222 results about "Customer order" patented technology

The invention provides an intelligent factory decision control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining equipment operation state data, customer cooperation data, customer order data and material feature data, determining a customer value level according to the customer cooperation data, and carrying out the health assessment based on the equipment operation state data, thereby achieving the intelligent factory decision control. Equipment state evaluation parameters are obtained, an initial scheduling plan is generated according to the customer order data, the customer value level and the equipment state evaluation parameters, a preset production line digital twin model is called in combination with the material feature data to perform scheduling verification optimization, scheduling optimization compensation parameters are obtained, and an optimized scheduling plan is obtained after optimization adjustment; according to the method, multi-dimensional association is constructed through clients, equipment and materials, global resource optimal allocation of scheduling is achieved, delay risks are found in advance through a twin model, adjustment and optimization are conducted through dynamic feedback, active optimization is achieved based on pre-verification and real-time compensation, decision global optimization coverage is reserved, and dynamic adaptability is improved.

The invention relates to the technical field of logistics information management, and discloses a cross-border logistics information management method and system based on a logistics management platform, and the method comprises the steps: carrying out the analysis of a received customer order in a domestic logistics system, and obtaining a labeling parameter set; querying equipment specification information of an overseas logistics system, and converting the labeling parameter set based on the equipment specification information to obtain an overseas execution instruction; the overseas execution instruction is transmitted to overseas warehouse labeling equipment of an overseas logistics system for automatic labeling operation, and labeling process data is obtained; state monitoring is conducted on the labeling process data, a labeling state report is obtained, the labeling state report is transmitted back to the domestic logistics system, and synchronous confirmation information is generated. According to the method, full-process automation and intelligentization of cross-border logistics labeling information coordination processing are realized.

The invention provides a lean operation method based on production scheduling optimization and equipment OEE monitoring, and relates to the technical field of lean operation, and the method comprises the steps: deploying a multi-source heterogeneous data collection interface, and carrying out the data collection, the method comprises the following steps: acquiring customer order data, material supply state data, production task priority data, process time sequence requirement data and equipment maintenance plan data, performing data preprocessing based on data acquisition, generating a semantic tag set, acquiring the semantic tag set, configuring a production demand dynamic feature set based on the obtained semantic tag set, and generating a production demand dynamic feature set; and calculating a feature influence weight. By deploying a multi-source heterogeneous data acquisition interface, customer order data, material supply state data, production task priority data, process time sequence requirement data and equipment maintenance plan data are acquired at the same time, and a semantic tag set and a production demand dynamic feature set are constructed according to a time sequence structure.

A method of operating a drive through interface of a drive through system includes detecting a presence of a vehicle at the drive through interface based on image data of a portion of a lane at the drive through interface. The method includes, responsive to an order being placed by a customer in the vehicle via the drive through interface, operating a display screen of the drive through interface to notify the customer that the order is not yet ready for pickup and prompting the customer to keep the vehicle at the drive through interface. The method includes, responsive to the order being ready for pickup at a particular pickup window of multiple pickup windows, operating the display screen of the drive through interface to notify the customer that the order is ready for pickup and prompt the customer to drive the vehicle to the particular pickup window.

The invention provides a supply chain toughness and efficiency collaborative improvement method and system and electronic equipment, and the method comprises the steps: obtaining various supply chain management variable data of a supply chain, such as an inventory level, a supplier attribute, a customer order, transportation time, cost, a risk impact resistance coefficient, a recovery coefficient and the like; then, on the basis of the supply chain management variable data, various to-be-evaluated supply chain strategies meeting one or more constraint conditions of a preset continuous supply constraint condition, a preset factory production capacity constraint condition and a preset order fulfillment constraint condition are screened out, and a supply chain comprehensive target value is obtained through a calculation formula of the supply chain comprehensive target value. And outputting the optimal supply chain strategy with the maximum supply chain comprehensive target value. According to the embodiment of the invention, the optimal supply chain strategy which can guarantee the efficiency of the supply chain, can resist risk impact and can be quickly recovered can be determined, the comprehensive performance of the supply chain is further improved, and the sustainable development of an enterprise is maintained.

The invention provides a customer order intelligent production scheduling system based on multi-source data analysis, a product production line is used for producing various products, the product production line comprises a central control device and M production devices, the customer order intelligent production scheduling system is carried in the central control device, and the customer order intelligent production scheduling system comprises a data acquisition module used for acquiring order production scheduling multi-source data, the order scheduling multi-source data comprises N pieces of scheduling order data; the data processing module is used for carrying out preprocessing and feature extraction on the order production scheduling multi-source data and generating input data for optimizing production scheduling; and the intelligent production scheduling module is used for constructing an intelligent production scheduling model to carry out optimization solution, determining an order production scheduling queue, and controlling a product production line to carry out product production based on the order production scheduling queue. By introducing the reusability of production equipment, parallel production scheduling of multiple order queues is realized on the basis of multi-source data, so that the work efficiency of the production equipment is improved while order task allocation is ensured to be completed, and energy conservation and cost reduction are realized while production scheduling is optimized.

The invention discloses a scheduling management method and system for an unmanned finished product warehouse area, and the method comprises the following steps: analyzing material attributes and order information when materials are warehoused, selecting a raw material area FIFO order scheduling strategy or a finished product area customer order clustering strategy according to the type of the warehouse area, candidate stack positions are generated in combination with a warehouse-area-row-column-layer-stack position five-level coding system, the optimal stack position is determined after stack safety verification, and a crown block operation instruction is generated; when the crown block works, acquiring starting and target coordinates, loading the digital twin model of the reservoir area to generate an initial path, and re-planning a high-risk path by adopting an improved path planning algorithm introducing a load coefficient and a congestion index through conflict risk assessment; and during multi-vehicle collaborative operation, task collaboration and path conflict avoidance are realized based on a distributed multi-agent architecture and a preset communication protocol.

The invention provides a logistics pickup and delivery path optimization method and system, and the method comprises the steps: constructing a complete graph according to the obtained customer order data and an arc set between each pair of customer nodes, modeling a vehicle path problem VRPSPDT corresponding to the complete graph into a multi-target optimization problem, decomposing the multi-target optimization problem into a plurality of sub-problems, and carrying out the modeling of the sub-problems, and the plurality of sub-problems are iteratively solved for many times until a logistics goods taking and delivering path corresponding to the customer order data is obtained. In the nth iteration, operation of an evolutionary operator is carried out on current iteration information, a filial generation solution is obtained, the evolutionary operator comprises sequential crossover and random variation, the filial generation solution is input into a pre-trained deep reinforcement learning DRL model, the filial generation solution is optimized based on the DRL model, an improved solution is obtained, and the current population is updated based on the improved solution, and updating the current reference point according to the sub-target value of the filial generation solution. The efficiency, the accuracy and the reliability of determining the logistics goods taking and delivering path are improved.

The invention relates to the field of trademark and commodity supply chain management, and discloses a commodity supply chain management system based on an electronic trademark, and an electronic trademark circulation platform comprises a trademark matcher, an order splitter, a circulation recorder and a payment driver. The trademark matcher is used for matching the electronic trademarks to corresponding commodities, batch numbers and supply quantities; the order splitter is used for splitting a new order according to the customer order data forwarded by the payment driver; the circulation recorder is used for checking and executing a management rule preset by the supply chain management system, and storing data by adopting a WORM type memory to form trusted data resources; the payment driver is used for receiving customer commodity order data. According to the invention, a trademark right holder can manage and control the issuing quantity, the use quantity, the use range and the use qualification of the electronic trademarks, and can obtain supply and marketing data and trademark infringement information of trademark commodities in the market; and the difficulty of consumers in identifying counterfeit trademark commodities is eliminated.

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

A machine-learned predictive model is trained to predict potential for customer complaint. The model is part of an online concierge system. The online concierge system accesses a customer order that includes one or more items. The online concierge system determines input data for an item of the one or more items. The online concierge system determines a prediction value associated with potential for customer complaint for the item by applying the machine-learned prediction model to the input data. The online concierge system provides the prediction value to a picker client device associated with a picker who is assigned the item. The picker client device presents an alert to the picker based in part on the prediction value, and the alert includes a message that is customized to mitigate a cause of potential customer complaint for the item.

A real-time order fulfillment and delivery optimization system that includes the following: an order processing module configured to dynamically receive and process customer orders using an AI-based prioritization algorithm; an inventory management module configured to allocate stock in real time based on stock availability, demand forecasting, and proximity to the warehouse; a route optimization module that uses artificial intelligence to determine the most efficient delivery routes by analyzing traffic, weather conditions and road restrictions in real time; a module for managing last-mile delivery that is configured to dynamically assign delivery agents, redirect deliveries in real time, and minimize failed delivery attempts; a customer interaction module that enables real-time tracking, automatic notifications, and dynamic rescheduling of deliveries based on user preferences; and a predictive analytics module that uses machine learning to improve order processing efficiency and optimize future logistics processes.

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 provides a wooden door customization order flexible splitting and plate utilization rate optimization method and system, and relates to the technical field of wooden product customization intelligent manufacturing, and the method comprises the steps: carrying out the standardization of customer order data, and generating an order vector and a door style assembly constraint; constructing point cloud data through three-dimensional scanning of the plate and reconstructing the point cloud data into a plate digital model; generating a mapping table based on constraint and order execution door style adaptation; the order data, the mapping table and the plate model are combined, the number of assemblies, splicing ports and the waste rate are optimized through flexible order splitting logic, and an order is decomposed into cuttable door assemblies and matched with specifications; and finally, a correction cutting scheme avoiding defects is generated, a production instruction containing the plate utilization rate is output, efficient resource utilization can be achieved, and material waste and production cost are reduced.

The invention discloses a production and sales intelligent management system for glass processing, and relates to a glass production and sales management system. The server collects preset target processing production capacity and stock data through a preset data collection terminal according to the overall supply scheme of the glass, and warehouse stock information, raw material stock, melting capacity, forming capacity and transportation capacity of the target processing production capacity and the stock data are obtained; meanwhile, the insufficient parts of the target processing production capacity and the inventory data are compared with the corresponding customer order information; according to the invention, information collection of the intelligent management system and scheduling guidance of on-demand production are utilized, so that supply and marketing disengagement is avoided.

The invention discloses a multi-cavity vulcanization constraint-containing flexible same-machine parallel batch optimization scheduling method for a rubber tire production workshop, and the method specifically comprises the steps: obtaining basic data of the rubber tire production workshop, and building a multi-cavity vulcanization constraint-containing flexible job workshop scheduling model; analyzing the customer order set to generate order-task-batch data; constructing a scheduling data structure, decoding each candidate scheduling scheme under multiple constraint conditions, generating a corresponding feasible scheduling scheme, evaluating the feasible scheduling scheme, executing iterative optimization, generating a new candidate scheduling scheme, and evaluating the new candidate scheduling scheme; and when a preset termination condition is met, taking the scheduling scheme with the optimal value as the optimal scheduling scheme. The method has the beneficial effects that the maximum completion time of a rubber tire production workshop can be remarkably reduced under the multi-cavity vulcanization constraint, the equipment utilization rate and the order delivery rate on schedule are improved, and good robustness and adaptability are kept when the order structure, the batch demand and the equipment load change.

A method of operating a drive through interface of a drive through system includes detecting a presence of a vehicle at the drive through interface based on image data of a portion of a lane at the drive through interface. The method includes, responsive to an order being placed by a customer in the vehicle via the drive through interface, operating a display screen of the drive through interface to notify the customer that the order is not yet ready for pickup and prompting the customer to keep the vehicle at the drive through interface. The method includes, responsive to the order being ready for pickup at a particular pickup window of multiple pickup windows, operating the display screen of the drive through interface to notify the customer that the order is ready for pickup and prompt the customer to drive the vehicle to the particular pickup window.

A system includes a plurality of stocked items arranged throughout a store for picking according to one or more electronic customer orders, a computing system configured to wirelessly transmit the electronic customer orders, and a mobile scanning device including a display. The mobile scanning device is configured to wirelessly receive an electronic customer order from the computing system. The electronic customer order includes a plurality of ordered items indicating which of the stocked items are to be picked. The mobile scanning device is configured to store an image-based map that indicates locations of the stocked items in the store and arrange at least one of the plurality of ordered items on the display based on the image-based map.

The invention relates to an intelligent distribution management optimization method for an agricultural product supply chain, and the method comprises the steps: collecting multi-source heterogeneous data of production, processing and transportation ends in real time through the Internet of Things, and uploading the data to a cloud end after dynamic grading desensitization and encryption to form a complete traceability record; automatically generating an initial transportation and profit scheme according to a customer order, and dynamically optimizing a route and assembly by using a loss model driven by real-time environment / road condition data; when the actual loss exceeds the threshold value, the transportation strategy is adjusted immediately, integration of data safety, path optimization and loss closed-loop control is achieved, the freshness of agricultural product supply is improved, meanwhile, the transportation cost is reduced, and the performance efficiency and the customer experience are improved.

The invention relates to the technical field of logistics management, and particularly discloses a modern medicine logistics management operation system. The method is used for solving the problems of high cost, low utilization rate and difficulty in achieving SLA caused by separation of a box and a path, imperceptible and non-quantifiable temperature control risk, slow border-crossing disposal, insufficient loading stability and difficulty in balancing cost and carbon emission in medicine cold chain distribution. Comprising a customer order management module, a vehicle-mounted monitoring module, a boxing path optimization module, an execution scheduling module, an accounting module and a tracing alarm module. According to the method, an encasement-path integrated optimization system is driven by an equivalent stability index generated by multi-source monitoring, path feasibility and loading feasibility are unified through early warning trigger constraint injection and local recalculation, dynamic rescheduling and linkage accounting, meanwhile, carbon emission, time window, stability and compliance event monetization are merged into a target function, and the load performance is improved. The cooperation of a temperature control early warning closed loop and an encasement path is realized, the cost and carbon emission are reduced, and the SLA achievement rate and the transportation efficiency are improved.

The present disclosure relates to automated compounding system, methods, and devices for manufacturing compounded compositions. The system comprises a controller including a data processor for receiving via data communication over a data network a customer order, the customer order identifying a Prescription ID associated with a compounded composition, a plurality of discrete stations, including a processing station comprising a processing apparatus configured, in response to instructions received from the controller, to perform one or more process steps required for manufacturing the compounded composition, and a plurality of containers, an industrial robot configured, in response to instructions received from the controller, to displace one or more containers from one discrete station to another discrete station.

The invention relates to the technical field of production dynamic optimization, in particular to a coal mine production and sales decision-making method and system based on intelligent flexible production. Constructing a three-dimensional geologic model of the coal mine, and obtaining a production task generated based on a customer order; generating a production scheduling plan of the coal mine working face according to the three-dimensional geologic model and the production task; obtaining multi-source dynamic data in coal mine working face mining; constructing a coal mine anomaly recognition model to recognize the multi-source dynamic data and the three-dimensional geologic model to obtain a geologic anomaly type and a spatial position; quantifying the multi-dimensional influence of geological anomaly types and spatial positions on a production scheduling plan in combination with the operation state of coal mining equipment and mining historical data of a coal mine working face; and constructing a multi-objective optimization model, and planning the production quota, resource allocation and coal blending schemes of the coal mine working face by taking cost minimization and risk minimization as optimization objectives under the constraint of a production task to generate a dynamic balance production scheme.

A system includes a plurality of stocked items arranged throughout a store, wherein each of the stocked items includes an item identifier (ID) code. A computing system receives an electronic customer order that includes a plurality of ordered items. A mobile scanning device is carried by a user and includes a mobile scanning device ID. The mobile scanning device wirelessly receives the electronic customer order. The mobile scanning device scans a first item ID code on a first stocked item and wirelessly transmits the mobile scanning device ID to the computing system along with the first item ID code indicating that the first stocked item has been picked and scanned. The computing system generates association data that associates the mobile scanning device ID with the first item ID code. The association data indicates that the first stocked item is moving throughout the store with the mobile scanning device.

The invention discloses a remote production control method based on an AI data center, and the method comprises the steps: importing a customer order from an ERP platform by the AI data center, determining a production task, and transmitting a production instruction to a laser cutting machine, a slitting machine and a welding machine according to the production task; the correction machine adjusts correction parameters within a correction range determined by a historical correction database according to the welded material detection data, the module value of the cutting adjustment vector, the module value of the splitting adjustment vector and the module value of the welding adjustment vector; and after each correction is completed, the AI data center updates the customer order and feeds back the customer order to the ERP platform and the remote client. According to the invention, through deep fusion of AI decision, equipment control and remote cooperation, efficient, accurate and adaptive intelligent manufacturing remote control is realized.

The invention discloses an unmanned aerial vehicle resource scheduling method and system based on unmanned aerial vehicle information and multi-modal data. The unmanned aerial vehicle resource scheduling method based on the unmanned aerial vehicle information and the multi-modal data comprises the steps of obtaining customer order information, a candidate pilot portrait and an unmanned aerial vehicle real-time state, then performing multi-modal analysis based on a large language model-visual language model, generating an order vector O, an unmanned aerial vehicle vector U and a pilot vector P, then performing reinforcement learning, and obtaining an unmanned aerial vehicle resource scheduling result; screening out an optimal combination of the pilot and the unmanned aerial vehicle, and generating a suggestion for completing the order; and the screened optimal pilot and unmanned aerial vehicle combination completes the customer order according to the order vector O and the suggestion for completing the order. The invention provides an unmanned aerial vehicle resource scheduling method and system based on unmanned aerial vehicle information and multi-modal data, which can realize efficient and accurate scheduling of unmanned aerial vehicle resources, improve order completion efficiency and quality, reduce scheduling cost, and better meet individual demands of customers.

The invention provides a medium-thickness plate assembly design method for stock plate blanks, and relates to the technical field of ferrous metallurgy and production scheduling. The method comprises the following steps that hierarchical classification of steel types, thicknesses, widths and lengths is carried out on contract orders through multi-level order grouping and an inventory plate blank management mechanism; a greedy strategy is adopted to construct an initial plate assembling scheme with the minimum plate blank use number; global excess material distribution is optimized through a pop-up chain network; and dynamically adjusting a plate assembling scheme in combination with a backtracking search technology so as to respond to the fluctuation of a production line, and generating the opposite punching loss of the non-delegation sub-plate. According to the method, on the premise that customer order requirements are met, the yield and the finished product rate of materials are remarkably improved, second-level dynamic response to production requirement changes is supported, the limitation of a traditional static model is broken through, the resource utilization efficiency and the production flexibility are remarkably improved, and efficient and reliable plate assembling decision support is provided for intelligent transformation of the steel industry.

A machine-learned predictive model is trained to predict potential for customer complaint. The model is part of an online concierge system. The online concierge system accesses a customer order that includes one or more items. The online concierge system determines input data for an item of the one or more items. The online concierge system determines a prediction value associated with potential for customer complaint for the item by applying the machine-learned prediction model to the input data. The online concierge system provides the prediction value to a picker client device associated with a picker who is assigned the item. The picker client device presents an alert to the picker based in part on the prediction value, and the alert includes a message that is customized to mitigate a cause of potential customer complaint for the item.

The invention discloses an automobile service store intelligent order distribution method and system based on multi-objective optimization, and the method comprises the following steps: 1, carrying out the collection of customer orders, and obtaining the information of the customer orders; 2, analyzing the customer order information to obtain customer information; 3, collecting information of the automobile service store; 4, comprehensively processing the service customer information and the automobile service store information to generate store order distribution information; and 5, respectively sending the store order distribution information to the client and the merchant side. According to the invention, through multi-objective optimization, scene-based order distribution and dynamic data integration, the core problem of a traditional order distribution mode in automobile service is systematically solved, and more intelligent order distribution operation is realized.

An online concierge system trains a computer model to map receipt item labels to order item identifiers, enabling the online concierge system to identify discrepancies between receipt items and customer order items. The online concierge system identifies a training set of data comprising quantities of receipt item labels and corresponding orders having quantities of order item identifiers and trains the computer model to predict quantities of order item identifiers based on the set of training data. The online concierge system applies a one-hot encoding for a receipt item label to determine predicted order item identifiers and maps the receipt item label to order item identifiers based on the predictions.

The invention belongs to the technical field of hotel service, and particularly relates to an intelligent system for self-service check-in through a mobile phone, which comprises a pre-check-in management system and a post-check-in management system, and is characterized in that the pre-check-in management system comprises a hotel brief introduction unit; a user registration and login unit; a room reservation unit; a catering reservation unit; the post-check-in management system comprises a customer order management unit; a hotel guest room management unit; a catering order management unit; a self-service check-out management unit; the system is reasonable in structure, reduces the workload of foreground workers, shortens the waiting time of guests, provides convenient and personalized services, enhances the satisfaction degree of the guests, reduces the labor cost and operation expenditure through an automatic process, can collect guest data, and provides decision support for hotel operation.