19 results about "Order scheduling" patented technology

This invention relates to the field of intelligent manufacturing and industrial automation technology, specifically to an intelligent order scheduling and material management system for a milling and turning workshop. It includes a task parsing module, a feature mapping module, a state acquisition module, a potential field construction module, a scheduling calculation module, and a feedback execution module. The system receives task drawing data and bill of materials data, extracts geometric feature bounding dimension data and machining tool vector sequence data, and tensors and encodes them. It collects the current tool library configuration status parameters of the target machining equipment and the physical attribute parameters of work-in-process, constructs a continuous potential energy field distribution matrix, and dynamically refreshes it. It calculates the potential energy gradient descent direction of the multidimensional machining feature tensor flow, generates a task queue of associated paths and a theoretically estimated execution cycle time sequence, issues CNC machining task plans and automated guided vehicle (AGV) allocation instructions, and feeds back the actual cycle deviation correction value for closed-loop updates. This invention makes the scheduling results closer to the actual executable state of the workshop.

The application discloses an order production scheduling and real-time early warning management system, comprising an order scheduling module, a process tracking module, a progress early warning module and a resource board module, wherein the order scheduling module is configured to determine scheduling information according to order product information; the process tracking module is configured to track the current state of the product by associating the ERP, MES and PLM systems; the progress early warning module is configured to conduct multi-level comprehensive risk early warning according to the current date, the delivery date and the current state; and the resource board module is configured to summarize order product production information and conduct statistical analysis. The application also discloses an order production scheduling and real-time early warning management method. The application can deeply integrate specific business knowledge, realize full closed-loop management from intelligent scheduling to real-time early warning, fill the digital gap between the planning and execution layers, and improve the overall operation resilience, delivery reliability and customer satisfaction.

This application provides a method and apparatus for optimizing ride-hailing order scheduling based on adaptive fairness constraints using reinforcement learning, belonging to the field of ride-hailing order scheduling technology. The method includes: acquiring pressure index data of the ride-hailing market at the current time step, fairness evaluation index data corresponding to the current time step and multiple historical time steps; determining historical statistical features based on the fairness evaluation index data corresponding to multiple historical time steps; determining a target fairness threshold based on the pressure index data and historical statistical features; determining the degree of constraint violation of the target fairness threshold based on the fairness evaluation index data corresponding to the current time step, and using Lagrange constraint reinforcement learning to determine the parameters of a neural network model used for ride-hailing order scheduling, and determining the updated neural network model based on the parameters of the neural network model; and scheduling ride-hailing orders based on the updated neural network model. This application can optimize ride-hailing order scheduling.

The application belongs to the technical field of steel production and artificial intelligence, and relates to a construction method of a hot rolling scheduling hierarchical reinforcement learning model, comprising the following steps: obtaining a single product order data set according to hot rolling production daily plan order data, and constructing a hot rolling production scheduling sample data set based on the data set; presetting a daily rolling total number, constructing a hot rolling scheduling hierarchical reinforcement learning model corresponding to all rolling processes, training the model by using a training sample data set, and generating an overall rolling order scheduling sequence; and verifying the trained model by using a verification sample order data set to obtain a final hot rolling scheduling hierarchical reinforcement learning model; the application improves model decision efficiency and training stability, effectively adapts to order structure changes and process parameter updates, ensures that the generated scheduling scheme meets process feasibility and production stability requirements, and significantly reduces the calculation cost caused by invalid action exploration.

The invention relates to the technical field related to work order management, in particular to a work order circulation management method and system for automobile maintenance, and the method comprises the steps: introducing workshop real-time operation data and work order execution scene parameters, and generating a conflict work order scheduling instruction set; and triggering a work order circulation linkage scheduling mechanism according to the abnormal type and priority level of work order circulation, and performing cooperative execution management and control on a plurality of edge computing nodes and each work order execution link according to the consistency deviation of the system process. The technical problems of exception identification lagging, scheduling response stiffness and work order execution overall efficiency limitation caused by lack of dynamic perception of the whole process of the maintenance task and frequent resource contention are solved, dynamic scheduling path planning of the maintenance task flow is realized, process conflicts are effectively avoided, exception types and priorities are identified, and the efficiency of work order execution is improved. Cooperative management and control are carried out on the plurality of edge nodes and execution links according to the process consistency deviation, and the technical effects of work order circulation robustness and closed-loop processing capability are enhanced.

The application discloses a kind of based on multi-version management's work order production scheduling method, device and storage medium, by creating and formal production scheduling environment isolated storage draft version, make planner free to simulate production scheduling under the premise of not affecting actual production, avoid the covering of formal plan by misoperation, realize the parallel storage and independent adjustment of multiple schemes;By generating operation instruction object containing pre-adjustment state and post-adjustment state, and based on withdrawal or recovery instruction, respectively according to pre-adjustment state or post-adjustment state update gantt chart display and back or recovery process state, reversible adjustment of single operation is realized, the consistency of interface display and back-end process state is ensured;By publishing satisfactory draft version as formal production scheduling plan, the smooth transition from simulation scheme to production execution is realized.

The application relates to the technical field of data processing methods, in particular to a service order dynamic allocation method and system based on multidimensional data analysis. The method comprises the following steps: determining the emergency degree of a current temporary order according to the user information of a target user and the temporary order information of the current temporary order; determining an insertable group according to the arrangement order information of each arranged order in a service group; combining the temporary order information and the arrangement order information to determine the possible delay degree of the current temporary order after being inserted into the insertable group and the possible interference degree of the current temporary order to other orders in the insertable group; determining the total order completion easy degree of the insertable group according to the arrangement order information; and combining the possible delay degree, the possible interference degree and the total order completion easy degree to determine the target insertion group of the current temporary order. Through the technical scheme, global order scheduling optimization is realized, and the stability and efficiency of the overall task flow are maintained.

This application provides an order scheduling method and a computer storage medium. The method includes receiving travel order information, obtaining service provider information and historical travel service information of at least two candidate service providers corresponding to the travel order information; determining expected revenue based on the travel order information, service provider information, and historical travel service information; and allocating orders to at least two candidate service providers based on the expected revenue to obtain an order scheduling result. Expected revenue represents the overall revenue, which includes not only the short-term revenue corresponding to the travel order but also the probability of candidate service providers offering travel services in the future, reflecting medium-term and long-term revenue. From the perspective of overall revenue, this increases the number of service providers offering travel services in the future predetermined time period, improving the travel experience.

1. Name of the product in this design: Graphical User Interface for Production Scheduling Management of Electronic Equipment. 2. Purpose of this design: An electronic device. 3. The key design features of this product are the combination of shape and pattern. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Purpose of the graphical user interface: To enable human-computer interaction for program operation and to manage order scheduling. 6. Human-computer interaction method of graphical user interface: Clicking the process card in the main view will enter the interface change state diagram 1. Clicking the confirmation interface in the interface change state diagram 1 will enter the interface change state diagram 2. Clicking the save interface in the interface change state diagram 2 will enter the interface change state diagram 3.

The application relates to the field of industrial processing, in particular to a work order scheduling method and device with optimized energy consumption, electronic equipment and a computer readable storage medium. The method comprises the following steps: obtaining scheduling parameters of each work order to be scheduled from a plurality of work orders to be scheduled, wherein the scheduling parameters comprise a work order process, a starting temperature and an ending temperature; performing work order scheduling on the plurality of work orders to be scheduled based on the work order process to obtain a plurality of simulated scheduling schemes; calculating an energy consumption value corresponding to each simulated scheduling scheme according to the starting temperature, the ending temperature, a work order interval length and a device thermal function; and selecting a target scheduling scheme from the plurality of simulated scheduling schemes according to the energy consumption value. The work order scheduling method and device with optimized energy consumption, electronic equipment and computer readable storage medium provided by the application can achieve the technical effect of reducing work order energy consumption while improving work order processing efficiency.

The application discloses an order scheduling method and device, equipment, medium and program product, and belongs to the field of travel services. The method comprises the following steps: displaying a first transport capacity type selected by a passenger account; in the case that no order is received by the first transport capacity belonging to the first transport capacity type, displaying a recommended transport capacity list, wherein the recommended transport capacity list comprises at least two candidate transport capacities and transport capacity information of the at least two candidate transport capacities, the at least two candidate transport capacities are idle transport capacities belonging to other transport capacity types except the first transport capacity type, and the at least two candidate transport capacities comprise a second transport capacity; and in response to a selection operation on the second transport capacity, displaying pick-up information corresponding to the second transport capacity. In this way, the freedom of user selection of transport capacity can be improved, the order dispatching efficiency can be improved, and the range of available transport capacity can be expanded by continuously dispatching orders to the pick-up object through the ride-hailing platform to other transport capacities (candidate transport capacities) that are not selected.

This invention relates to a dynamic optimization method for the entire steel production process. The method first constructs a full-process production scheduling rule engine library. After feature preprocessing, an adaptive unit process structure and a fully connected aggregated process structure are generated through a general modeling framework. Then, a hierarchical optimization framework is built. The monthly plan fusion optimization model adopts a three-stage progressive optimization strategy of capacity balancing, production line division of labor, and order scheduling, combined with a daily scheduling fusion optimization model to achieve dynamic scheduling. Finally, an intelligent solution algorithm engine is used to match and solve the problem and output the results. This application can solve the problems of insufficient model generalization ability, process coordination failure, and difficulty in balancing solution efficiency and global optimization objectives in existing technologies. It improves the model's cross-scenario generalization ability and the degree of coordination of the entire process, significantly reduces solution time, and significantly improves order delivery rate and inventory control level.

This invention provides a method and system for intelligent work order scheduling and resource optimization in the energy industry, relating to the field of intelligent energy management technology. The method includes parsing work order text to generate work order type feature vectors and work order element sets; constructing a dynamic relationship graph based on an energy resource knowledge graph; recommending the optimal work order scheduling scheme; calculating the fit degree and outputting the optimal resource combination; generating a complete work order configuration scheme; executing the scheme and optimizing it in real time through dynamic monitoring and feedback, outputting the final scheduling execution result. The system includes modules for work order parsing and feature extraction, dynamic relationship graph construction, scheduling scheme recommendation, resource fit optimization, scheduling plan generation, and execution monitoring and feedback optimization. This invention achieves intelligent scheduling and resource optimization throughout the entire work order scheduling process, significantly improving plan achievement rate, reducing time deviation rate, shortening emergency repair response time, and improving resource utilization.

The purpose of this application is to provide a manufacturer joint improvement incentive method and device based on quality cost allocation. Through the collaborative work of the FA report parser and the quality cost allocation engine, qualitative root cause descriptions are transformed into responsibility ratios accurate to the percentage, eliminating ambiguities in manual negotiation. In addition, the quality cost allocation engine integrates product complexity coefficients and manufacturer capability profiles, ensuring that the cost allocation results are both fair and guiding, and promoting high-potential manufacturers to receive more improvement support. During the order scheduling stage, the resource scheduling linkage module triggers corresponding order acceptance actions based on the read current quality score value, realizing the automatic conversion of points into business actions, ensuring that incentive measures can be implemented without manual intervention.

This invention discloses a method, system, and medium for intelligent work order scheduling service management in property management, relating to the technical field of property service management. The method includes: classifying and mapping historical work order data to construct a work order type-execution resource mapping, a resource warehouse distribution network, and a worker location distribution network; receiving real-time work order requests from the property center and parsing to obtain the target work order type and work order request location, matching the target execution resource set with the corresponding worker distribution network; and dynamically matching workers with resource warehouses based on the current worker scheduling status, work order location, and resource warehouse network to generate a scheduling plan. This invention solves the technical problems of delayed response times, inaccurate resource matching, and difficulty in coordinating and optimizing multi-dimensional scheduling factors in existing technologies, achieving accurate and intelligent matching and scheduling of property work orders, improving resource utilization efficiency, shortening service response time, and enhancing property service quality and user satisfaction.

The application provides a runtime task scheduling method, system, medium and product based on a hardware abstraction layer. The software scheduler replaces the traditional hardware scheduling unit, simplifies the system architecture, saves the hardware firmware development, and lightens the design of the driver, runtime, etc., thereby reducing the development and maintenance cost. The task is no longer issued in the form of a data packet as in the industry, but directly transmits the parameters required by the task to execute on the hardware unit, thereby shortening the task preparation and verification process. The hardware is directly operated based on the hardware abstraction layer, thereby reducing the performance overhead caused by frequent system calls, shortening the task issuance delay, and reducing the power consumption. Based on the information transmission carrier of the task context and the strict in-order scheduling characteristics of the flow and the synchronization between flows, the task is executed according to the time sequence and the dependency relationship, the differentiated scheduling requirements of different AI operation tasks are adapted, and the adaptation capability of the system to the complex application scenarios is enhanced.

This application discloses a microservice-based automated logistics order scheduling method to address the problems of ineffective system resource occupation and low order throughput caused by synchronous blocking processing. The method includes: receiving logistics order requests, validating the logistics order requests, and storing the valid logistics orders as pending orders in a database; responding to a scheduling task, scanning the database to retrieve pending orders that meet the preset order status conditions; sending the pending orders to a first message queue; retrieving the pending orders from the first message queue, calling the corresponding logistics provider system interface based on the information of the pending orders to process the orders, and obtaining the processing results; updating the status information of the corresponding pending orders in the database; generating an order status update message, and sending the order status update message to a second message queue for downstream services to perform subsequent processing based on the order status update message.

This invention discloses a supply chain order scheduling method using big data processing, belonging to the field of supply chain order scheduling technology. The method includes matching the inventory distribution of integrated orders from various sources, followed by field-level, logical-level, and association-level validation. Based on the obtained field validation results, logical validation results, and association validation results, abnormal orders are reassigned to obtain dynamic scheduling condition one. Historical vehicle routes in historical data are segmented into straight-arc and curved segments to obtain straight-arc and curved sub-segments. Based on the curvature and curvature distance of the straight-arc sub-segments, the weights of the straight-arc and curved segments are calculated, and the driver's route complexity is determined. Finally, dynamic scheduling is performed based on vehicle status, driver status, and dynamic scheduling condition one. This invention can improve the efficiency of supply chain order scheduling.

This invention discloses an intelligent order scheduling method, belonging to the field of order scheduling management technology. The method includes acquiring and analyzing order information to be scheduled, obtaining the demand quantity, time gradient data, and gradient purchase quantity of the goods to be purchased; acquiring the production efficiency, current inventory, and delivery time of each supplier, and determining the satisfaction level of each supplier with the goods to be purchased based on the time gradient data, thus obtaining a first supplier to be allocated and a second supplier to be allocated; for the first supplier to be allocated, matching and calculating the demand quantity of the goods to be purchased with a supplier quantity-price comparison table, and selecting the first procurement plan with the lowest purchase price; for the second supplier to be allocated, performing combination judgment and price analysis on the suppliers to obtain the second procurement plan with the lowest purchase price; and comparing the first and second procurement plans to obtain the optimal allocation plan. This invention has the effect of reducing procurement costs.