54 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 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 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.

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.

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 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 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.

The invention discloses a product demand design auxiliary method, a demand agent system and a related device, and relates to the technical field of product development, and the method comprises the steps: obtaining user interaction information, determining skill intention information corresponding to the user interaction information, carrying out the task planning according to the skill intention information, and obtaining a plurality of planned task steps, the plurality of task steps comprise a target step, and the target step is to obtain multi-source heterogeneous data related to user interaction information from a tool platform pre-configured for a product demand design process or other processes, process the multi-source heterogeneous data into a uniform format, and sequentially execute the plurality of task steps, and based on the multi-source heterogeneous data and the user interaction information, obtaining the staged output data corresponding to the specific sub-stage. According to the method, the multi-source heterogeneous data can be obtained from the multiple tool platforms in the product demand design process to assist demand understanding, and the accuracy of stage output data is improved.

Embodiments of a supply chain management system (SCMS) are disclosed that enable the generation of synthetic supply chain activity data for developing machine learning models, such as models for predicting vendor lead times (VLTs) of purchase orders fulfilled by a supply chain network. In embodiments, the generation process is performed over successive time periods to simulate dynamically changing variables of the supply chain network, including inventory levels, product demand, and stock manager decisions. The generation process may also be used to generate synthetic data to simulate elements within the supply chain network, such as simulated warehouses, vendors, or products. The disclosed SCMS is able to generate highly realistic training data that simulates the operations within the supply chain network, which can be used to improve the performance of machine learning models.

The invention provides a product recommendation method which can be applied to the technical field of big data. The product recommendation method comprises: in response to a product recommendation request of a target object, performing product demand intention recognition on voice data carried by the product recommendation request to obtain an intention recognition result, the intention recognition result comprising a first demand intention and a second demand intention having an attribute relationship, the demand level attribute of the first demand intention is higher than that of the second demand intention; obtaining a first candidate product set related to the first demand intention and a second candidate product set related to the second demand intention; based on the correlation degree between the first candidate product set and the second candidate product set, a target recommended product is determined from the candidate products, the target recommended product is pushed to the target object, and the first candidate product set and the second candidate product set comprise the candidate products. The invention further provides a product recommendation device and equipment, a storage medium and a program product.

The application discloses a kind of material set early warning method, device, storage medium and terminal, method includes: determining current early warning level, obtains the product demand information to be produced under this early warning level;Load the material set early warning model pre-trained for this early warning level;The product demand information to be produced is input into the model, and the actual inventory of each material corresponding to the early warning level and the predicted usage of each material are output;According to actual inventory and the predicted usage of each material, judge whether the product to be produced satisfies set, and the product and its corresponding semi-finished product not set are early warned for not satisfying set.Due to the material set early warning model of each early warning level under five early warning levels is trained in the present application, so when obtaining current early warning level and product demand information, it can be automatically judged whether the product to be produced satisfies set, and the product and its corresponding semi-finished product not set are early warned for not satisfying set, so as to improve production efficiency.

The invention relates to the technical field of artificial intelligence, in particular to a product demand management method and system based on a large model agent, and the method comprises the following steps: multi-source demand information collection and injection, demand intelligent analysis and structuring, demand collaboration and refinement, demand priority intelligent evaluation, demand tracking and influence analysis, and closed-loop feedback and self-evolution. The method has the beneficial effects that a large amount of repeated labor (such as information extraction, document writing and state tracking) is automatically processed, so that a product manager can focus on high-value decision; and through standardization processing of the LLM intelligent agent, demand ambiguity is reduced, and a unified demand language is established.

The invention discloses a high-flexibility self-adaptive intelligent disordered sorting robot system, which relates to the technical field of intelligent manufacturing and industrial automation, and comprises a visual inspection and positioning module, a dynamic trajectory planning module, an intelligent grabbing and flexible control module, a digital twin quality tracing platform and a system stability and fault-tolerant design module, through self-adaptive production line collaboration, multi-mode sorting compatibility, part feature autonomous learning and full-link quality tracing, the sorting efficiency and the production efficiency can be effectively improved, the unit cost is reduced, small-batch mixed line production is supported, the flexible production capacity of equipment is greatly improved, and the product quality is improved. By means of the technical scheme, enterprises can better cope with diversified product requirements, the market competitiveness is enhanced, meanwhile, the new material introduction adaptation time is shortened, the rapid remodeling capacity of equipment is remarkably improved, and when the enterprises face the rapidly-changed market requirements, production plans can be adjusted more rapidly, the downtime is shortened, and the equipment utilization rate is increased.

To improve the efficiency of the whole supply chain, to optimize the stock of the whole supply chain by strengthening cooperation especially between retail business and manufacturing business and to appropriately adjust the balance of the demand and supply of merchandise.SOLUTION: The following processes are executed by the supply chain management system 300 arranged on the cloud. Retail data managed by a retailer are acquired from a retailer side main system and stored in a retailer side database 310. Manufacture data managed by the manufacturing industry are acquired from a manufacture side main system and stored in a manufacture side database 370. On the basis of retail data stored in a retail side database 310, the demand prediction of merchandise is calculated, and the demand prediction result of merchandise is outputted as demand prediction information 346. The production plan of merchandise is performed based on manufacturing data stored in a manufacture side database 370 and demand prediction information 346, and the production plan of merchandise is outputted as production plan information 386.SELECTED DRAWING: Figure 3

The invention provides a product recommendation method and device, equipment, a storage medium and a program product, and relates to the field of intelligent recommendations, the method comprises the following steps: matching a plurality of candidate recommendation products for a user in a product database according to product demand interaction information of the user at the first time; according to a recommendation strategy network and recommended product information of the user at the second time, determining a target recommended product at the first time for the user in the plurality of candidate recommended products; wherein the parameters of the recommendation strategy network are obtained by updating a first reward value obtained according to the first feedback information of the user on the recommended product at the second time; the second time is before the first time, according to the scheme, precise product recommendation can be achieved, and the product recommendation service efficiency and the user satisfaction degree are improved.

The utility model relates to the technical field of glue filling, and discloses an adjustable nozzle glue filling device which comprises a bottom plate, the upper surface of the bottom plate is fixedly connected with a sliding rail, the outer wall of the sliding rail is slidably connected with a sliding block, the upper surface of the sliding block is fixedly connected with a first U-shaped frame, the inner top wall of the first U-shaped frame is fixedly connected with a first electric push rod, and the inner top wall of the first U-shaped frame is fixedly connected with a second electric push rod. The output end of the first electric push rod is fixedly connected with a second U-shaped frame, the inner wall of the second U-shaped frame is fixedly connected with a first sliding column, the outer wall of the first sliding column is slidably connected with a sliding block, and the outer wall of the sliding block is fixedly connected with a fixing block. According to the utility model, the sliding block moves to drive the distance between the nozzle heads to change through the fixed block, so that the effect of adjusting the distance between the nozzle heads is achieved, the requirements of different products are met, the production of various products is met, the change of the products can be quickly adapted, the adjusting time and cost are reduced, and the flexibility and universality of a production line are improved; and enterprises can deal with diversified market demands.

The invention discloses remaining needle guide wire automatic assembly and Z crimping circulation equipment and a using method, the remaining needle guide wire automatic assembly and Z crimping circulation equipment comprises an equipment main body, a frame body and a lower cabinet box are positioned and mounted at the bottom of the equipment main body, an upper cabinet box is positioned and mounted at the upper end of the equipment main body, and a conveying seat is mounted in the equipment main body; a feeding pushing mechanism and a feeding protection box are assembled at one end of the conveying base, a discharging pushing mechanism and a discharging protection box are assembled at the other end of the conveying base, and a monitoring control mechanism is installed at the rear end of the conveying base. According to the remaining needle guide wire automatic assembly and Z pressure welding circulation equipment and the use method, the manual guide wire penetrating process is reduced, the equipment achieves automatic assembly, various requirements of market products are met to the maximum extent, a conveying system is more stable, meanwhile, starting, detection, blocking and clamping positioning can be effectively controlled, precision is high, accuracy is excellent, the assembly effect is improved, and the production efficiency is improved. And full-automatic Z pressure assembling is achieved.

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.

A new product demand prediction apparatus according to the present disclosure includes: a memory configured to store instructions; and one or more processors configured to execute the instructions to: acquire new product information including at least information regarding promotion of a new product that is a target of demand prediction; acquire first similar product information indicating a sales performance of a first similar product selected based on at least one element of a scale of the promotion, a characteristic of a product, and a sales method; acquire second similar product information indicating a sales performance of a second similar product selected based on the category of the product; and predict sales in a predetermined period including a period after a promotion period of the new product based on the new product information, the first similar product information, and the second similar product information.

Embodiments of the present application provide a kind of for product batching management method, equipment and storage medium. Among them, method includes as follows: obtaining multiple product demands;The product demand includes product demand amount in it;According to the multiple product demands, utilize first optimization model, determine the multiple raw materials required by each product demand in the multiple product demands and the demand amount of each raw material;The first objective function of the first optimization model is related to total raw material cost minimization;The multiple product demands include first product demand;According to the multiple raw materials required by the first product demand and the demand amount of each raw material, utilize second optimization model, determine the batching scheme corresponding to each respectively for completing the first product demand multiple batching;The second objective function of the second optimization model is related to batching times minimization. The optimization scheme provided by the embodiments of the present application can improve the output efficiency of batching scheme.

The invention relates to the field of product demand prediction, and particularly provides a supply chain purchase demand prediction method and device based on a hybrid deep learning architecture, and the method comprises the following steps: S1, data preprocessing: carrying out the preprocessing of original time series data; s2, constructing a hybrid deep learning model, introducing a plurality of parallel channels into the network to realize'multi-channel 'processing, and processing the same input data by each channel through different paths; s3, performing model training and hyper-parameter optimization; and S4, after training is completed, demand prediction is generated by the model, and transparency and credibility of prediction are enhanced in combination with an interpretable artificial intelligence technology. Compared with the prior art, the transparency and credibility of the model decision making process can be enhanced, and practical guidance is provided for supply chain decision making.