coal purchasing decision-making, reducing the purchasing cost and improving the decision-making efficiency.

The information processing method provided by the invention comprises the following steps: determining a target sorting mode; determining at least one target prop; according to the sorting mode and the at least one target prop, sorting the gift bags in the gift bag set to obtain a sorted gift bag set; and displaying the sorted gift bag set. Therefore, personalized recommendation sorting and display can be performed on the gift bags according to the target props and the sorting mode selected by the user, the user is helped to quickly find the high-cost-performance gift bags, and the purchase decision-making efficiency is improved.

The invention provides a multi-role cooperation-oriented dynamic evaluation system and method for a management procurement process, and relates to the technical field of management decision assistance and semantic big data process.The method comprises the steps that firstly, a procurement intention evolution tree is built, and operation feature embedding is conducted through a semantic increment model of a non-European geometric space; the method comprises the steps of capturing a fuzzy-to-accurate evolution trajectory of a purchase demand in real time in a dynamic topology, solving the problem that a traditional time sequence process is difficult to quantify a cooperation value, adopting a multi-dimensional cooperation friction recognition mechanism, introducing a semantic alignment constraint, and decoupling an interaction behavior into effective deterministic contribution and invalid oscillation loss. According to the method, deep attribution of cooperation efficiency is realized, elastic process reconstruction is carried out based on a dynamic credit portrait, role capability and environment interference variables are distinguished, and a process optimization strategy with execution guiding force and a dynamic intervention instruction are output, so that the substantive interpretability of an evaluation result is effectively improved; and the response speed and scientificity of the purchase decision in the complex collaborative environment are enhanced.

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 discloses an enterprise internal acquisition cost optimization decision support system based on big data analysis. The system comprises a data aggregation and integration module, a purchase behavior feature extraction module, a cost driving factor deconstruction module, a supplier multi-dimensional evaluation module, a purchase strategy intelligent customization module, an enterprise purchase data warehouse and an enterprise purchase supervision terminal. The purchase strategy suitable for the enterprise is intelligently generated by integrating the purchase behavior characteristics, the cost driving factors and the supplier evaluation result, the purchase cost composition and the dynamic change can be accurately informed, scientific and reasonable purchase decision suggestions are provided for the enterprise, the purchase cost is effectively reduced, and the purchase efficiency is improved. And the execution effect of the purchasing strategy is monitored, evaluated and fed back in real time, so that each link of the purchasing process can be monitored in real time, potential problems can be found and solved in time, the purchasing efficiency and quality are improved, the optimization of the purchasing cost and the maximization of the overall benefit are realized, the purchasing supervision difficulty of an enterprise is remarkably reduced, and the intelligent level is high.

The invention provides an electric power spot market price prediction system and method based on artificial intelligence. The system comprises a market boundary prediction analysis module, a spot market price prediction module, an agent electricity purchase decision optimization module and a prediction result redisk analysis module. According to the system, a deep learning model fused with a CNN-MLP-Attention algorithm is adopted, kernel density estimation is combined to carry out electricity price interval prediction, meteorological data weighting processing, feature engineering, rolling training and multi-day prediction functions are integrated, and 1-7-day high-precision determinacy and uncertainty prediction of the electricity price of the electric power spot market is achieved. The method solves the problems of low electricity price prediction precision, large uncertainty, lack of scientific support of electricity purchasing strategies and the like in the existing electric power spot market, can effectively improve the decision scientificity of power grid enterprises in the spot market, reduces the transaction risk, and improves the efficiency of the power grid enterprises. And full-process data support and strategy simulation capability are provided for a power grid enterprise to participate in an agent power purchase transaction mode.

We provide the system. [Solution] Using data collection methods, electronic transaction information and paper-based transaction information are acquired. Using a generative model to analyze that data, we classify the transaction information. A means of automatically creating financial records, A means of evaluating household finances based on those financial records and providing financial management advice, Means of providing budget management and advice to support purchasing decisions, A system that includes simulation tools for setting long-term goals and proposing plans to achieve those goals.

An electronic system, a method, and a computer program product select and provide published reliable information articles that are contextually appropriate and authoritative to influence selection and timing of purchase of a product identified as of interest to a user. The electronic system includes at least one electronic device that detects, via input device(s), user input(s) by a user indicating a current interest in purchasing a product or service. The electronic system activates an electronic search to identify, in published information source(s), relevant article(s) containing product or service information associated with the product or service. The electronic system presents, via output device(s), a purchase interface section indicating the product or service and at least a portion of or a link to the product or service information from the relevant article(s), to influence timing of a purchase decision for the product or service by the user.

The application discloses a commodity recommendation method and system based on graphic decoupling characteristic learning, and belongs to the technical field of electronic commerce and artificial intelligence recommendation. In view of the problem that the current electronic commerce recommendation system cannot effectively capture the complex purchase decision and behavior change of consumers and is difficult to meet the personalized demand, firstly, the popularity factor and the interest factor influencing the user decision are decoupled, and a preliminary user-item interest representation and a popularity representation are constructed; then, a contrast learning mechanism is introduced to optimize the decoupled representation space, so as to reduce the coupling degree between the interest embedding and the popularity embedding; the real intention of the user is mined, and thus accurate commodity recommendation is realized. The application effectively improves the ability of the recommendation system in the aspect of personalized modeling, and has good practicability and scalability.

The invention discloses a medical consumable prediction and purchase decision-making system and method based on machine learning, and belongs to the technical field of medical artificial intelligence, intelligent hospital operation management and supply chain optimization. The method adopts a two-stage hybrid intelligent decision-making mode: in the first stage, a machine learning model based on a gradient boosting decision-making tree or a random forest algorithm is constructed, the future consumable consumption quantity is predicted in combination with multi-dimensional features such as historical consumption data, clinical business scheduling and seasonal factors, and then a preliminary purchase list is generated in combination with a safe inventory strategy; in the second stage, a decision engine which takes the large language model as a core and plays a role of'intelligent supply chain general supervision 'is constructed, dynamic market information, hospital financial constraints and external risk events are integrated, multi-target and constrained complex reasoning and tradeoff are carried out, an intelligent purchasing strategy report containing supplier selection, purchasing quantity distribution and other contents is generated, and a procurement strategy report is generated. And an interpretable reason is provided for the decision.

This enables users to consider appropriate purchasing decisions. [Solution] The generation device 100 receives prompts expressed in natural language text, which include information about the product selected by a user who wants to purchase the product, information about the user's situation, and a command to generate the target person's opinion on the product, into a large-scale language model that has information about the target person's profile set up, and generates the target person's opinion on the product. The generation device 100 outputs the generated opinion of the target person on the product.

The invention discloses a medical insurance management system and management method, and belongs to the field of medical insurance management, and the method comprises the following steps: collecting all pharmacy sales data, patient medical insurance medicine purchasing records, patient feedback data and medicine enterprise circulation data of a region, and carrying out the correlation integration processing of the data; and analyzing a price strategy and a prescription recommendation behavior of drug sales of each pharmacy, evaluating an influence degree of the pharmacy on a drug purchase decision of the patient in combination with a drug purchase record of the patient, and finally generating an individual behavior portrait of each pharmacy. According to the method, the multi-source data is collected, associated and integrated, the pharmacy behavior portraits are generated, the pharmacy portraits of the same specification are compared to find the abnormal points, the abnormal standards are formulated according to the abnormal points, regulation and settlement are performed on the illegal pharmacies, and the suspected pharmacies are manually screened, so that the medicine purchasing behavior can be accurately identified and induced, and the loss of medical insurance funds is reduced.

The embodiment of the invention provides a virtual commodity processing method and device, equipment, a storage medium and a program product. The virtual commodity processing method comprises the steps of displaying a media browsing interface of a target wearable commodity, wherein the media browsing interface comprises first media content and a try-on control of the target wearable commodity; and in response to a trigger operation for the try-on control, displaying second media content, the second media content being used for displaying an effect of wearing the target wearable commodity by the target virtual object, and the target virtual object being determined based on pre-uploaded portrait media content. By adopting the embodiment of the invention, the effect of wearing the wearable commodity by the virtual object can be displayed, and the virtual object is determined based on the uploaded portrait media content, so that the wearing effect of the wearable commodity can be displayed in a personalized manner, the accuracy of commodity purchase decision is improved, the condition of changing or refunding is reduced, and the purchase satisfaction and the use experience are improved.

The application provides a user-side power transaction electricity purchasing scheme personalized recommendation method and device, including: extracting the maintenance window landing position from the user equipment maintenance plan text, obtaining the month distribution of the maintenance window landing position in the contract period, identifying the shutdown period and the production recovery period during the maintenance; analyzing the contract scheme whose centroid month exceeds the effective interval, evaluating the penalty clause triggering risk of the long contract under the condition that the maintenance window landing position is not adaptive; using the decision tree algorithm to integrate the contract period length and the penalty clause triggering risk, combining the unit price discount level of each contract to determine the candidate scheme; for the candidate scheme, determining the scheme to be excluded according to the centroid month and the guaranteed power threshold matching result to obtain the recommended candidate set after preliminary screening; the application can effectively reduce the penalty risk under the long contract, and improve the feasibility of the user electricity purchasing decision.

The invention discloses a decision analysis method and system for a standard material purchasing system, and relates to the technical field of purchasing decision, and the method comprises the steps: collecting a historical material purchasing data flow, extracting a sum value, a supplier credit rating, and logistics cost, and combining the sum value, the supplier credit rating, and the logistics cost into a basic feature set; carrying out big data analysis attenuation modeling on the aging attribute of the basic feature set, establishing a hyperbolic attenuation model and outputting a dynamic attenuation factor; mapping the basic feature set into a coordinate vector, inputting the coordinate vector and a dynamic attenuation factor into a Lorentz compression engine, and compressing and storing the coordinate vector and the dynamic attenuation factor into a quotation memory bank; and converting the purchasing scheme into an electronic order, distributing the electronic order to a supplier platform, collecting performance time delay data, and calibrating a dynamic attenuation factor to complete a closed loop. According to the method, dynamic modeling is carried out on the aging attribute of the basic feature set through the hyperbolic attenuation model, and the dynamic attenuation factor is enabled to be adaptive to the market environment change by using a nonlinear coupling mechanism of the market fluctuation rate and the logistics cost drift factor.

If the price of a financial product falls, holders of that financial product will be able to purchase that financial product. [Solution] The information processing device 1 of this embodiment has a memory unit 12 that stores a financial portfolio including constituent financial products in association with a user, a setting acceptance unit 131 that accepts from the user a set purchase amount used to determine the purchase amount of additional financial products, which are constituent financial products to be purchased when the prices of the constituent financial products fall by more than a predetermined decline range, and a purchase decision unit 134 that decides to purchase additional financial products in an amount based on the set purchase amount accepted by the setting acceptance unit 131 when the latest price of a target financial product, which is a constituent financial product included in the financial portfolio associated with the target user, falls by more than a predetermined decline range compared to the base price of the target financial product at a past reference point.

The present application relates to the technical field of coal purchasing decision support, and specifically discloses a coal purchasing decision method, system and device based on a large language model, the coal purchasing decision method based on the large language model comprising: obtaining target data; inputting the target data into the large language model to output a coal purchasing basic data analysis result; according to the coal purchasing basic data analysis result, further analyzing through the large language model to generate a fuel purchasing strategy direction suggestion; according to a strategy direction selected by a user, constructing a multi-objective optimization model and solving the same to obtain an optimal combination of a purchased coal type, quantity and purchasing route; and according to the optimal combination of the purchased coal type, quantity and route, generating a customized coal purchasing scheme through the natural language generation capability of the large language model. The embodiments of the present application realize the intelligentization, scientification and high efficiency of coal purchasing decision, and provide strong support for reducing costs, improving efficiency and ensuring safety for thermal power enterprises.

We provide the system. [Solution] A means of acquiring images of the user using a video acquisition device, A means for identifying a face and extracting its appearance based on acquired image data, A means of presenting a style suitable for the user using an AI model via an information processing device, based on extracted appearance data, A means of virtually applying the presented style to an image in real time and providing it to the user using a display device, A means of presenting product information related to the selected style and providing a purchase interface, A means of providing an interface that enables users to make purchase decisions based on the presented virtual style, A system that includes this.

We provide the system. [Solution] The information processing device provides a means for receiving product information entered by the user, A means for obtaining product-related data from multiple information sources based on the aforementioned product information, A means for analyzing acquired data and performing price comparisons and summarizing reputation information, A means of presenting analysis results to users and supporting their purchasing decisions, A means of extracting and summarizing the content of text data using natural language processing technology, A means of displaying analysis results in an easy-to-understand visual format, A system that includes this.

The application provides an intelligent marketing method and system driven by an ai large model, which comprises the following steps: extracting a behavior feature vector based on the micro interaction behavior of a user by using a multi-scale behavior mode coding network; extracting the core marketing interest of the user by using an adaptive multi-layer interest clustering, and generating a behavior label; analyzing the real influence of different marketing strategies on the purchase decision of the user by using double-layer causal reasoning, and outputting an individualized marketing effect label; scoring the applicability of the marketing strategy based on the individualized marketing effect label, and optimizing the recommended strategy by using a reinforcement learning framework; pushing the individualized marketing content to the user according to the individualized marketing strategy distribution result, and optimizing the recommended strategy by using a dynamic closed-loop feedback optimization model based on the real-time feedback of the user. The application overcomes the limitations of the existing precision marketing technology by introducing innovative technologies such as fine-grained behavior analysis, causal reasoning optimization and dynamic marketing strategy adjustment, and improves the precision of the recommendation and the marketing ROI.

Embodiments of the present application disclose information display methods and apparatuses and electronic devices, including: in response to a request for displaying a target object in an augmented reality (AR) mode, loading a three-dimensional model corresponding to the target object, and mapping the three-dimensional model to an AR space for display, the target object being a handheld device type object with a display screen; detecting an interaction operation performed by a user on the three-dimensional model in the AR space, the interaction operation being used to simulate an operation related to starting a target function in the target object; and displaying interface content corresponding to the target function in a region of the three-dimensional model where the display screen is located according to the detected interaction operation. Through the embodiments of the present application, a user can be provided with an experience closer to that in an offline purchase or trial process of a real product, which is conducive to helping the user make a purchase decision.

Embodiments of the present application disclose a method for processing user review information of a product object, and an electronic device. The method includes: in response to a request from a target user to view user review for a target product, generating target text content by inputting a plurality of user reviews associated with the target product into a target algorithm model for processing, where the target text content is used for providing a summarizing description of the plurality of user reviews; and returning the target text content to a client, so as to display the target text content when displaying the user review information. Through embodiments of the present application, it is possible to help a user more efficiently achieve a more complete and comprehensive understanding of user review, thereby enabling the user review to more effectively help the user make purchasing decisions.

The invention discloses a commodity searching and sorting method based on purchase data, relates to the technical field of commodity searching and sorting, and solves the problems that a traditional sorting method is static and one-sided in value evaluation, is disjointed with a real-time market supply and demand state, and lacks quantitative avoidance of supply chain multi-dimensional risks. According to the method, the supply continuity risk, the quality and performance risk and the price fluctuation risk are brought into the final sorting calibration link, and the right of high-risk commodities is systematically reduced through the risk penalty term, so that the final sorting list pursues the value and opportunity, and meanwhile, suppliers with high commodity shortage risk, high after-sales problems or unstable cost can be automatically avoided, and the efficiency of sorting the commodities is improved. Therefore, the long-term robustness of the purchase decision and the safety of the supply chain are greatly improved.

The invention discloses an AI-based fixed asset full life cycle procurement decision-making system, which comprises a model initialization module used for constructing an operation causal map based on multi-source heterogeneous data; the combination optimization module is used for generating a plurality of purchase combinations formed by different to-be-purchased assets based on a preset total budget constraint and at least two optimization targets; the simulation deduction module deduces the effective utilization level and the influence effect of each combination on the causal map; the decision support module is used for determining and outputting a Pareto frontier based on the plurality of purchase combinations and the corresponding overall influence effect, and the Pareto frontier displays a non-dominated solution set between optimization targets; and the model evolution module is used for adaptively updating the structure of the hospital operation causal atlas. According to the method, the dynamic and multi-dimensional influence after asset introduction can be quantitatively predicted, and systematic tradeoff is carried out among a plurality of conflict targets, so that the scientificity and foresight of purchase decision making are improved.

The invention provides an electricity purchase decision-making method and system for multi-time scale rolling optimization of an electricity selling company, and belongs to the field of electricity market transaction. The method comprises the following steps: constructing a decision framework including an annual transaction layer, a monthly transaction layer and an intra-monthly transaction layer; in the annual transaction layer, establishing an annual transaction decision model with the goal of minimizing the comprehensive electricity purchase cost, and determining the annual contract electricity purchase quantity; in the monthly transaction layer, establishing a monthly transaction decision model considering the CVaR risk, and optimizing monthly contract electricity purchase quantity distribution; in the intra-month transaction layer, establishing an intra-month transaction decision model, and dynamically adjusting the contract position; through a time-phased decomposition curve mechanism, contract electric quantity of each level is decomposed step by step and conducted to a spot market, and dynamic coupling of medium and long term transaction and the spot market is realized. According to the method, dynamic coupling of the position electric quantity and the spot price is realized, the electric power medium and long term transaction rules are accurately adapted, cross-cycle deviation cost accumulation caused by curve decomposition is effectively avoided, and decision scientificity is improved.

The invention relates to the technical field of virtual clothing recommendation, and particularly discloses a virtual fitting clothing recommendation method and system based on artificial intelligence, and the method and system achieve the extraction and unified representation of clothing essential visual attributes through the fusion of a commodity standard image and a user real-shot image and the establishment of a cross-domain feature decoupling and alignment mechanism. The problem of inconsistent garment appearance caused by different shooting conditions is fundamentally solved. By constructing the environment independent feature model and the domain invariant feature space, the system can accurately measure the visual similarity of clothes in different source images, and the reliability of a recommendation result and the user credibility are remarkably improved. Accurate matching of the recommendation result and high-fidelity presentation of the virtual fitting effect are achieved, the fitting effect of the clothes and the individual form can be accurately captured before the user makes a purchase decision, and early recognition and visual satisfaction of personalized requirements are achieved.