129 results about "Product demand" patented technology

The invention discloses a process parameter optimization control method and system for an automobile injection molding part, and belongs to the technical field of data processing, and the method comprises the steps: collecting sample data to train a feedforward neural network based on the historical processing record of a target injection molding part, and constructing a product quality prediction plug-in; performing parameter fluctuation influence analysis according to the historical processing record, and constructing a fluctuation influence analyzer; determining product demand characteristics of the target injection molded part in combination with the automobile use scene; and by taking the product demand characteristics as expectations, optimizing the process parameters by using the product quality prediction plug-in and the fluctuation influence analyzer, and outputting the optimal process parameters for processing control. The problems of large product quality fluctuation and low production efficiency caused by insufficient injection molding process parameter control precision in the prior art are solved. By combining historical records, product quality prediction plug-ins and fluctuation analysis, precise optimization of process parameters is realized, the product quality is stabilized, and the controllability and efficiency of the production process are improved.

The invention discloses a production and manufacturing management method and system based on digital twinning, which are used in the field of production and manufacturing management, and the method comprises the following steps: collecting logistics management data in logistics storage and operation data of a production line, and constructing a logistics digital twinning processing model; simulating a logistics management process based on a logistics digital twin processing model, and tracking the positions of goods and transportation tools in real time by using a positioning and identification technology; utilizing a demand prediction model to predict a material demand and a product demand based on the data in the logistics digital twinborn model updated in real time, and adjusting the stock of logistics storage according to a prediction result; and calculating an optimal transportation path by using a path optimization algorithm, making a production and manufacturing plan of logistics storage, and monitoring the production and manufacturing process in real time. According to the bidirectional fast random search tree, a new path can be quickly recalculated, and the accuracy and adaptability of decision making are improved by comprehensively considering various transportation and operation constraints.

The invention provides a large model test case generation method, system and device based on multi-Agent collaboration. The method comprises the steps of obtaining product information of target product demand information; if the product demand information is incomplete, performing structured supplement on the extracted product demand information according to a slot filling technology to obtain complete product demand information; performing assembly according to the complete product demand information and the background information, and determining a first prompt word used for requesting the large model to generate a test case; and generating a first edition test case according to the first prompt word by using the plurality of agents, reviewing the first edition test case, generating an optimization suggestion, and fusing the first edition test case based on the optimization suggestion to generate a final test case. Through knowledge injection and structured requirements, the test case not only covers functional requirements, but also improves the accuracy of the test case; dynamic verification and correction of the use cases are achieved through cooperation of the multiple agents, the generation efficiency of the test use cases is improved, and the coverage rate is increased.

The invention discloses a supply chain intelligent replenishment method and system based on data analysis, and relates to the technical field of intelligent replenishment, during the operation of the system, supply chain related data is collected, sales data, inventory data and supplier data are obtained as a supply chain data set, and the supply chain data set is transmitted to a data preprocessing module; the collected supply chain data set is preprocessed to form a first data set, a second data set and a third data set, a supply chain performance index Gyjx is obtained after calculation, historical sales data and market trend analysis are utilized to predict product demands in a period of time in the future, and a supply chain performance index Gyjx is obtained. The supply chain performance index Gyjx is compared with a preset standard threshold value M and a preset standard threshold value N, a replenishment strategy is obtained, an inventory strategy and replenishment parameters are dynamically adjusted according to demand prediction and inventory conditions, operation conditions and inventory changes of all links of the supply chain are monitored in real time, abnormal conditions are found, and early warning is carried out in advance.

An intelligent system for interactive processes and experiences in the store, consisting of: a network of IoT sensors, including RFID readers, computer vision cameras, LiDAR sensors and environmental sensors, configured to capture real-time data on customer movements, product interactions and store conditions; an AI-powered analytics engine configured to process real-time and historical sensor data using machine learning models, customer behavior analysis, and forecasting techniques to generate dynamic insights into customer preferences, purchasing patterns, and inventory optimization; a real-time advertising module that is operationally connected to the AI-supported analysis engine, whereby the module dynamically generates and delivers personalized advertising content via digital signage, mobile applications, interactive kiosks or AI-supported smart carts based on individual customer profiles, customer traffic analyses and product demand trends; a subsystem for inventory management that includes RFID-based tracking, computerized shelf monitoring and predictive analytics, and is configured to automate inventory level detection, trigger replenishment alerts and optimize product placement in the store; an AI-driven workforce management module configured to dynamically allocate store staff based on real-time customer density, service demand, and predicted workload distribution, with staff assignments adaptively updated using AI-driven planning techniques; an interactive checkout subsystem that includes RFID-based automatic product identification, mobile payment integration, and AI-powered fraud detection; a cloud-integrated data processing server that communicates with the IoT sensor network and the AI-powered analytics engine, with the server performing distributed computing tasks to refine machine learning models, store historical data, and synchronize operational parameters across multiple retail locations; and a user interface system consisting of mobile applications, AR-enabled shopping assistants and voice-interactive kiosks, configured to provide customers with guided navigation, product recommendations and virtual try-on features based on real-time store interactions and AI-driven engagement models.

This system provides a technical solution for managing product sales, inventory, and deliveries through QR codes and predictive analytics. It incorporates algorithms that generate and assign unique QR codes to products based on real-time sales data from farmers. These QR codes provide direct links to product information, facilitating efficient inventory management and customer engagement. The system also leverages predictive analytics to forecast product demand by analyzing historical sales data, adjust inventory levels, and optimize delivery driver assignments based on proximity to farms. The hardware supporting the system includes servers, processors, network components, and scanning devices to ensure reliable operation and data synchronization. By automating QR code generation, integrating sales data, and dynamically assigning drivers, the system improves operational efficiency, minimizes errors, and enhances the customer experience.

The invention provides an automobile after-service pricing model optimization method under dynamic supply and demand balance, and relates to the technical field of pricing model optimization. The pricing model optimization method comprises the following steps of accessing a government policy issuing platform and an industry association database, capturing related policies and macroeconomic indicators of automobile post-service in real time, performing semantic analysis on financial news and policy interpretation articles, extracting potential factors, and performing classification and analysis on the potential factors. A competitor official website, an e-commerce platform and a social media account are monitored for 7 * 24 hours, a real-time feedback module is embedded in a service platform, customers are encouraged to evaluate services, prices and new product demands in time, a market signal threshold model is established, and the market quality is improved. And early warning thresholds are set for the policy change frequency, the competitor price fluctuation amplitude and the customer demand growth rate index. Through real-time market dynamic monitoring, scientific quantitative analysis, intelligent and accurate decision making and rapid iteration strategies, the enterprise pricing ability and market competitiveness are effectively improved.

The invention belongs to the technical field of order management, and particularly relates to a production order supply planning management system based on data analysis, and the system comprises an order data collection module which is used for collecting customer order data, and the customer order data comprises historical order data and current order data; the order data analysis module is used for calculating the demand quantity of product orders in M periods in the future based on the obtained historical order data; the supply chain data collection module is used for collecting data of each production line in a supply chain; the supply chain data analysis module is used for calculating the actual order demand quantity of the product in the M periods in the future based on the product order demand quantity and the data of the production line in the supply chain; and based on the data of the production line in the supply chain and the actual demand of the product, the delivery time index of the order product required by the current customer is calculated.

The invention discloses a data processing method. The method comprises the following steps: constructing a linear programming model; a decision variable of the linear programming model represents the number of products transported from a production site to a sales site, the constraint conditions at least comprise a product production capacity constraint for at least one production site and a product demand constraint for at least one sales site, and the target is at least one of the minimum product production cost and the minimum product transportation cost; determining at least two groups of initial solutions corresponding to the linear programming model; solving the linear programming model in parallel based on the at least two groups of initial solutions by using an interior point method so as to iteratively update the at least two groups of initial solutions; for at least one solving process corresponding to each group of initial solutions, the updated solution of the initial solutions is determined based on solving information of at least one group of initial solutions except the initial solutions; the solving information at least comprises value boundary updating information of the decision variable; and taking the updated solution as a target solution of the linear programming model in response to the condition that the updated solution corresponding to the at least one initial solution meets the convergence condition.

The invention relates to the field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a digital life cycle management method, device and equipment of a product and a medium, and the method comprises the steps: collecting product associated data of different dimensions, carrying out the demand integration analysis, and generating product demand information; inputting the product demand information into a generative adversarial network for product scheme generation processing to obtain a product design scheme; performing semantic analysis on the product design scheme to generate corresponding standard product information; performing feature matching on the standard product information and different customer groups, determining a target product of each customer group and pushing marketing information; and when a purchase request is received, performing pricing evaluation according to request customer information and request product information in the purchase request, and returning a dynamic pricing result to the customer. The full life cycle management from demand to sales is realized by processing the multi-dimensional product associated data, and the life cycle management efficiency of the product is improved.

The invention relates to the technical field of petroleum refining, and provides a refining product production scheme determination method, device and equipment and a medium. Obtaining the input-output proportion of products of various grades produced by the production device, the public engineering consumption proportion of unit products, the input-output proportion of transition materials produced in the grade product switching process, and the public engineering consumption proportion of unit transition materials; based on the proportion, constructing an input-output material balance equation and a public engineering consumption equation for producing products of various grades by the production device, and a material balance equation and a public engineering consumption equation for producing transition materials in the grade product switching process; constructing a target function with maximized total benefits; constructing a full-connection network of the switching paths of the products of each trademark, and constructing an integer variable constraint equation and a continuous variable constraint equation based on the full-connection network of the switching paths of the products of each trademark according to the demand types of the products of each trademark and the demand quantity of the products of each trademark; under the current market demand and market price conditions of products of various brands, the objective function and the equation are solved simultaneously, the optimal production scheme of the production device in the switching production process of the products of various brands and the number of the products of various brands are obtained, the optimization problem of the switching sequence of the products of the production operation brands of the petroleum refining device can be solved, and the production efficiency is improved. The overall benefit is improved.

The embodiment of the invention provides an intelligent customer service response method and device, electronic equipment and a medium, belongs to the technical field of artificial intelligence, and is suitable for financial scenes and medical scenes. The method comprises the following steps: performing service period prediction by combining user portrait features and behavior features extracted from service interaction data to obtain a user service period state; performing product demand prediction according to dialogue features, behavior features, user portrait features and user service period states extracted from the service interaction data to obtain a product service demand degree; and sending a target service response scheme generated based on the product service demand degree and the user service cycle state to the user. According to the embodiment of the invention, the target service response scheme is generated by combining the product service demand degree and the user service period state generated by the dialogue features, the behavior features and the portrait features of the user, differentiated service strategies can be adopted for clients with different demand degrees in a proper service stage, and the intelligent customer service response effect is improved.

The invention relates to the field of product demand prediction, in particular to a garment product demand prediction method and system, and the method comprises the following steps: S1, obtaining an original data set; s2, expanding the original data set by adopting a TimeGAN model to obtain a synthetic data set, and merging the original data set and the synthetic data set to serve as a new data set or taking the original data set as the new data set; s3, constructing a clothing product demand prediction model; s4, performing preliminary training on the clothing product demand prediction model by adopting the new data set, optimizing parameters of the model by adopting a black wing plinergic optimization algorithm, constructing a demand prediction model according to the obtained optimized parameters, and performing re-training on the demand prediction model to obtain a hybrid prediction model; s5, predicting clothing product demands by using the hybrid prediction model, and outputting a prediction result; the problem of a clothing product demand prediction model in an actual prediction task is solved.

The invention relates to the technical field of data management, in particular to an industrial data management control system and method for furniture customization, and the system comprises a pre-selected scheme preliminary making module, a scheme multi-source data reasonability judgment module, a scheme evaluation and screening module, and an early warning signal generation and processing module. The scheme multi-source data rationality judgment module is used for performing periodic processing test on different product prediction assembly schemes in sequence, analyzing product qualification conditions by combining corresponding periodic processing test reports, and calculating comprehensive benefit conditions of corresponding products according to analysis results; according to the method, the benefit condition generated by the combination scheme of different parts in the product production in the to-be-monitored area is analyzed and obtained, the optimal process manufacturing scheme is generated in combination with the benefit condition, and the optimal process manufacturing scheme is calibrated in real time in combination with the corresponding product demand list, so that the production efficiency is improved, the product production flexibility is improved, and the product quality is improved. And maximization of the production benefit of the product is realized.

This application relates to a fine-grained demand forecasting method for retail products based on hierarchical learning. The method includes: First, define auxiliary variables, which include the initial demand, actual sales volume, and product transfer probability of retail products; then, determine the predicted sales volume of products based on the initial demand, actual sales volume, and product transfer probability; then, determine the demand forecasting objective function based on the predicted sales volume of products; then, use a hierarchical method based on the demand forecasting objective function to determine the consistency constraints based on the product dimension, spatial dimension, and time dimension; finally, solve the demand forecasting objective function when one of the auxiliary variables is fixed based on the consistency constraints to perform fine-grained demand forecasting of retail products. It improves the accuracy of demand forecasting for retail products such as vending machines or intelligent shelves, can more accurately reflect the demand changes of customers, and thus improves the forecasting accuracy.

The invention discloses a product distribution strategy optimization method considering the endogenous influence of a distribution network on a demand, and the method comprises the steps: 1, considering the endogenous influence of the distribution network on the demand, taking the minimization of the total logistics time consumption in the distribution network as a target, and building a distribution strategy optimization model; 2, analyzing historical demand data, constructing product attribute proxy features, forming a demand feature vector with historical logistics service quality and regional economic development level of a cargo distribution network, training in combination with a random forest prediction algorithm to obtain a prediction model, predicting product demand, and providing input for a cargo distribution strategy optimization model; and 3, constructing an iterative solution framework of product demand prediction and product distribution strategy optimization, and outputting a stable distribution decision. According to the method, the distribution network is dynamically adjusted by introducing a feedback mechanism, so that the total logistics time consumption in the distribution network is effectively shortened, the logistics operation efficiency of an enterprise is improved, and a systematic solution is provided for a product distribution strategy optimization problem in a complex environment.

The invention discloses a data generation method, a test method, a related device and a related system, and relates to the technical field of software testing, the data generation method comprises the following steps: obtaining product related data of a target product, the product related data comprising product demand data and / or interface definition data; using the large model to generate a test outline according to the product demand data, and using the large model to generate a test case according to the product demand data and the test outline; and / or utilizing the large model to generate an interface test script according to the interface definition data. According to the data generation method disclosed by the invention, the test cases and the test scripts can be automatically generated by utilizing a large model, and compared with a manual writing mode, the acquisition efficiency of the test cases and the test scripts is greatly improved, and the labor cost and the technical threshold are greatly reduced; and the acquisition efficiency of the test case and the interface test script is greatly improved, so that the test efficiency is greatly improved.

This invention provides a method, apparatus, device, and readable storage medium for predicting the value of banking business, relating to the field of computer technology. The method includes: acquiring historical product data and demand analysis, whereby the demand analysis includes demand names, directories, associated systems, and demand rules; calculating a demand data set based on the historical product data and demand analysis, where each element in the demand data set represents the number of transaction demand modifications, the set of demand rules, and the transaction credit value corresponding to a transaction; establishing a mathematical model of product value based on the historical product data, using the demand data set as input, and solving the mathematical model to obtain a product value set, where each element in the product value set represents the number of product demand modifications, the proportion of new business rules, and the product credit value corresponding to a product; and calculating the product value prediction result based on the product value set. By establishing a value chain relationship between products and demands, the method quantitatively reflects the value attributes of a product, displays the direction of business changes and the quality of demands in real time, assesses the direction of business changes, thereby improving customer experience, reducing operating costs, and forming a positive cycle.

The application discloses a kind of manufacturing process process control method, system and device, it is related to process control field, comprising: according to product demand to determine the production procedure corresponding to product and the unit under production procedure;Current unit under production procedure is sequentially regarded as current unit according to the order of production procedure, and current unit is controlled to carry out production according to corresponding process control item standard.If the process control item of current unit does not comply with process control item standard in the process of unit production, and current is not the last production procedure, timely adjust the process control item standard of the unit of next production procedure, reduce the problem of final product due to adjustment lag or process not found in time to comply with standard.It does not need artificial calculation, reduce the waste of human resources, and improve the calculation efficiency.If the unit of next procedure can not be adjusted process, the material produced by current unit is withdrawn from production line, timely stop loss, and improve production efficiency.

To construct a demand forecast system capable of calculating recommended order quantity in detail.SOLUTION: A demand forecast system for forecasting demand of seasonal commodities includes: a demand forecast unit which receives, as input, sales performance data, using a first predetermined time-series forecasting algorithm for forecasting market trends of a target seasonal commodity and a second predetermined time-series forecasting algorithm for forecasting share trends of a target SKU in the market of the target seasonal commodity, and outputs demand forecast data for the target SKU for each store; a current inventory estimation unit which receives, as input, the sales performance data and store delivery / return data, and outputs current inventory estimated value data for the target SKU for each store; and a recommended order quantity calculation unit which receives, as input, the sales performance data, the demand forecast data for the target SKU for each store, and the current inventory estimated value data for the target SKU for each store, and outputs recommended order quantity data for the target SKU for each store.SELECTED DRAWING: Figure 2

The invention discloses an individual care product process optimization method and system based on user behavior data, and relates to the field of intelligent data processing, and the method comprises the steps: collecting a historical user behavior data set in a historical time range after a target product is put into the market through multiple channels, and carrying out the data preprocessing and product improvement correlation feature extraction, obtaining a historical product improvement associated feature set; determining an optimal process adjustment time period by combining a predicted product demand growth rate and a predicted product demand fluctuation coefficient within a preset time range; according to the user behavior data set in the optimal process adjustment time period, determining a product improvement index according to the product quality associated feature set; and obtaining an optimal process adjustment scheme by taking approximation to a product improvement index as an optimization target, and carrying out production process iterative optimization within a preset time range. According to the method and the device, the problems of poor user experience, low optimization strategy scene adaptability and inaccurate process optimization direction in the prior art are solved.

The present invention discloses a comprehensive supply chain logistics management system, which relates to the technical field of supply chain logistics management and includes the following modules: a demand statistics module, which obtains the total remaining quantity of products, sets the product supply cycle time, obtains the historical daily consumption quantity of supplied products to establish a product consumption database, and obtains a predicted consumption value according to the product consumption database; calculates the predicted consumption value based on the historical daily quantity of products, so as to estimate the future product consumption quantity, calculates the total product demand according to the predicted consumption value, the total remaining quantity of products and the product supply cycle time, and then calculates the maximum support days of the products according to the total remaining quantity of products and the predicted consumption value, so as to facilitate mastering the total quantity of products that need to be purchased and the number of days that the remaining products can be maintained without affecting production, thereby facilitating the subsequent implementation of a targeted procurement plan for products according to the actual situation.

The invention relates to the technical field of computer software and information management, and discloses a product demand classification method, which comprises the following steps of: performing data acquisition of multi-source data according to product demands to obtain a multi-source data set; performing data preprocessing on the multi-source data set; performing multi-modal feature fusion, cross-process semantic alignment and demand modeling storage on the multi-source data set after data preprocessing to obtain a standardized demand model; performing full-process association on the standardized demand model through process mapping and weight calculation, and establishing a multi-dimensional association relationship between the demand and the business process; and solving cross-process demand contradictions through conflict management and an iteration mechanism, and optimizing the standardized demand model to complete closed-loop feedback of demand optimization. By utilizing the scheme of the invention, the problems of extensive demand classification, weak cross-process integration capability, poor dynamic adaptability and the like in the prior art can be solved, and efficient management and integration of demands can be realized.

The application discloses a data processing method and device. The method comprises: processing demand data about a target product input by a user by using a semantic recognition model to obtain a plurality of product demand parameters of the target product; obtaining a plurality of product physical parameters of the target product by using a physical structure model corresponding to the target product; at least some of the plurality of product physical parameters have production constraint conditions; processing the plurality of product demand parameters and the plurality of product physical parameters by using a parameter fusion model to obtain a target parameter set; the target parameter set comprises a first parameter and a second parameter; a parameter value of the first parameter is consistent with a parameter value of a corresponding parameter in the product demand parameters; a parameter value of the second parameter satisfies a production constraint condition corresponding to the second parameter in the physical structure model; and the target parameter set is used to produce the target product.

The invention relates to the technical field of data prediction, and particularly provides a supply chain new product demand-oriented multi-scale transfer learning prediction method. The method comprises the steps of obtaining sales data of a to-be-predicted product and a product demand prediction model; the training process of the model comprises the steps of obtaining original sales data and determining target domain data; calculating the similarity between the original sales data and the target domain data through a graph attention network, and determining source domain data; training the initial models corresponding to different scales through the source domain data to obtain a pre-training model; performing fine tuning on the pre-training model through the target domain data; fusing the pre-training models corresponding to different scales to obtain a product demand prediction model; and inputting the sales data of the to-be-predicted product into the product demand prediction model to obtain predicted demand data of the to-be-predicted product. Through the graph attention network and the multi-scale transfer learning, an underfitting phenomenon caused by sample scarcity and imbalance can be solved, and accurate prediction of small sample data is realized.

A technique is disclosed for generating a retail forecasting model from product cluster-based estimated price elasticity values ​​and predicting the impact of price changes on demand for a set of products. The system generates cluster-based price elasticity values ​​for a set of products by applying a set of regression elasticity estimation algorithms to a set of product data and clustering the products based on product descriptions and estimated price elasticity values. This system generates a retail forecasting model using the cluster-based price elasticity values ​​of the products.

The invention discloses a product demand document analysis method and device, electronic equipment and a storage medium. The method comprises the steps that a to-be-analyzed product demand document is acquired, and the content of the to-be-analyzed product demand document is subjected to structured processing; identifying at least one entity object involved in the product demand document; matching operation fault data corresponding to the entity object in a preset database; and generating risk prompt information corresponding to the product demand document based on the operation fault data, and associating the risk prompt information to the product demand document. According to the application, through automatic analysis and association of the content of the product demand document and the operation fault data, potential operation risks can be identified in a demand design or review stage; the efficiency and quality of demand review are improved, and the operation risk after the system is online is reduced.

The invention discloses a recommendation method and device of an intelligent vending machine, equipment, a medium and a program product, and relates to the technical field of intelligent vending machines. And based on the demand characteristics of the product demand of the to-be-purchased customer and the product characteristics of each intelligent vending machine in the existing product sequence of the intelligent vending machine, determining a target product in the existing product sequence through a preset selection model, and recommending the target product to the to-be-purchased customer. Therefore, by learning the preset selection model of the specific association relationship between the demand characteristics and the product characteristics, the target product meeting the demand of the to-be-purchased customer is obtained through screening, so that the selection efficiency and accuracy of the intelligent vending machine are improved.

To realize a demand forecasting display device that can provide users with information to make more accurate product demand forecasts. [Solution] The demand forecasting display device comprises: a consumption forecasting unit that uses purchase data showing the actual purchase history of the product by consumers to forecast consumer demand for the product for each unit period during the forecast period; a wholesale shipment forecasting unit that uses wholesale shipment data showing the actual shipment history of the product by wholesalers to forecast wholesale shipment demand for each unit period during the forecast period; a manufacturer shipment forecasting unit that uses manufacturer shipment data showing the actual shipment history of the product by manufacturers to forecast manufacturer shipment demand for each unit period during the forecast period; and a display control unit that displays the forecast results for consumer demand, the forecast results for wholesale shipment demand, and the forecast results for manufacturer shipment demand on a common time axis.

The invention discloses an intelligent auditing decision-making method based on industrial big data, and is applied to the technical field of production decision-making. Comprising the following steps: acquiring demand data of different products in a plurality of historical periods, and converting the demand data into time sequence data; constructing a product demand prediction network, and training based on the data set; acquiring demand data of different products in a preset time period, converting the demand data into time sequence data, inputting the time sequence data into the corresponding demand prediction network, and obtaining demand prediction data of the different products; taking the demand prediction data as a production decision value, and taking the minimum total production cost and the minimum total production time as production scheduling targets to obtain an intelligent decision scheme for production scheduling; auditing the intelligent decision scheme; and arranging factories to produce different products based on the intelligent decision-making scheme. According to the invention, the decision scheme can be generated based on industrial production big data for reference of workers, so that the production scheme better meets the market demand.