539 results about "Purchasing" patented technology

The invention provides a procurement supply chain collaborative intelligent management method and a procurement supply chain collaborative intelligent management system. Belongs to the technical field of supply chain management. Constructing a purchase demand prediction model; dynamically adjusting the purchasing strategy according to the prediction result; the record and the evaluation result are stored on the block chain; the system automatically sends order information to a supplier; the supplier confirms the order through the block chain platform and updates the production progress and the logistics information; the system adjusts an inventory strategy in real time; the logistics state is monitored in real time; the system analyzes data of each link of the supply chain in real time and identifies potential risks. Through dynamic path planning and real-time transportation optimization, the logistics path can be adjusted in real time according to traffic conditions, environmental factors and emergencies, the transportation time is shortened, the transportation cost is reduced, and therefore the overall efficiency of a supply chain is remarkably improved.

The invention provides a supply chain prediction method based on a knowledge graph and an industrial model, and the method comprises the steps: collecting the features needed by supply chain prediction, and the needed features comprise historical order data and production plan data, industrial product purchasing data and collection information, logistics data and storage data; performing data cleaning, standardization and correlation analysis on the historical order data, the production plan data, the industrial product purchasing data, the collection information, the logistics data and the storage data to obtain target features; key entities and relationships in the target features are identified, and a knowledge graph is constructed based on the key entities and the relationships; the industrial model is used for identifying supply chain key nodes and node relations in the knowledge graph, the supply chain key nodes and the node relations are converted into numerical feature vectors used for supply chain prediction so as to carry out supply chain prediction, the relations among the nodes in the supply chain can be better captured by introducing the knowledge graph and the industrial model, and the supply chain prediction accuracy is improved. Therefore, the supply chain prediction precision is effectively improved.

The invention provides a financial analysis method and system based on big data, and the method comprises the steps: analyzing the dependency relationship between a non-standard code combination and a fuzzy text mode in a material purchasing scene based on a credible rule library, extracting a new attribute combination feature, and forming an optimization rule feature set if the new attribute combination feature can improve the matching accuracy; performing rule-based key field and attribute set verification matching on the real-time non-standard transaction data according to the optimization rule feature set in combination with a dependency relationship between a non-standard code combination and a fuzzy text mode, and outputting a matched transaction record set; and comparing the difference between the matched transaction record set and the historical manual matching record, extracting and judging whether the newly added non-standard attribute combination is applicable to the non-standard transaction data which is not covered or not after a matching rule is adjusted by adopting an incremental learning algorithm, and if so, forming a dynamic matching table.

The invention discloses an enterprise purchase collaborative management method and system based on a cloud platform, and relates to the technical field of cloud platform and supply chain management, and the method comprises the steps: collecting multi-factor demand prediction model data; predicting a purchase demand and performing optimization; and executing purchasing based on the demand prediction and optimization result. According to the enterprise purchase collaborative management method based on the cloud platform, purchase demand prediction is carried out through a multi-factor dynamic time sequence model, high precision and high adaptability of demand prediction are realized in combination with a dynamic weight adjustment mechanism and adaptive optimization of model parameters, a purchase demand trend chart and influence factor analysis can be provided, and the enterprise purchase collaborative management method based on the cloud platform can be applied to enterprise purchase collaborative management. According to the method and the system, the enterprise is helped to make a purchase plan in advance, the purchase cost, the inventory holding cost and the stockout risk are balanced by taking the minimization of the total purchase cost as the target through the demand-inventory joint optimization model, and the method and the system have better effects in the aspects of accuracy, controllability and stability of enterprise purchase.

The invention relates to the technical field of information, and provides a purchase abnormity monitoring and fraud early warning method and system based on behavior analysis, and the method comprises the steps: obtaining the position and authority information of a purchaser, setting different purchase amount thresholds and approval limits according to authority levels, building respective behavior mode baselines for the purchaser at different positions, meanwhile, purchasing mode characteristics of each purchaser are identified based on historical purchasing records of the purchasers; purchasing behaviors of purchasing personnel are monitored in real time, the purchasing behaviors are compared with a personal historical purchasing mode and a general purchasing mode of a team where the purchasing behaviors are located in combination with personal purchasing mode characteristics and behavior mode baselines, and if abnormity obviously deviating from the baselines or the team modes is found, the purchasing behaviors are marked as suspected abnormal purchasing behaviors; key attributes of suspected fraud orders are extracted, the key attributes comprise order amount, supplier information and purchasing time, and whether associated orders appearing for multiple times by the same supplier in similar time exist or not is analyzed;

The invention discloses an automatic price inquiry and comparison supply management system based on the price and address of a supplier, particularly relates to the field of price inquiry and comparison supply management, and is used for solving the problems of geographic information lag and insufficient matching between the supplier and a purchasing unit in the existing traditional supply chain management. The method comprises the following steps: analyzing supplier addresses into standardized geographic information, carrying out clustering and region division on suppliers based on geographic distribution, retrieving a region where a purchasing unit is located, associating the region with the position of a related supplier, defining a purchasing mode, generating a multi-dimensional logistics network topological graph, and carrying out real-time optimization and evaluation on a transportation path. The supplier price trend is dynamically predicted based on historical price data and market fluctuation, a prediction model is adjusted in combination with real-time data, and an optimal supplier list is generated according to a preset purchase scene and a multi-dimensional constraint condition, so that accurate and efficient supply chain decision is realized.

The invention relates to the technical field of supply chain management data analysis, and discloses a supply chain management data analysis system and method based on machine learning, and the system comprises a supplier object search module, a supplier object service quality analysis module, and a supplier object selection module. The method comprises the following steps: accurately searching a qualified historical supply quality number of a material purchasing target supplier based on target supplier object parameters in combination with an intelligent search algorithm and supplier historical supply behavior information, and accurately analyzing a historical supply quality behavior completion result of the material purchasing target supplier in combination with numerical analysis; supplier service level accurate data analysis is performed by adopting an intelligent search algorithm in combination with the historical supply quantity of a material purchasing target supplier, quality behavior analysis parameters and different supply behavior supplier service level parameters, so that enterprise supply chain supplier service quality dynamic and accurate analysis is realized; and the intelligence and quality of enterprise supply chain management are improved.

The invention discloses an intelligent logistics supply chain management system, and the system comprises a block chain information sharing platform which is responsible for building an information sharing platform based on a block chain, inviting all participants on the supply chain to join, and storing and transmitting the data of each link of the supply chain through the distributed account book technology of the block chain; the product information tracing system is responsible for distributing a unique block chain ID for each fresh food and recording the circulation process of the fresh food in a supply chain; and the collaborative management platform is responsible for integrating and uniformly managing data of purchasing, warehousing, transportation and sales, predicting future sales trend and inventory demand change by using a random forest according to historical sales data and market trend information, and optimizing an inventory level, a reorder strategy, a sales strategy and a promotion activity plan in combination with a genetic algorithm, so as to achieve the purpose of sales promotion. The inventory cost is minimized, the sales demand is met, and the block chain information sharing platform and the collaborative management platform carry out data synchronization.

An autonomous futures contract orchestration platform includes a set of processors programmed with a set of non-transitory computer-readable instructions. The instructions include receiving, from a data source, an indication associated with a product that relates to an entity that purchases or sells the product. The instructions include predicting a baseline cost of purchasing or selling the product at a future point in time based on the indication. The instructions include retrieving a futures cost, at a current point in time, of a futures contract for an obligation to the purchasing or selling the product for delivery or performance of the product at the future point in time. The instructions include executing a smart contract for the futures contract based on the baseline cost and the futures cost. The instructions include orchestrating the delivery or performance of the product at the future point in time.

The invention discloses a university purchase performance supervision comprehensive management system and management method, and the system comprises a purchase plan management module which makes a purchase plan according to the actual demands of a university, and carries out the verification of the purchase plan; the purchase execution management module is used for issuing purchase information, inviting suppliers to participate in bidding, performing qualification auditing and reputation evaluation on the suppliers, recording and tracking a purchase negotiation process, and performing electronic signing and management on contracts; the performance acceptance management module is used for supervising performance conditions of suppliers and is responsible for recording and tracking an acceptance process; the performance supervision and evaluation module is used for carrying out regular evaluation on purchasing activities, forming a performance evaluation report, providing rectification measures for problems found in evaluation, and tracking rectification conditions; and the information query and statistics module is used for providing a function of querying related information of purchasing activities and carrying out statistics and analysis on the purchasing data.

A school food and beverage recognition and processing system includes a self-checkout system that attaches to a school meal line. The self-checkout system uses multiple cameras to obtain images of a student for identification and images of a meal, identify contents of the meal, and determine whether the meal qualifies for reimbursement through a health program or agency. In addition to determining meal reimbursement qualifications, the self-checkout system tracks the meals and items sold for inventory and purchasing purposes. The self-checkout system sends reports to the school or administration agency including reimbursement information, inventory information, and customer information. This information is used to update a student's prepaid school meal account and track transactions.

The invention discloses a management method and system for guaranteeing the purchase quality of spare parts of a nuclear power plant, and relates to the technical field of equipment reliability and maintenance, and the method comprises the steps: collecting and coding the information of nuclear power spare parts; before the spare parts are purchased, the qualification of the supplier is verified; a demand list is automatically generated in the purchasing stage according to the coding information of the spare parts; matching suppliers meeting conditions in a supplier database according to the content of the demand order, and generating a purchase order; the unique code of the safe and important spare part is collected, and an RFID label or a two-dimensional code label is pasted on the spare part; spare part codes and supplier information are checked by scanning RFID or two-dimensional code labels, it is ensured that the spare part information is consistent with a purchase order, and then the spare part information is put in storage. According to the invention, spare part purchasing quality process control is realized, technical clarification is greatly reduced, one-code multi-object problems are reduced, purchasing efficiency is improved, and counterfeiting is prevented, so that quality of safe and important spare parts is ensured, nuclear power plant equipment reliability is improved, and safe and stable operation of a unit is ensured.

Systems and methods for purchasing (typically under a factoring model but other models are still possible), on a selected basis, declined payment transactions; receive, for assessment, the failed payment transaction via API; enrich the data received for the failed transaction with external and internal data; define, via one or more predictive models, the expected cause of payment failure; define, via the one or more predictive models the probability of successfully process in the following days the transactions successfully, calculating the associated cost of risk; interact in real-time with the customer where needed; confirm to the party invoking the service, for each transaction sent for assessment, the purchase or not of the failed payment transaction.

A method for constructing an intelligent bid-invitation purchasing customer service system based on a retrieval enhancement generation technology comprises the following steps: constructing a multi-source knowledge base, and integrating and processing customer service work orders, bid-invitation files and laws, regulations and policies; non-sensitive metadata information is extracted through entity recognition to construct a knowledge graph, and a block chain is used for evidence storage; classified navigation is provided during user consultation, and metadata is complemented through brief interactive questions and answers; performing fusion retrieval through a dual-channel RAG architecture to generate answers; and continuously supplementing data and optimizing training. According to the method, the data security is guaranteed, the semantic comprehension capability is improved, the agile response to information change is realized, and the compliance and controllability of the system are enhanced.

The invention provides an internet right product agent management system, and relates to the technical field of product agent management. The system comprises a product management module, an order management module, an agent management module, a database and a data analysis module. The system processes on-off and stock of products through the product management module, the order management module calculates commission, and the agent management module achieves credit evaluation. The data analysis module predicts requirements through a multi-layer architecture; a data input layer integrates internal and external data; the analysis engine layer adopts feature coding and a time attention mechanism to model space-time correlation, and outputs a demand prediction formula; and the decision output layer generates an agent personalized report through inventory calibration, credit grading and regional weighting strategies, recommends products and early warns risks. The system realizes intelligent acquisition and sales decision making, and improves the operation capability and market influence of the system.

The invention discloses a building project material ordering optimization method and system considering supply interruption. The optimization method comprises the steps of obtaining a construction plan and material consumption; based on the construction plan and the material consumption, predicting a material demand within a preset time; obtaining a preliminary material ordering plan according to the material demand; obtaining an optimal material purchasing channel, and performing risk assessment according to the optimal material purchasing channel and the preliminary material ordering plan to obtain a supply risk; and obtaining an optimal material ordering plan according to the supply risk. According to the method, factors such as project progress, construction plans and material consumption are comprehensively considered, material demands in a certain period in the future are predicted, a material ordering plan is made according to a prediction result, the purchasing time, the purchasing quantity and the purchasing channels of materials are reasonably arranged, and the situation of material shortage or excessive purchasing is avoided.

The invention discloses a milling cutter service life management and control system based on a two-dimensional code, and relates to the technical field of machining. The cutter service life management and control center is in communication connection with a cutter service life management database, a binding end, an acquisition end, an unbinding end, a background data acquisition and analysis module, a cutter service life early warning module and a wear detection and service life correction module, and all the modules are in electric signal connection; and the tool life management database is used for storing related data of tools, including unique identification codes, initial residual life, use time, manufacturers, quantity and model data. According to the tool management system and method, the tools are managed in the whole life cycle, detailed data records are recorded in each link from warehousing, ex-warehouse, use to scrapping of the tools, enterprises can better master the use conditions of the tools, data support can be provided for purchasing, storage and the like of the tools, and the tool management process is further optimized.

To provide a system.SOLUTION: A system comprises: means for comprehensively collecting sales data, data for a market trend and data for customer's purchase patterns; means for analyzing and visualizing in real time the pieces of collected data; and means for predicting demand in the future and proposing an optimal purchasing plan utilizing AI technique.SELECTED DRAWING: Figure 1

The invention discloses a supplier material market price evaluation method and system based on AI networking search, belongs to the technical field of electronic commerce and data processing, and provides a double-weight confidence model based on time sequence fluctuation and statistical deviation, data are grouped according to platform types and suppliers, a price fluctuation oscillogram is generated for each group of data, and the price fluctuation oscillogram is calculated. Calculating average price and standard deviation in the group; the global average price is introduced, the confidence coefficient of a single group of data is dynamically quantified through a normalization formula, low weight is given to high-fluctuation and high-deviation data, noise interference is effectively suppressed, and the reference of the finally output optimized market price is improved; in addition, structured data with timestamps are generated and the grouping average price and confidence factor of each platform are synchronously output, so that purchasing personnel are assisted to transversely compare the quotation rationality of suppliers; and finally, the output optimized market price is fused with multi-source high-confidence data, so that enterprises are helped to accurately grasp the market price interval, and the bargaining capability and the purchasing efficiency are improved.

The invention relates to the technical field of insurance pushing, in particular to an insurance product intelligent pushing mode based on a deep neural network, which comprises a user portrait construction module, a causal reasoning module, a personalized pushing module and a feedback optimization module, and is characterized in that a user portrait is constructed through extracted features and a modeling result, and the user portrait is pushed to the user. The causal reasoning module is based on mass user data, determines a variable to be deduced and a conditional probability through the model establishment module, establishes a model for Bayesian reasoning, and calculates a posterior probability based on the Bayesian theorem through the calculation analysis module; and through the posterior probability, the client with the higher purchase intention for the insurance product can be analyzed and judged. According to the invention, on the basis of existing massive customer data, portrait customer characteristics and customer touch data are analyzed through customer purchase, and characteristics, demands and behavior preferences of customers are described through deep analysis, so that targeted sales strategies and high-quality services are provided for marketing personnel of a company.

The file is a patent document of an electric power material purchase and inventory optimization management system, the system is mainly introduced to comprise a material information management module, a warehouse-in and warehouse-out management module, an inventory monitoring and analysis module, a purchase demand grading module and an approval process generation module, and comprehensive optimization management of electric power material purchase and inventory is achieved. According to the system, basic information and historical purchasing data of electric power materials are imported, stored and updated through a material information management module; the warehouse-in and warehouse-out management module records warehouse-in and warehouse-out information of the materials and updates inventory data in real time; the inventory monitoring and analysis module monitors the inventory in real time and performs trend analysis, and sets a residual threshold to trigger a purchase application; the purchase demand grading module sets material purchase demands and emergency degrees according to historical purchase data and trend analysis results; and the approval process generation module automatically generates approval process lists of different levels according to the purchase emergency degree. The system improves the efficiency and accuracy of electric power material purchasing, and optimizes inventory management.

A system and method for generating personalized product recommendations for a user, along with the personalized reasons for the recommendation, which can then be used as part of a product purchasing system to deliver that product to the user. A system for image analysis may perform: obtaining an image of a face or head of a person, generating face features from the image, retrieving a plurality of features of a plurality of objects from a database, generating a plurality of combined face and object features from the face features and object features of the plurality of objects, generating a ranking of the combined face and object features, selecting at least one object based on the ranking of the combined face and object features, generating an interactive display of the selected at least one object, and providing user interaction of the interactive display to provide purchase of the selected object.

The invention relates to a text semantic matching method and system for judging the consistency of collection and sales contract subject matters, and belongs to the field of natural language processing. Comprising the following steps: extracting subject matter text data from purchase and sales contract files respectively, and preprocessing to construct purchase and sales contract data sets; constructing a contract subject matter consistency comparison model which is used for inputting purchasing and selling contract subject matter sample pairs and outputting a consistency judgment result of purchasing and selling contract subject matters; training the model based on a purchase and sales contract data set; and inputting the preprocessed to-be-tested procurement and sales contract subject matter text data into the trained contract subject matter consistency comparison model to obtain a consistency judgment result of the to-be-tested procurement and sales contract subject matter text data. According to the method, a twin network structure is adopted to carry out purchase and sales contract subject matter consistency judgment, and the problems that an existing purchase and sales contract subject matter consistency checking method is low in accuracy and efficiency and poor in adaptability are solved.

The invention discloses a material demand planning method and device, electronic equipment and a storage medium, and relates to the technical field of logistics transportation. The method comprises the following steps: acquiring description information and material demand information of at least one material forming a target product; the material demand information comprises the demand quantity of each material and corresponding supplier information and is used for indicating candidate suppliers for supplying the materials. And according to the material inventory information and the material demand information corresponding to the candidate suppliers, determining whether the material inventory of the candidate suppliers can meet the material demand of the target product. If not, a material purchasing plan for the target product is generated based on a preset model according to the description information of the at least one material and the material demand information, and the plan comprises the number of materials purchased from the candidate suppliers. Therefore, under the scene that the customer orders the product and appoints the supplier, the pre-constructed model can be utilized to automatically realize the material demand plan, the labor cost can be saved, and the efficiency can be improved.

The present application belongs to the field of computer technology, and specifically relates to a method and device for predicting the purchase of spare parts based on machine learning. The method includes: obtaining an inspection and maintenance work ticket within a preset time period; the inspection and maintenance work ticket includes information on the corresponding inspection and maintenance; when there is a replacement of spare parts in the corresponding inspection and maintenance process, the inspection and maintenance work ticket includes the model and consumption of the spare parts; semantically matching the inspection and maintenance work ticket with a pre-obtained spare parts function library; the spare parts function library includes the model of spare parts; when the matching degree between the inspection and maintenance work ticket and the spare parts function library is greater than a first preset threshold, the demand for the corresponding spare parts is determined according to the consumption in the inspection and maintenance work ticket. The present application establishes a spare parts procurement prediction model in combination with the inspection and maintenance work ticket, which can greatly improve the efficiency and turnover rate of spare parts management and rationalize spare parts inventory.

The invention provides a real-time commodity price prediction system based on multi-source data fusion, and the system comprises a data integration module which is used for obtaining multi-source heterogeneous data, carrying out the alignment processing of the multi-source heterogeneous data according to the commodity types and timestamps, and generating a standardized fusion data set; the dynamic weight distribution module is used for dynamically calculating the weight coefficient of each data source according to the relevance between each data source in the standardized fusion data set and the commodity category; the model integration module is used for performing weighted fusion on the price trend prediction result output by the time sequence prediction model and the game correction output by the market game model based on the weight coefficient to generate initial price prediction; and the optimization output module is used for performing periodic rule correction and strategy collaborative analysis on the initial price prediction according to the real-time market dynamic data to obtain a predicted commodity price trend. By adopting the system, the commodity price trend can be dynamically predicted based on multi-source big data, an enterprise is helped to optimize a purchasing strategy, and the inventory overstock risk is reduced.

The invention discloses a supply chain and e-commerce purchase field large model compression and online incremental learning method based on third-order combined distillation, and the method comprises the steps: collecting and preprocessing supply chain compliance documents and purchase order structured data, and obtaining a data set; constructing a teacher model and a student model, wherein each of the teacher model and the student model comprises an encoder, a decoder and a prediction head; performing three-stage knowledge distillation on the teacher model and the student model by using the data set; acquiring newly added data, judging whether the newly added data triggers an incremental learning condition or not, and if yes, performing incremental learning by utilizing a teacher model and a student model obtained by three-stage knowledge distillation to obtain a trained teacher model and a trained student model; and carrying out collaborative reasoning by utilizing the trained teacher model and student model to obtain a prediction result. Knowledge distillation is realized through a hierarchical migration protocol and multi-mode joint optimization, and data distribution drift problems such as e-commerce promotion, seasonal demands and supplier quotation floating are solved in combination with an online incremental learning mechanism.

The invention relates to an application that utilizes artificial intelligence to enhance project management in construction and repair industries. The system includes a user computing device, an application server, and a database server. The application server processes project data, including blueprints, to generate materials lists, integrates with online marketplaces to facilitate purchasing, and employs machine learning for product recommendations. The system also uses predictive analytics to forecast project deliveries and prevent backorders, while optimizing resource allocation through efficient supplier and material selection. The software application features an AI-powered chat, job planner, marketplace, and cart, providing a comprehensive platform for real-time collaboration among contractors, suppliers, and manufacturers. This innovation streamlines project workflows, reduces errors, and improves overall efficiency in managing construction and repair projects.

The embodiment of the invention provides an oil and gas service integration platform management system, the system comprises a distributed service platform and an oil and gas service integration platform, and the distributed service platform comprises a purchasing platform, a distribution platform, a warehouse logistics platform, a resource platform and a risk management platform; the purchasing platform is used for receiving a purchasing order of a purchaser, carrying out order integration and sending the integrated purchasing order to a corresponding supplier; the distribution platform is used for receiving a sales order of a supplier, searching a purchaser and sending the sales order to the purchaser; the warehouse logistics platform is used for matching a transportation party after detecting cargo delivery information, and sending cargo attributes to the transportation party; the resource platform is used for sending financial service information to a purchaser after detecting a non-full order of the purchaser, querying a financial service party and sending cooperation information to the financial service party; the risk platform is used for sending request information to a risk management party after receiving risk management requests of a purchaser and a supplier.

The invention relates to the technical field of financial data processing, in particular to a payment and settlement service system based on a big data technology, and the system comprises a data collection module, a user payment behavior recognition module, a payment channel evaluation module, and a payment execution and monitoring module. According to the invention, through deep analysis of transaction data and dynamic user portrait construction, consumption modes and preference trends of users can be captured and analyzed more accurately, and through extraction of periodic purchase behaviors and sudden change preferences, payment experience is highly personalized, customer service quality is greatly improved, payment channel assessment is comprehensive, and user experience is improved. The method includes real-time performance monitoring and cost analysis, guarantees the optimization of the payment process, effectively improves the processing speed and safety, reduces the fraud risk, enhances the transaction safety and transparency through the implementation of full-chain encryption and the real-time tracking of the payment progress, and improves the response speed to potential abnormalities.