52 results about "Customer order" patented technology

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 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.

An automated retail store system for providing automated storage of multiple items and scheduled handling of a customer order for said items at a single retail site, including: a store control system adapted to operate under the control of control signals generated based on order information for an order by a customer, said order information including information representing one or more items and a pickup time; a plurality of storage channels for storing different respective items; a retrieval system adapted to operate under the control of said store control system to: retrieve a plurality of said items in said order information from said channels, and place a plurality of said retrieved items into a delivery container; a conveyor system adapted to operate under the control of said store control system to: move said delivery container to either a storage rack or a locker compartment based on said pickup time, and move said delivery container from said storage rack to an available said locker compartment based on said pickup time; and a verification system adapted to operate under the control of said store control system to: verify an identity of said customer or a unique identifier for said order, and allow said customer to access said items in said locker compartment only when said identity or said identifier has been successfully verified.

The invention belongs to the technical field of automobile flexible manufacturing, and particularly discloses a method and system for analyzing and issuing technological parameters in real time in automobile flexible manufacturing, and the method comprises the following steps: a customer submits required configuration orders of appearance, interior decoration and functions through an Internet of Things customization platform; the Internet of Things customization platform converts the customer order information into a standardized data format and sends the data format to a manufacturing execution system MES; and the MES system presets process basic data required by each vehicle type and configuration rules of the corresponding vehicle type, analyzes production process information of the vehicle required by a client, issues the information to the production equipment through the IOT platform, and executes the steps of collecting the state and production data of the production equipment, judging whether the production equipment works normally or not and judging whether the product quality reaches the standard or not. By adopting the technical scheme, based on a two-way communication mechanism between the MES and the IOT platform, rapid customization, accurate issuing and real-time adjustment of the process data are realized, so that the efficient production requirement of flexible manufacturing of automobiles is met.

The invention provides an automobile foot mat multi-style mixed flow production scheduling method based on customer order data, and the method comprises the steps: carrying out the standardization and feature vector generation of order process parameters, combining with the real-time state collection of production equipment, building a static process similarity and dynamic equipment response cost evaluation model, and fusing multi-source data to analyze the dynamic switching cost. The method comprises the following steps: establishing a scheduling sequence, realizing scheduling sequence optimization by using an improved NSGA-II multi-target genetic algorithm, comprehensively considering switching cost, equipment utilization rate and production line rhythm, finally dynamically selecting an optimal scheduling scheme through a TOPSIS decision method and a workshop load rate, and continuously correcting and evaluating model parameters by using an online incremental learning algorithm, thereby realizing scheduling sequence optimization. According to the method, the production scheduling efficiency and the resource utilization rate are improved, the equipment switching loss is remarkably reduced, and flexible production self-adaptive optimization is realized.

The invention discloses a high-density storage system order batch optimization method and system under uncertain factors. The method comprises the steps of obtaining order data and performing preprocessing; inputting the preprocessed data into an order batch sorting model, and carrying out order task allocation according to an output result; and the order batching model is established by combining customer order returning tendency prediction, fusing a dynamic decision rule and a batching clustering algorithm with capacity limitation and taking minimization of all order sorting completion time as an objective function. According to the method, the order batch sorting model fusing the dynamic decision rule and the batch clustering algorithm with capacity limitation is provided, the orders can be batched in real time according to the real-time arrival state and number of the orders and the capacity of the workbench in the order arrival process, the defect of fixed time window batch is avoided, resource allocation can be flexibly adjusted, and the efficiency of batch sorting is improved. The equipment utilization rate is effectively improved; and in combination with customer order returning tendency prediction, the order batch sorting result is more accurate and efficient.

An order fulfillment facility with a delivery system is disclosed having plurality of mobile robots with containers to bring customer orders and / or power supply device / s to UAV / s for final delivery. UAV leaves the discharged power supply devices that are received in empty container and transported to storage area for replenishment and diagnostic checks. Order fulfillment operations along with power supply device charging and monitoring is performed to achieve timely delivery of customer orders.

1. The name of the design product: user portrait display graphical user interface for electronic equipment. 2. The use of the design product: the design product is used for an electronic device. 3. The design points of the design product: the interface content of the graphical user interface. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: the graphical user interface is used for user portrait dimension display interface. 6. The human-computer interaction mode of the graphical user interface: the front view is the user portrait dimension display interface, which displays order index column chart, portrait score and rating, customer order total amount, order total tonnage, annual order trend and other information.

Storage and picking system (10) for picking individual items of a plurality of customer orders (34), wherein the orders (34) comprise at least one multi-line order (38) and a plurality of single-line orders (36), wherein each order line (40) of each of the orders (34) corresponds to an item type, each of which is stored in a source container (20), wherein the system (10) comprises: a storage facility (18) which is set up to store a large number of the source containers (20); a goods-to-person, WzP, station (12) with a number of buffer locations (14) for destination containers (16); a transport system (22) designed to transport the source containers (20) from the storage facility (18) to the WzP station (12), and vice versa; and a control device (24) which is set up to generate a source container sequence (44) which represents a sequence in which the source containers (20) are to be provided for the successive processing of the orders (34) at the WzP station (12) by means of the transport system (22); wherein the control device (24) is further configured to bundle the single-line orders (36) into a bundle order (48) which represents a further multi-line order (38) and which replaces the single-line orders (36) during the generation of the source container sequence (44).

The invention discloses a quotation management method and system based on a product cost guiding principle, and the method comprises the following steps: setting a plurality of product service options, presetting a corresponding income item and a corresponding cost item for each product service option, and building an income-cost architecture system; respectively calculating a non-supplier cost reference index and an operation cost reference index according to an apportionment rule; selecting a target cost item corresponding to the customer order business demand according to the target product service, and further generating a supplier quotation list; a target income item is selected according to the target product service, a target cost item in the supplier quotation is associated and matched with the target income item, and based on non-supplier and operation cost reference indexes, predicted self-operation transportation mileage and the like, various earnings are calculated, and a customer quotation is generated; and after the customer order business is completed, according to customer and supplier quotations and the like, respectively generating account receivable and account payable, submitting settlement auditing, and executing collection / payment after passing the auditing.

To improve convenience. [Solution] A sales data processing system in which a customer orders products and receives the ordered products at a store, comprising: a list display unit that displays the ordered products to a store employee in a list; an input unit that accepts input from the store employee for each of the ordered products displayed in the list as a picking operation; a declaration unit that declares the completion of the picking operation; and a generation unit that generates a message related to the picking operation when a declaration is made by the declaration unit, wherein the generation unit generates different messages depending on the input status from the input unit.

The invention relates to a moving service pricing method, system and device based on artificial intelligence and a medium. The method comprises the following steps: constructing a standardized article database containing article identifiers, basic unit prices and an attribute matrix, and defining dimension rules of floors, carrying distances and transportation distances; analyzing a customer order, extracting information of two places and an article identifier set, and calculating a corresponding coefficient; and inputting the basic unit price, the attribute matrix and the coefficient into a pricing model to generate comprehensive unit prices, and summing the comprehensive unit prices and performing global adjustment to obtain a final total price. According to the method, the standardization, transparency and accuracy of moving service pricing are improved by constructing the standardized article database and the quantitative dimension rule and combining the artificial intelligence pricing model, the problems of price randomness and consumption disputes caused by high subjectivity and non-transparent information in a traditional pricing mode are effectively solved, and the method is suitable for popularization and application. And meanwhile, the pricing efficiency and the user experience are improved.

The invention provides a vehicle software version determination method and device, electronic equipment and a storage medium, and relates to the technical field of vehicle configuration management. The method is applied to a vehicle configuration management system, and comprises the steps of obtaining a customer order configuration item value submitted by a target customer, and determining a to-be-assembled part group based on the customer order configuration item value; and then creating a hardware configuration item class and / or a software configuration item class for each part class included in the part group. And further based on a preset selection logic rule, automatically selecting a hardware configuration item value from the hardware configuration item class, and / or automatically selecting a software configuration item value from the software configuration item class. And in combination with the hardware configuration item value and / or the software configuration item value, based on a pre-stored software version configuration item class, selecting a software version for the to-be-delivered vehicle corresponding to the preset vehicle type. The accuracy of the vehicle software version and the vehicle delivery efficiency can be improved.

The application discloses a raw material recommendation method and system in a printing process, and the method comprises the following steps: determining product development size and product printing p number; determining product piece number and material loss rate according to the on-machine size and the product development size; determining material piece number according to the product piece number and the product printing p number; determining product residual p number according to the product printing p number, the product piece number and the material piece number; and determining the recommendation sequence of the raw material according to the material loss rate, the material piece number and the product residual p number according to a preset raw material recommendation strategy, so as to recommend the raw material for the product to be printed. The method can comprehensively consider the loss rate, the material piece number and the residual p number of the raw material, and can reduce waste to the maximum extent. Meanwhile, when a customer order is received, the loss of different raw materials can be determined according to the product printing p number in the order at the first time, so that the cost generated by different raw material combination schemes can be quickly estimated, the customer can be quoted, and the order transaction success rate is improved.

This invention discloses a ceramic processing system and brick sorting process, belonging to the field of ceramic tile manufacturing equipment. It includes a production scheduling department, a first inkjet printing department, a sintering and forming department, a first diversion and transfer department, a first brick storage department, a processing department, a second inkjet printing department, a second diversion and transfer department, and a second brick storage department, arranged sequentially. Based on customer order information received by the production scheduling department, the ceramic processing system uses an inkjet printer to coat the surface of the ceramic tiles and simultaneously prints identifiable barcodes on them. The barcodes record the type of ceramic tile. Different types of ceramic tiles can be simultaneously fed into the kiln for sintering, fully utilizing the kiln's processing capacity and reducing energy consumption. This ceramic processing system can simultaneously produce different types of ceramic tiles and perform graded management of tiles of the same type, achieving flexible production of small batches of ceramic tiles to meet customers' personalized needs.

The present application relates to the lace fabric supply chain management technical field.The present application relates to a kind of lace fabric supply chain management method based on big data.It includes the following steps: S1, the inventory data of enterprise, sales data are acquired, while the lace type corresponding to the lace fabric of enterprise is acquired, and the sales customer list is extracted in sales data;The present application realizes accurate prediction by multidimensional data fusion, on the one hand, combined with customer group portrait and network heat trend data, extract accurate heat information and calculate fluctuation coefficient, clear market heat influence degree on customer order, on the other hand, combined with historical order difference analysis screening reference period, with the maximum quantity difference limit prediction range, avoid prediction data deviate from actual demand, can let enterprise master customer real demand in advance, both reduce the inventory accumulation caused by prediction too high, also avoid the shortage problem caused by prediction too low, greatly reduce supply chain resource waste.

The application discloses a tobacco order vehicle distribution optimization method based on deep reinforcement learning, and the optimization method is as follows: step one: using a data structure of a graph to represent customer orders to be distributed on the day, and obtaining a set of available distribution vehicle models as the input of an order vehicle distribution model; step two: designing a state space and an action space of the order vehicle distribution model; the application has the beneficial effects that: an A3C reinforcement learning algorithm is used to autonomously learn an order vehicle distribution strategy, a double-pointer decoding framework is proposed for generating a distribution order and a distribution vehicle sequence in view of the pain point that a transportation vehicle demand is not clear in actual distribution business, a design mode of a distribution environment state, an action and a reward is described in detail, and finally, through experiments on actual order data provided by a company, the method can generate a distribution distance shorter and a total number of used vehicles less in a very short time compared with a traditional heuristic solver.

A system includes a plurality of location indicators for arrangement throughout a store that includes a plurality of stocked items. Each of the location indicators defines a different area of the store. The system includes a computing system configured to generate a location map that defines a plurality of sections in the store. Each section includes a plurality of areas of the store defined by different location indicators. The location map defines how the sections and the areas are arranged. The system includes a mobile scanning device that is configured to wirelessly receive an electronic customer order including a plurality of ordered items. The mobile scanning device determines a location value associated with a location indicator and arranges at least some of the plurality of ordered items on the display based on the determined location value and the location map.

Systems and methods relating to management of delivery fleets are disclosed. Such systems and methods include using functionally-aligned machine learning engines to manage aspects of fleet dispatch and routing, inventory level adjustments, and order optimization. The techniques may be applied to manage an automated fleet delivery services system controlling a fleet of autonomous delivery devices (e.g., UAVs) to deliver products from a plurality of distribution hubs to fulfil customer orders. The interconnected machine learning engines enable efficient management of various aspects of such automated fleet delivery services in real time, including optimization of fleet distribution, optimization of inventory distribution, and optimization of order distribution. Optimization of order distribution may be obtained by dynamically adjusting delivery charges for orders based upon current fleet capacity and inventory levels.

Systems and method for supplying and distributing prepared or partially-prepared meals are configured to (i) produce meals made to order, (ii) transport the meals to one or more chilled automated dispensers located in a convenient location readily accessible to multiple customers for pickup, and (iii) maintain cold-chain integrity by storing the meals in one or more chilled environments immediately after preparation and until pickup by the customers. A customer (user) device facilitates placement of a customer order for one or more meals. A kitchen facility produces the meals and one or more dispensers stores the meal(s) in a chilled environment until pickup by the customer. A delivery vehicle provides refrigerated transport of customer orders from the kitchen facility to the dispenser(s), and a central control system manages communications and operations.

1. The name of the design product: sales order graphical user interface for electronic device. 2. The use of the design product: for an electronic device. 3. The design features of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design features: front view. 5. The electronic device is a conventional design, omitting the rear view, left view, right view, top view and bottom view. 6. The use of the graphical user interface: for processing plant to enter customer order information and processing product details. 7. The human-computer interaction mode of the graphical user interface: the front view is the default display interface; clicking "manual setting" on the top of the front view can enter interface change state figure 1, and clicking "confirm" in the lower right corner of interface change state figure 1 can enter interface change state figure 2.

A method and apparatus for dynamically assigning keyboard scan codes during the manufacturing process of an Information Handling System (IHS). The method involves loading an operating system onto the IHS, utilizing a diagnostic tool to read customer order data, and determining appropriate keyboard scan code settings based on the order data and operating system requirements. The default BIOS scan codes are then modified to align with these settings, enabling the use of a generic keyboard to support multiple configurations and ensuring all keys have defined functions relevant to the operating system and customer preferences.

The invention relates to the technical field of big data and production plans, and discloses a novel intelligent excellent and special steel wire scheduling method based on a big data model, and the method comprises the steps: extracting the material characteristic parameters of each steel grade order, and constructing a material technology knowledge graph; extracting a material volatility index and an order stability index of each customer order, and marking customers; calculating the process window conformity by order and production line, and dynamically calculating the residual available capacity of each production line by taking the process window conformity as a constraint; taking the conformity of the process window and the residual available capacity as constraint conditions, determining an order and production line combination meeting production constraints, performing scoring, and generating a production scheduling scheme for the order to be scheduled according to a scoring result; and production is executed according to the production scheduling scheme, and the pulling speed of the current production line is adjusted in a self-adaptive mode. According to the method, accurate configuration and dynamic optimization of production line resources can be realized according to the multi-variety and small-batch order characteristics of the excellent and special steel wire manufacturing industry.

The invention provides a customer demand work order processing method, device and equipment based on AI voice interaction, and the method comprises the steps: transmitting a voice stream to an ASR engine in real time, and outputting text data with a timestamp; performing intention classification on the text data, identifying a user core request, and generating a to-be-processed work order according to the user core request; determining the priority level of the to-be-processed work order according to a level calculation formula and the work order information of the to-be-processed work order; adding the to-be-processed work orders into a global work order queue, and sorting the to-be-processed work orders according to the priorities to obtain a corresponding sorting result; and after entering the dispatching process, if there is no guest room service robot which can be scheduled and is adaptive to the execution demand of the to-be-processed work order, screening out an optimal executor with the highest matching degree from the on-duty employees according to a preset intelligent dispatching strategy and the to-be-processed work order, and completing the automatic dispatching of the to-be-processed work order. According to the invention, the overall efficiency and accuracy of hotel customer demand work order processing can be significantly improved.

The embodiment of the invention provides a designated driving order dispatching method and device, electronic equipment, a computer readable storage medium and a program product, and relates to the technical field of computers. According to the method, the order receiving judgment is performed by combining the customer order information and the system load state, so that the system operation stability and the order receiving reasonability are improved; after order receiving is determined, target suppliers with service capability are screened out based on customer order information, and candidate drivers are obtained, so that preliminary matching of transport capacity is realized; when the number of the candidate drivers is insufficient, sorting is carried out according to the commercial value data, the quality data, the driver coverage data and the service score of each target supplier, a priority sequence is established, and the search range of the target suppliers is expanded and the candidate drivers are dynamically supplemented according to the sequence, so that the probability of successful order dispatching in a transport capacity sparse region is improved; the designated driver order is pushed to all candidate drivers and is distributed to the first receiver, efficiency and fairness are both considered, and the order distribution success rate and the resource utilization rate are effectively improved.

The invention discloses an analysis method and system for large-scale multi-type customer order data, and the method comprises the following steps: extracting a remark demand text of the order data, carrying out the data preprocessing, extracting a hidden layer feature vector, decomposing the hidden layer feature vector into a plurality of business semantic features, outputting a decoupling enhancement feature, and carrying out the data preprocessing; calculating the similarity between the intention semantic feature and a preset intention prototype, and outputting an intention activation distribution vector; calculating a matching confidence coefficient for each knowledge domain, and generating a knowledge feature vector; separating a corresponding semantic subspace feature from the decoupling enhancement feature, calculating a gating coefficient with an intention activation distribution vector, performing element-by-element product operation on the gating coefficient and a knowledge feature vector to obtain knowledge representation, and splicing the knowledge representation and the decoupling enhancement feature to obtain a fused semantic feature; and performing intention recognition and entity extraction based on the fused semantic features, and outputting intention probability distribution and a service entity sequence. According to the invention, the automation degree and reliability of customer special demand analysis can be improved.

The application discloses a remote production control method based on an AI data hub, which comprises the following steps: an AI data hub imports customer orders from an ERP platform, determines a production task, and sends a production instruction to a laser cutting machine, a slitting machine and a welding machine according to the production task; a correction machine adjusts a correction parameter within a correction range determined by a historical correction database according to material detection data after welding, a modulus value of a cutting adjustment vector, a modulus value of a slitting adjustment vector and a modulus value of the welding adjustment vector; after each correction is completed, the AI data hub updates the customer order and feeds back to the ERP platform and a remote client. Through deep integration of AI decision, equipment control and remote collaboration, the application realizes efficient, accurate and self-adaptive intelligent manufacturing remote control.

The application discloses a kind of main plan compilation system and method for solving customer order change.The main plan compilation method for solving customer order change is mainly realized in the main plan adjustment service based on local change strategy, the main plan local recompilation service based on reinforcement learning algorithm, capacity calculation service of system service layer.The system judges the numerical relationship between new demand capacity and residual capacity after receiving customer order change information, executes the main plan adjustment method based on local change strategy, generates new main plan compilation record according to the customer order increment allocation strategy based on multi-day residual capacity ratio and the non-urgent customer order reduction strategy, guarantees the premise of making full use of residual capacity, replaces, supplements original main plan, if still cannot meet the demand of customer order change by using local adjustment strategy, executes the main plan local recompilation method based on improved reinforcement learning algorithm, and recompiles part of main plan for the remaining days after change date.The application can quickly respond to customer order change, reduce large-scale adjustment of original main plan, and reduce the chain effect on material demand plan and other plans.

This invention relates to the technical field of industrial manufacturing and production management, and in particular to a multi-factory tire production collaborative scheduling and resource allocation system. The system includes a unified order management center, a data acquisition unit, a data processing and analysis unit, a factory capability twin model unit, a collaborative scheduling and resource allocation unit, an operation interface unit, and a report generation and query unit. The unified order management center is used to collect, parse, and manage all customer orders, forming a unified global order pool. The data acquisition unit includes equipment sensors, logistics sensors, and a manual input terminal. Equipment sensors are installed on production equipment in each factory to collect real-time information on equipment operating status, production parameters, and production progress. Logistics sensors are installed in warehouses and transport vehicles. This system reduces equipment idle time and production cycles, improves the efficiency of tire production, and optimizes resource allocation and production task distribution.

The invention belongs to the technical field of computers, and particularly relates to a steel trade order signing task automatic generation and field assistance scheduling method. The method aims at solving the problems that a traditional order signing process depends on manpower, the scheduling efficiency is low, and the strain capacity is weak. Customer order data in an enterprise resource planning system are acquired in real time, multi-dimensional feasibility evaluation is performed in combination with customer credit, inventory state and logistics capability, and a structured order signing task work order is automatically judged and generated; an outworker dynamic resource pool containing four-dimensional attributes of position, load, skill and efficiency is constructed, an improved mixed integer linear programming algorithm is adopted to realize multi-target optimization scheduling, and geographic proximity is considered. The method also integrates thermodynamic diagram visualization and load prediction functions, and supports pre-deployment and manual intervention. By means of the scheme, the order signing automation rate exceeds 97%, the order signing period is compressed to be within 8 hours, the overall response time is shortened by 42%, and the abnormal overstock rate is reduced to be below 5%.