100 results about "Product demand" patented technology

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 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 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.

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.

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 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.

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 embodiment of the invention provides a product scheme generation method and device, equipment, a medium and a product, and belongs to the technical field of computer application. The method comprises the following steps: calling a domain-driven design model to generate a business process, corresponding to a business scene, of a target product according to the business scene corresponding to the target product; extracting each system function menu required by the target product from the business process; the authority of the system corresponding to each system function menu is broken through through a single sign-on technology, and the system function menus are combined to obtain an integration process of the target product; performing multiple rounds of conversations based on the integration process through the intelligent assistant to obtain a target product scheme of the target product; wherein the intelligent assistant is obtained by training the intelligent large model based on the product field data corresponding to the target product. The method and the device can be applied to business scenes such as financial science and technology, medical health and the like, and can improve the generation efficiency of the product scheme.

A demand prediction assistance apparatus includes: a first displaying section for displaying, based on a plurality of indexes, information indicating a product which requires a prediction reassessment, the plurality of indexes including (i) a first index which indicates a degree of divergence between an actual outcome value of past sales and the predicted value of each of the plurality of products and (ii) a second index which indicates a result of comparison between the predicted value or the planned value for one future period and an estimated value of sales for the one future period of each of the plurality of products; an accepting section for accepting a selection made by a user with respect to a product displayed by the first displaying section; and a second displaying section for displaying an analysis result regarding demand for a product corresponding to the selection accepted by the accepting section.

To allow demand prediction which more easily follows a demand fluctuation.SOLUTION: A demand prediction device 1 for predicting the demand for a product comprises a customer information generation unit 11 which acquires and aggregates company information of an end customer from a supply chain network for the product that includes sales channels for 0-th products to N-th products and generates customer information indicating an aggregation result, a statistical information extraction unit 12 which extracts statistical information about the end customer on the basis of the customer information, a feature amount generation unit 13 which generates a feature amount about the end customer on the basis of the statistical information about the end customer, a demand prediction model generation unit 14 which generates a demand prediction model by learning a demand fluctuation with the feature amount as an explanatory variable and the demand of the product as an objective variable, and a demand prediction model application unit 15 which predicts the demand for the product by applying the demand prediction model.SELECTED DRAWING: Figure 1

The invention discloses an agricultural product supply chain transportation management method and system based on multi-source data fusion, relates to the technical field of agricultural product logistics, and solves the problems that in the prior art, a long-term fixed mode is mostly adopted for supplier cooperation, sudden demand changes or regional supply shortage cannot be flexibly responded, and the transportation efficiency is high. The agricultural products cannot be supplied timely and effectively, and the accuracy and efficiency of the agricultural product supply chain transportation management method are low. Agricultural product demand prediction quantity is generated based on market data and environmental data; dynamically searching an optimal cooperative supplier based on the agricultural product demand prediction amount; the optimal transportation path is generated based on the cooperative suppliers, and when the agricultural product demand prediction amount is obtained, the supply condition and the comprehensive capability of the current cooperative suppliers are analyzed, so that whether the suppliers need to be adjusted is determined, the supply demand can be accurately and timely completed, the supply stability is improved, and the supply efficiency is improved. And the accuracy and efficiency of the supply chain transportation management method are improved.

This invention provides a sales plan publishing method and related equipment. The method includes: creating a product demand plan based on a product planning unit list, wherein each product planning unit includes: a planned product name, a planned sales volume, and the hierarchical relationship between products within the planned product list; triggering a sales plan in the demand plan according to a planned time node, wherein the sales plan is determined based on the planned sales time and the planned sales scheme; creating a corresponding time-version sales plan based on the triggered sales plan and publishing an execution notification. This achieves the goal of enabling the system's automatic task module to execute plan creation tasks at fixed time points according to business process settings, avoiding human error and transparently and promptly sharing upstream and downstream task information, thus meeting the needs for efficient and accurate collaborative planning process execution.

The invention relates to the technical field of production scheduling, in particular to an intelligent production scheduling plan scheduling collaborative optimization system which comprises a product splitting module, an inventory analysis module, a productivity calculation module and a product marking module. The product splitting module is used for splitting the finished product into minimum manufacturing units; and the inventory analysis module is used for checking the existing material inventory according to each material required by the product and calculating the number of parts required by the target product according to a product demand calculation formula. The product can be split into minimum manufacturing units through the product splitting module, the inventory analysis module, the productivity calculation module and the product marking module; according to the method, the number of the raw materials needed by the minimum manufacturing units is calculated, meanwhile, the minimum manufacturing units can be analyzed, existing inventory is checked, and the materials which cannot be manufactured are marked, so that the production plan of all the parts is clearer, and production scheduling is more efficient.

We will develop an outlier correction device that can present users with options for more accurate product demand forecasting. [Solution] The abnormal value correction device comprises: an abnormal value detection unit that detects abnormal values ​​from actual values ​​of the volume of goods distributed for each unit period; a reception unit that receives a user's specification of the starting point of a first period corresponding to the abnormal value detected by the abnormal value detection unit; a correction proposal unit that calculates a correction value for the actual values ​​of the first period based on the actual values ​​of a second period prior to the starting point received by the reception unit, and proposes the calculated correction value to the user; and an option presentation unit that presents the user with a first option of not correcting the actual values ​​of the first period, a second option of provisionally correcting the actual values ​​of the first period with the correction value, and a third option of finalizing the correction of the actual values ​​of the first period.

The application discloses a product plan template generation method and device, and the method comprises the following steps: obtaining a concerned element in a historical plan; the concerned element comprises a concerned object, and the concerned object corresponds to a concerned index and a concerned time; determining a plan element according to the concerned element; the plan element comprises a plan object, and the plan object corresponds to a plan index and a plan time; matching a corresponding prediction algorithm and a data object for the plan index; the prediction algorithm is used for predicting the plan index, and the data object is used as input data of the prediction algorithm; and generating a plan template according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index and the data object corresponding to the prediction algorithm. The application improves the processing efficiency and reliability of product demand planning of an enterprise.

The present invention provides a method for controlling silica gel production equipment, relating to the technical field of silica gel production control. The method comprises collecting historical production record data of the equipment, performing production control analysis based on product demand, and forming demand production control reference data; performing cost analysis based on control adjustment based on the historical production record data of the equipment, and forming production control adjustment cost reference data; obtaining target product demand information, and performing production control analysis in combination with the demand production control reference data and the production control adjustment cost reference data, and forming target demand production control reference data; performing production control based on the target demand production control reference data, and monitoring the production process to form real-time production monitoring information. By combining the historical production control data of the equipment to establish production control adjustment methods for different silica gel products, the method greatly improves the adaptability of equipment production, production efficiency, and production quality.

The invention discloses a user behavior habit analysis system and method based on big data fusion, and relates to the technical field of intelligent interaction, and the system carries out the preliminary screening of sales personnel based on the actual product demands, analyzes the value degree of an order based on the actual product demands of a customer, and analyzes the purchasing stage and emergency degree of the customer based on the customer dialogue record. The purchase intention of the customer is analyzed based on the purchase stage and the urgency degree, the priority of the customer is analyzed based on the value degree of the order and the purchase intention of the customer, and matching of a plurality of sales is performed based on the priority of the customer. According to the invention, through real-time dialogue analysis, the purchase intention of the customer is quantified, and the optimal sales is allocated according to the priority of the customer, so that the accurate and preferential delivery of sales resources is realized, and the customer experience is improved. Therefore, the purchase transaction rate and the customer satisfaction are improved.

The invention provides a financial product recommendation method and device, equipment, a storage medium and a program product, and relates to the technical field of finance. The method comprises the following steps: in response to product demand information input by a user, obtaining a behavior sequence of the user and product feature data of a plurality of candidate products, inputting the behavior sequence and the plurality of product feature data into a product recommendation model, and based on a first product preference feature of the user in a current period, recommending the candidate products to the user. And determining recommendation probabilities corresponding to the candidate products according to the product demand information and the second product preference characteristics of the similar users in the current period, and generating product recommendation information based on the product demand information and the recommendation probabilities corresponding to the candidate products. The method combines the product preferences of the user in different periods and the product preferences of similar users in the same period to construct the dynamic demand of the user for the financial product, and further based on the dynamic demand and the product demand information input by the user, the accuracy and suitability of personalized recommendation of the financial product are remarkably improved.