179 results about "Product demand" patented technology

The invention provides a material simulation test method and platform for mold flow analysis, and relates to the technical field of material simulation testing, and the method comprises the steps: carrying out the product demand analysis of a target test material, and obtaining the product demand information; performing injection molding analysis to obtain an injection molding control parameter threshold; obtaining an injection molding simulation test model; carrying out an injection molding simulation test, and carrying out image pre-acquisition to obtain a first pre-acquired image; defect feature analysis is carried out, attention area distribution is carried out, and a target attention area is obtained; an injection molding defect area image is obtained, and defect feature information is obtained; and feedback testing and parameter optimization are carried out, optimized injection molding control parameters are obtained, and injection molding control is carried out. The technical problems that in the prior art, injection molding analysis generally depends on a fixed mathematical model and limited technological parameters, and problems in the molding process cannot be recognized and adjusted in real time, so that the production efficiency and the product quality are low are solved.

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.

A system and method are disclosed to identify one or more price-demand elasticity causal factors and to forecast demand using the one or more price-demand elasticity causal factors. Embodiments include a computer comprising a processor and memory. Embodiments train a machine learning model to identify one or more external causal factors that influence demand for one or more products. Embodiments train the machine learning model to generate one or more price-demand elasticity causal factors to predict a target outcome for a given product demand. Embodiments predict, with the machine learning model, a demand for the one or more products based, at least in part, on the identified one or more external causal factors and the generated one or more price-demand elasticity causal factors.

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.

The invention discloses a client information management method and system based on real estate transaction, relates to the technical field of client information management, and solves the technical problem that client information management cannot be evaluated according to a real-time product demand trend, so that client types cannot be distinguished according to different demand trend stages in the prior art. Specifically, a demand trend evaluation unit evaluates the demand trend of a real-time sales product, collects demand trend information, substitutes the demand trend information into a formula to obtain a demand trend evaluation coefficient in a real-time circulation stage, and deduces that the current circulation stage is in a purchaser market or a supplier market according to coefficient comparison; a customer stage analysis unit performs customer stage analysis according to the real-time circulation stage, obtains the speed-to-time ratio information of a supplier and the speed-to-time ratio information of a purchaser, deduces whether a current customer is a customer in a knowing stage or a customer in an intention stage according to information comparison, and correspondingly sends the customer to a customer information management platform; the label classification setting unit performs label classification setting on clients.

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.

The invention discloses a commodity recommendation method based on a user portrait. The method comprises the following steps: generating purchase preferences and purchase capabilities of a user based on purchase records and browsing records of the user; generating a commodity demand list of the user based on the purchase record and the browsing record; generating a commodity recommendation list of the user based on the commodity demand list and the purchase preference; generating a social relationship of the user based on the historical payment information of the user; based on the purchase preference, the purchase capability is determined. Generating an online shopping recommendation model according to the commodity demand list, the commodity recommendation list and the social relation; and recommending commodities to the user based on the online shopping recommendation model. According to the method, commodity recommendation is further carried out based on the online shopping time period of the user and the location of the user.

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 relates to a complex product multi-subject demand mining and importance analysis method. Determining a demand subject of the complex product based on the subject judgment element; constructing an information quality framework; performing noise filtering on the low-quality data based on a natural language processing technology; performing multi-subject demand fusion based on the semantic similarity and the demand subject weight; constructing a possibility degree model combining the hesitant fuzzy set and Kano; calculating a technical initial weight in combination with information entropy; and finally, the overall weight of the multi-subject demand is calculated based on combined weighting, and a multi-subject demand weight sorting result is obtained. According to the method, multiple technologies such as natural language processing, deep learning and Kano model improvement are combined, a technical scheme for carrying out complex product multi-subject demand mining and importance analysis is realized, and a decision basis is provided for enterprises to carry out product demand development and product design and manufacturing.

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.

A system and method are disclosed to identify one or more price-demand elasticity causal factors and to forecast demand using the one or more price-demand elasticity causal factors. Embodiments include a computer comprising a processor and memory. Embodiments train a machine learning model to identify one or more external causal factors that influence demand for one or more products. Embodiments train the machine learning model to generate one or more price-demand elasticity causal factors to predict a target outcome for a given product demand. Embodiments predict, with the machine learning model, a demand for the one or more products based, at least in part, on the identified one or more external causal factors and the generated one or more price-demand elasticity causal factors.

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 invention discloses a code scanning method and device, a storage medium and electronic equipment, and relates to the technical field of data processing.The method comprises the steps that a to-be-scanned target code file is determined; a target code scanner is obtained based on product demand information and programming language information corresponding to the target code file, and the product demand information at least comprises description information of a product output after the code file is scanned; and analyzing the target code file through a target code scanner to obtain a target product corresponding to the product demand information. According to the method and the device, the technical problem of relatively low code scanning flexibility caused by the fact that only a specific product can be identified by code scanning products in related technologies is solved.

The invention belongs to the field of system optimization and production scheduling, and particularly relates to an optimization control method of a conveyor belt feeding processing system capable of performing mixed-flow production of multiple types of workpieces in consideration of product demand conditions. The multi-variety workpiece mixed flow conveying belt feeding processing system comprises a conveying belt, a photoelectric sensor, # imgabs 0 # different types of workpieces, a processing main body, # imgabs 1 # cache libraries with different capacities and corresponding finished product libraries with different capacities. Firstly, the state, action and corresponding strategy of the system are defined, and the decision moment and decision process of the system are determined according to the working mechanism of the system; and then, according to the random characteristics and the decision process of the system, establishing a semi-Markov decision process model for optimizing the control problem, and solving an optimal strategy by using a strategy iterative algorithm. According to the invention, the productivity of the multi-dimensional flexible CSPS system can be effectively improved, and the requirements of customers can be better met.

The invention discloses an electronic creditor's right voucher transaction information matchmaking method and device and a storage medium, which are used for solving the problems of high electronic creditor's right voucher transaction risk and low transaction rate, and the method comprises the steps: obtaining the fund demand information of a fund demand side; the fund demand information comprises an electronic creditor's right certificate used for financing and verified by a movable property financing registration service mechanism, and the fund demand information comes from an enterprise user registered on the platform or is a third-party platform approved by the platform; obtaining financing product demand information of each fund provider from a supplier information pool; calculating the similarity between the fund demand information and financing product demand information of each fund provider; sorting all the calculated similarities in a descending order, and selecting a preset number of financing product demand information to recommend to the fund demander for the fund demander to select a transaction object; and performing transaction matching according to a selection result returned by the fund demand side.

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 field of purchase management, in particular to a raw material purchase management system based on a one-stop flexible supply chain, and the system achieves the butt joint of upstream and downstream industrial chains through the one-stop flexible supply chain, enables manufacturers to timely know the supply information of upstream manufacturers, also can know the demand information of downstream manufacturers, and improves the efficiency of purchase management. According to the method, the production speed and the raw material supply are allocated, the production speed capable of meeting the delivery time is obtained by analyzing the goods requirements of downstream manufacturers, the logistics transportation time and the raw material supply are combined, and the raw material inlet and outlet behaviors in the production process of the manufacturers are analyzed in a dynamic model analysis mode; therefore, the stock of a manufacturer can be effectively controlled, the capability of the manufacturer to deal with production fluctuation is ensured, and the influence on the manufacturer caused by market change due to excessive raw material overstock is avoided.

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 comprehensive supply chain logistics management system, and relates to the technical field of supply chain logistics management, and the system comprises the following modules: a demand statistics module which obtains the total residual quantity of products, sets the supply cycle time of the products, obtains the historical daily consumption quantity of the supplied products, and builds a product consumption database; obtaining a predicted consumption value according to the product consumption database; the method comprises the following steps: calculating a predicted consumption value according to a historical daily number of products so as to estimate a future product consumption number, calculating a total product demand amount according to the predicted consumption value, a total product residual amount and a product supply cycle time, and then calculating a maximum supporting day number of the products according to the total product residual amount and the predicted consumption value; therefore, the total number of products needing to be purchased and the number of remaining products can be conveniently mastered on the premise that production is not affected, and then a targeted purchasing plan can be conveniently executed on the products according to the actual situation in the follow-up process.

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.