98 results about "Purchasing decision" patented technology

The invention discloses a chain drugstore purchasing scheme generation method and system based on big data, and relates to the field of purchasing management. The method comprises the following steps: firstly, collecting historical sales data, real-time inventory data and external environment data, and constructing a multi-dimensional database; performing standardization processing on the data, and constructing a medicine classification matrix through a data mining model; identifying key factors influencing drug requirements by using a causal analysis method, and establishing a requirement influence factor weight matrix; based on the weight matrix, calculating purchase parameters such as a safe inventory level, an economic purchase batch, a purchase trigger point and a purchase period; and finally, in combination with a supplier evaluation system, generating a purchasing scheme including a purchasing medicine list, a purchasing quantity, purchasing time and supplier selection. According to the method, through multi-dimensional data analysis and causal relationship mining, scientization and accuracy of purchasing decision are realized, the purchasing efficiency is improved, and the inventory cost is reduced.

The invention discloses a purchase management decision support system driven by an AI large model, and particularly relates to the technical field of intelligent purchase management. A data acquisition module obtains historical and current purchase, market price, inventory, sales and customer feedback data of commodities; the clustering analysis module divides purchasing groups based on inventory states and market prices; the self-portrait construction module generates a comprehensive self-portrait through client feedback data; the market analysis module analyzes sales data and predicts future demand changes; the optimization adjustment module optimizes the purchase quantity in combination with demand prediction, customer preferences and purchase group data, so that the accuracy and efficiency of purchase decision making are improved; through intelligent data processing and model prediction, the limitation of dependence on experience and static rules in traditional purchase management is avoided, fluctuation of market demands can be responded in real time, the purchase quantity is ensured to be matched with the market demands, inventory overstock and shortage phenomena are avoided, and customer satisfaction and market competitiveness are improved.

The invention discloses a purchase management method and system based on business big data, and relates to the technical field of purchase prediction and supplier management, and the method comprises the steps: collecting historical purchase records of a business database, carrying out the preprocessing, generating a preprocessing data set, and constructing and training a purchase prediction model through a long short-term memory (LSTM) network algorithm; the purchase demand is predicted through the purchase prediction model, a demand prediction result is output, and the suppliers are graded through the demand prediction result to form graded suppliers; and generating a purchase suggestion list through the graded suppliers, and sending the purchase suggestion list to the purchase management terminal. According to the invention, the purchase prediction model based on the multilayer LSTM neural network is established, so that the long and short term characteristics of the purchase data are effectively captured and accurately predicted; and in combination with the purchase decision matrix and the differentiation processing strategy, the system automatically generates purchase suggestions and pushes the purchase suggestions to the management terminal through the encryption channel, so that the automation level and the operation efficiency of enterprise purchase management are effectively improved.

The invention discloses an asset use analysis method and system based on an intelligent prediction and optimization algorithm, and aims to solve the problems of low efficiency, insufficient prediction capability and unscientific purchase decision in existing asset management. According to the invention, asset use records, maintenance information and purchase history are obtained through the data acquisition module; constructing an asset use prediction model by using time sequence prediction, regression analysis and machine learning algorithms, and predicting future asset demand quantity and use trend; generating an optimized purchase plan and an asset allocation scheme in combination with an inventory state, an asset life cycle and a budget constraint; and the prediction result and the optimization suggestion are displayed through a visual tool, and support is provided for decision making. According to the invention, efficient planning and purchase optimization of asset use are realized, and the method can be widely applied to the field of enterprise asset management and supply chain optimization.

The invention discloses an auxiliary decision-making method and system for regional coal market purchase, and belongs to the technical field of coal market purchase decision-making, and the method comprises the steps: building model input parameters through collecting the data of a fire coal demand plan of a power plant, the maximum supply of a supplier, a coal quality index, a purchase price and a transportation cost; constructing a mixed integer linear programming model taking the minimization of the total purchase cost as a target function, and setting a demand satisfaction constraint, a supply capability constraint and a coal quality standard reaching constraint; solving the mixed integer linear programming model by adopting a two-stage approximation algorithm; and converting the obtained approximate optimal solution into a visual purchasing scheme chart and report, and supporting a user to dynamically adjust the purchasing scheme through an interactive interface to generate a final purchasing decision. According to the invention, on the basis of comprehensively considering the coal quality, the purchase cost, the transportation mode and the supplier capability, the coal purchase plan is optimized, the cost minimization is realized, and the overall quality of coal purchase is improved.

The invention provides an ai large model driving-based intelligent marketing method and system. The method comprises the following steps of: extracting a behavior feature vector by adopting a multi-scale behavior mode coding network based on a micro interaction behavior of a user; core marketing interests of the users are extracted by adopting self-adaptive multi-layer interest clustering, and behavior tags are generated; analyzing the real influence of different marketing strategies on the purchase decision of the user by using double-layer causal reasoning, and outputting a personalized marketing effect label; the applicability of the marketing strategy is scored based on the personalized marketing effect label, and dynamic recommendation strategy optimization is carried out by adopting a reinforcement learning framework; and pushing personalized marketing content to the user according to a personalized marketing strategy distribution result, and optimizing a recommendation strategy by adopting a dynamic closed-loop feedback optimization model based on real-time feedback of the user. According to the method, innovative technologies such as fine-grained behavior analysis, causal reasoning optimization and dynamic marketing strategy adjustment are introduced, so that the limitation of an existing precision marketing technology is overcome, and the recommendation precision and the marketing ROI are improved.

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 provides a method for constructing a cigarette brand analysis model, which comprises the following steps of: acquiring combustion speed data and sales data of different brands of cigarettes, and constructing an associated data set of combustion speed and consumption frequency through data preprocessing; combining the prediction result of the combustion speed and the influence coefficient to establish a cigarette demand prediction model, and judging the demand trend and purchase quantity distribution of cigarettes with different combustion characteristics in a future period of time; based on the prediction result of the demand trend and purchase quantity distribution of the cigarettes in a future period of time, adopting a multi-objective optimization algorithm to construct a purchase decision optimization model, and solving the model to obtain the optimal purchase quantity and purchase opportunity of each type of cigarettes; and regularly monitoring the consumption behavior change and competing product dynamic state of the cigarette market, updating the data set and adjusting the prediction model.

The invention provides a transaction service method and system based on voice recognition, and relates to the technical field of automatic ticketing. The method comprises the following steps: receiving a voice instruction of a user, converting sound waves into digital signals by adopting a signal processing technology, and converting voice into texts to generate a voice text conversion result. According to the invention, through keyword extraction and intention analysis, the demands of the user, such as budget and lottery ticket types, can be analyzed more accurately, purchase parameters better meeting the demands of the user can be configured, a linear programming tool is used to optimize the time configuration of a purchase process, waiting and processing time is reduced, transaction efficiency is improved, and by analyzing a lottery ticket purchase strategy, the user experience is improved. According to the method, the expected value is calculated, and the optimal purchase strategy is recommended, so that the user can make a more intelligent purchase decision according to the expected income, the potential of return on investment is improved, the reachability and personalization of the service are greatly enhanced, and more convenient and safer ticket purchase experience is provided.

The invention discloses a transport capacity purchase abnormity monitoring and early warning method, and relates to the technical field of logistics transportation, and the method comprises the following steps: collecting original data needed by logistics purchase through a multi-source data collection system, and carrying out the preprocessing of the collected original data, including data cleaning, integration, standardization and feature extraction; according to the method, historical transportation records of predetermined transportation capacity are called and verified through the Internet of Things technology, risk prediction and early warning are carried out in combination with an AI algorithm, deep mining and analysis are carried out on data in the purchasing process, potential problems are identified, improvement measures are given, and data support is provided for purchasing decision making in combination with the big data warehouse. According to the method, support is provided for data analysis in the transport capacity purchasing process through a big data warehouse, a provider transfers and provides historical transport records and real-time state data of predetermined transport capacity to a buyer from two stages, and an AI algorithm can comprehensively carry out risk prediction and early warning on a transport capacity purchasing decision.

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 material purchasing management system. The system comprises a supplier management and evaluation module; the system is responsible for collecting and analyzing supplier basic information, historical performance, quality evaluation, after-sales service data structured data, social media data and industry news unstructured data in real time, and dynamically evaluating suppliers. The supplier management and evaluation module transmits an evaluation result to the purchase order and quality standard docking module and the product quality verification and detection module; and the product quality verification and detection module is responsible for carrying out intelligent quality verification and detection on samples or preliminary batches provided by the recommended suppliers before purchase decision making by adopting the Internet of Things technology and automatic detection equipment. The product quality verification and detection module carries out quality verification and detection on samples or preliminary batches provided by recommended suppliers before purchase decision making, it is ensured that products meet the set quality standard, and subsequent problems caused by substandard product quality are effectively avoided.

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 invention discloses a smart retail recommendation method and system based on graph decoupling representation learning, and belongs to the technical field of smart city recommendation. Aiming at the problems that a current smart retail recommendation system cannot effectively capture complex purchase decisions and behavior changes of consumers and is difficult to meet personalized requirements, the method comprises the following steps: constructing a hypergraph according to user session behavior data, and embedding and constructing an intention hyperedge by taking multiple intentions of a user as commodity nodes to form a complete user behavior hypergraph structure; based on a user behavior hypergraph structure, multiple intentions behind user behaviors are analyzed from microscopic and macroscopic angles, then preference scores of all commodities are calculated for prediction, and thus a smart retail recommendation model is established; and designing a joint optimization objective function, carrying out model training on the objective function to realize recommendation of the smart retail, and providing a recommendation result to the user and a retail platform merchant for use.

The invention discloses a purchase information processing method and device and computer equipment. According to the method, the formatted purchasing scheme template and the information needing to be provided by the user can be combined to generate the purchasing scheme, the scheme conforming to the target purchasing scheme attribute can be quickly generated, the tedious process of compiling the scheme from the beginning is avoided, part of content can be dynamically adjusted according to the actual requirement, and the procurement efficiency is improved. And the flexibility and the universality during scheme creation are optimized. Moreover, the purchase items with similar characteristics can be classified into one group, and the schemes belonging to different integrated item groups are displayed in a differentiated manner, thereby facilitating the user to compare the differences between different item groups from the perspective of groups. In addition, the advantages and disadvantages of the purchasing scheme can be found according to the comparison items, the user can quickly position the difference items, and the quality of the purchasing decision is improved.

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 present invention relates to the technical field of report generation, and discloses an evaluation report generation system based on artificial intelligence and big data analysis, comprising a data acquisition module, a preprocessing module, a sentiment analysis unit, a commodity analysis unit, and a report generation unit; in the present invention, the data acquisition module acquires multi-dimensional data to lay a foundation for analysis and ensure comprehensiveness and objectivity; the preprocessing module extracts keywords with a specific algorithm and accurately refines key contents, which is conducive to subsequent work; the sentiment analysis unit analyzes sentiment tendencies with the help of sentiment dictionaries, etc., can clearly present consumers' word-of-mouth on commodities, and assist in decision-making adjustments; the commodity analysis unit analyzes commodity attributes in combination with word frequency and information entropy, calculates key attribute word scores and correlations with sentiment tendencies, and provides a basis for optimizing commodities; the report generation unit generates a report covering key contents, which has important reference value for merchants to improve products and formulate strategies, and for consumers to make purchase decisions.

To provide a game system or the like for providing a person considering purchase with useful information referenced for judging purchase so as to activate transactions in a market place or the like regarding content (NFT) created by a user in a game.SOLUTION: In a game, content (NFT) created by a user can be contributed. In a game, contributed content is evaluated by other users. Evaluation information is included in meta data of NFT corresponding to the content.SELECTED DRAWING: Figure 14

The invention discloses a multi-modal information-based user purchase intention prediction method and apparatus. The method comprises the steps of obtaining a user purchase behavior information set; the user purchase behavior information set comprises a collected behavior information set related to a purchase decision in the commodity purchase process of the user and a purchase decision information set of the corresponding user; each behavior information set has corresponding purchase decision information; performing association processing on the behavior information set and the purchase decision information set to obtain a user purchase intention prediction model; and processing the acquired user behavior information by using the user purchase intention prediction model to obtain a user purchase intention prediction value. According to the method, the key factors for determining the purchase decision of the user are obtained through extraction, the user purchase intention prediction model is established, and a quantitative basis is provided for improving the offline commodity sales volume.

The invention discloses a data-driven material demand analysis and prediction method considering uncertainty, and the method comprises the following steps: (1) dividing business expansion materials into continuous demand materials and intermittent demand materials based on historical demand data; (2) respectively carrying out k-means clustering analysis on the continuous demand materials and the intermittent demand materials; (3) according to the clustering result, a time sequence model and a deep learning model are selected for each subclass of materials for demand prediction, the time sequence model is an autoregressive integral moving average model ARIMA, and the deep learning model is a long short-term memory network LSTM; comparing prediction effects of different models through evaluation indexes, selecting an optimal prediction result as a final demand prediction value, and applying the result to a material purchasing decision; according to the invention, a scientific and reasonable decision basis is provided for business expansion project material purchase management.

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.