175 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 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 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 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 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 relates to the technical field of text travel service management, and discloses a text travel service management method and system, and the method comprises the steps: obtaining ticket business data and historical ticket booking data of a text travel field, predicting the customer order amount of a prediction day, and generating a text travel configuration scheme; monitoring the entrance and exit passenger flow volume of the travel field in real time to obtain a first passenger flow volume, and if early warning is carried out, generating a first early warning task; acquiring a passenger flow distribution condition according to the information of each sub-region, and if early warning is carried out, generating a second early warning task; and dynamically generating an optimal sightseeing path by combining the sightseeing influence factor of each sub-region. According to the invention, the tourist scale can be pre-estimated in advance to generate a tourist configuration scheme, the entrance and exit passenger flow volume is monitored in real time, whether the overall passenger flow exceeds the carrying capacity is found in time, early warning measures are taken, a corresponding emergency plan is started, and an optimal tourist route is dynamically generated to guide tourists to reasonably plan the tourist travel. And the sightseeing fluency and comfort of the tourists are improved.

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 relates to the technical field of computers, in particular to an internet fresh agricultural product distribution management system which comprises an order management module, a logistics management module, an inventory management module and a data analysis module. The order management module is used for processing customer orders, including order receiving, processing, tracking and state updating, so as to ensure efficient processing of the orders; the logistics management module is used for monitoring the temperature and humidity of the fresh agricultural products in the transportation process and planning a distribution route so as to ensure that the fresh agricultural products are delivered to customers in the shortest time; the inventory management module is used for monitoring the inventory level of the fresh agricultural products so as to ensure that enough inventory meets the order requirements; and the data analysis module is used for optimizing a purchasing strategy through data analysis. According to the invention, optimization of the fresh agricultural product distribution process is facilitated.

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.

The invention provides a production order management method and system based on artificial intelligence, and relates to the technical field of production management. The method comprises the steps of obtaining predicted order information according to historical order information and industry trend information; according to the customer order information, the prediction order information and the product inventory information, generating supplementary library order information; according to the customer order information, the research and development order information and the replenishment order information, determining an order execution sequence and a production process sequence of the order; according to the order execution sequence, the production process sequence and the equipment capacity, a production equipment sequence and a production plan are determined; if the order insertion information is received, the order execution sequence, the production equipment sequence and the production plan are updated according to the information of the orders being executed, the order execution sequence and the order insertion information; if the production plan of the production equipment needs to be updated, the real-time capacity of the production equipment is determined, and the production equipment sequence and the production plan are updated. And the flexibility and response speed of production order management are improved.

The invention relates to the technical field of enterprise resource planning, and discloses a sales method, system and device and computer equipment, the sales method is applied to a management platform, and the method comprises the steps: obtaining customer order request information of each store in a preset time period, and generating an order and an order number of a corresponding store based on the customer order request information; based on the order number and a preset auditing condition, auditing the order of the corresponding store; after the verification is passed, a store delivery order is generated based on the order, the market inventory condition and the delivery strategy; and picking up the goods from the market warehouse to the corresponding store warehouse based on the store delivery order and the order deposit number, so that the store warehouse picks up the goods to the customer according to the goods picking application, the exclusive auditing rule is made for different market stores, the belonging market is accurately determined, and the purpose of accurate goods picking of the customer is achieved.

The present application provides a customer order intelligent scheduling system based on multi-source data analysis. The product production line is used to produce multiple products and includes a central control device and M production devices. The customer order intelligent scheduling system is installed in the central control device and includes: a data acquisition module for acquiring multi-source data for order scheduling, where the multi-source data for order scheduling includes N scheduling order data; a data processing module for preprocessing and feature extraction of the multi-source data for order scheduling to generate input data for optimizing scheduling; an intelligent scheduling module for constructing an intelligent scheduling model for optimization and solution, determining an order scheduling queue, and controlling the product production line to produce products based on the order scheduling queue. By introducing the reusability of production equipment, parallel scheduling of multiple order queues is realized based on multi-source data, so as to improve the working efficiency of production equipment while ensuring the completion of order task allocation, and achieve energy conservation and cost reduction while optimizing scheduling.