125 results about "Purchasing decision" patented technology

The invention, which relates to the technical field of medicine inventory management, discloses an intelligent medicine inventory management and optimization system comprising a demand prediction module, an inventory monitoring module, a purchase decision module and a medicine supply module. The demand prediction module is used for performing drug demand prediction according to historical sales data, seasonal factors and market trends; and analyzing and predicting by adopting a machine learning algorithm. According to the intelligent medicine inventory management and optimization system, based on a multi-objective optimization algorithm, in combination with the inventory turnover rate, the purchase cost and the medicine validity period, a scientific and reasonable purchase replenishment strategy is generated, including purchase time, quantity and supplier selection, and the scientificity and the accuracy of a purchase decision are improved; by integrating historical sales data, seasonal factors and market trends, demand prediction is performed by using a machine learning algorithm, and an accurate drug future demand trend prediction result can be generated.

The invention relates to the technical field of food storage management, in particular to a food storage management system, which comprises a data acquisition layer, is composed of a plurality of devices and is used for acquiring food, environment and user operation data; the data transmission layer transmits data by using wired and wireless network technologies; the data storage layer stores various types of data in combination with a relational database and a non-relational database; the data processing layer comprises a plurality of functional modules, such as an intelligent warehousing management module, and is used for realizing automatic input verification and batch management of food information; the intelligent ex-warehouse management module is used for optimizing ex-warehouse sequence and tracking food directions; the expiration and waste management module is used for carrying out expiration reminding and surplus value evaluation; the data analysis and purchase optimization module is used for predicting demands and analyzing loss; and the dish and inventory association module is used for matching the dish formula and the inventory and predicting the sales allocation inventory. The system improves the management efficiency, reduces the waste, optimizes the purchase decision, enhances the dish and inventory association management, guarantees the system safety, and provides data support to assist the decision.

Systems and methods are provided for offering tenants the ability to purchase commoditized network resources in a next-generation, large-scale wireless communication network (e.g., a 5G or 6G cellular network) according to dynamic pricing. For example, the network can update pricing of commoditized network resources intelligently and dynamically based on present and / or predicted supply and / or demand of the network resources. The updated pricing is offered to tenants on a periodic or other basis, and the tenants can decide whether to purchase more or less of those resources based on the updated pricing. The network can adjust resource allocations to the tenants based on the updated purchase decisions. The network can also automatically adjust QoS parameters for the tenants to fit within their purchased resource allocations.

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 an intelligent commodity management method, and relates to the technical field of commodity management. Through the design of the whole method, the efficiency can be improved, the intelligent system can automatically manage a plurality of goods management tasks such as inventory management, purchase decision making and demand prediction, manual intervention is reduced, the management efficiency is improved, inventory optimization is facilitated, the intelligent system can accurately predict demands through data analysis and prediction algorithms, and the management efficiency is improved. The inventory level is optimized according to the prediction result, the inventory overstock or stockout phenomenon is avoided, the inventory turnover rate is improved, the cost is reduced conveniently, the intelligent system can help enterprises to optimize purchasing strategies and reduce the inventory cost, the purchasing cost is reduced through price optimization and other modes, and therefore the overall cost of the enterprises is reduced, and the enterprise experience is improved. The customer satisfaction can be improved conveniently under the overall application, and the customer satisfaction can be improved and the customer loyalty can be enhanced by meeting customer requirements in time, accurately predicting the market trend and providing personalized services and intelligent commodity management.

The invention belongs to the field of block chains, and discloses an application method and system of a smart contract in college purchase and multi-supplier settlement, and the method comprises the steps: deploying a block chain network and the smart contract; future purchase demands and market price fluctuations are predicted based on artificial intelligence, a decentralized autonomous organization mechanism is introduced, and multiple parties participating in the purchase process are allowed to participate in making and execution of purchase decisions at the same time; initiating a purchase demand, automatically triggering a purchase contract after internal auditing, generating a purchase order, and sending the purchase order to a supplier; the supplier delivers the article and the college accepts the article; the intelligent contract automatically judges whether settlement conditions are met or not, and if yes, settlement operation is executed; the system adjusts parameters and rules of the smart contract in real time; related data of suppliers are collected regularly, credit evaluation and feedback are carried out, and various data are monitored and analyzed in real time. According to the invention, the transparency of purchasing and settlement is improved, the purchasing and settlement process is simplified, the management cost is reduced, the supplier management is optimized, the data analysis and decision-making capabilities are improved, and the digital transformation is promoted.

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 provides a hospital purchase cost dynamic control method and system, and the method comprises the steps: converting the obtained data of a to-be-purchased article set and a supplier set into structural data, constructing a space-time diagram of the to-be-purchased article set and the supplier set, extracting the node features and edge features of the space-time diagram, and calculating the purchase cost of the to-be-purchased article set and the supplier set; embedding a time sequence into the graph node features and the edge features, obtaining an influence factor of time change on a purchase decision, constructing a time-space diagram of historical purchase data, supplier set data and time sequence data, training a time-space diagram network model, and completing training of the time-space diagram network model; the node features and the edge features of the space-time diagram serve as input of a training completion model, the purchasing number and the purchasing time of each kind of to-be-purchased goods are obtained, and a purchasing strategy is determined based on supplier set information meeting the purchasing number and the purchasing time; based on the change information of the to-be-purchased article set monitored in real time, the purchase strategy is dynamically adjusted to control the purchase cost, the dependence on manual intervention is reduced, and the efficiency and accuracy of purchase cost decision making are improved.

A user review information processing method for a commodity object, and an electronic device. The method comprises: in response to a request of a target user for viewing user review content with regard to a target commodity, inputting a plurality of pieces of user review content associated with the target commodity into a target algorithm model for processing, so as to generate target text content, wherein the target text content is used for performing inductive and conclusive expression on the plurality of pieces of user review content; and returning the target text content to a client, such that the target text content is presented when the user review information is presented. Therefore, a user can be helped to more efficiently realize more complete and more comprehensive understanding of user review content, such that the user review content can more effectively help the user to make a purchasing decision.

The invention provides a smart purchase decision-making method and decision-making platform based on electric power big data, and the method employs a target decision-making analysis algorithm obtained through multi-task training, and achieves the accurate analysis of a target decision-making result of the target electric power big data. And extracting an extraction feature array of the target electric power big data by adopting a first machine learning algorithm, wherein the extraction feature array is a feature information expression of the system working situation of the target electric power big data. Besides, a second machine learning algorithm is adopted to extract a contrast feature array of the target electric power big data, a third machine learning algorithm is adopted to extract an associated feature array of the target electric power big data, and the contrast feature array and the associated feature array are perfect features of the target electric power big data. Thus, after the extraction feature array, the contrast feature array and the associated feature array are combined, a decision operator in a target decision analysis algorithm is adopted to analyze a combined target combination array, and a decision result of the target electric power big data is obtained.

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.

The invention relates to the technical field of report generation, and discloses an artificial intelligence and big data analysis-based evaluation report generation system, which comprises a data acquisition module, a preprocessing module, an emotion analysis unit, a commodity analysis unit and a report generation unit, according to the invention, the data acquisition module obtains multi-dimensional data, lays foundation for analysis, and ensures comprehensiveness and objectivity; the preprocessing module extracts keywords by using a specific algorithm, accurately extracts key contents, and facilitates the development of subsequent work; the sentiment analysis unit analyzes the sentiment tendency by means of a sentiment dictionary and the like, can clearly present the public praise of the consumer to the commodity, and assists decision adjustment; the commodity analysis unit analyzes commodity attributes in combination with word frequency and information entropy, calculates key attribute word scores and emotion tendency correlation degrees, and provides a basis for commodity optimization; the report generation unit generates a report containing key content, and has important reference value for merchants to improve products and formulate strategies and consumers to make purchase decisions.

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.