28 results about "Inventory level" patented technology

This application relates to the field of database operation and maintenance technology, specifically to an automated operation and maintenance method for audit system databases. This method includes: periodically collecting inventory levels and thresholds at each level of an LSM-Tree, and calculating the level capacity occupancy ratio; using eBPF technology to statistically analyze background I / O latency and traffic within a sliding window, and calculating the unit latency for background disk writes; performing recursive calculations in reverse order using the level capacity occupancy ratio to obtain the end-to-end logical write multiple; analyzing the coupling relationship between the level capacity occupancy ratios of adjacent levels, determining the multi-level concurrent congestion coefficient, obtaining the estimated unit data occupancy time, and inversely deriving the allowed front-end write rate; and combining this with a token bucket algorithm to smoothly limit the write requests of the front-end audit system. The goal of this application is to achieve early detection of backlog trends within the LSM-Tree and to automatically perform predictive traffic control to optimize the operation and maintenance management of the audit system database.

This specification provides an embodiment of a method for outbound goods processing. The method involves acquiring outbound orders to be processed in a target warehouse; determining the inventory information of each item in the outbound order; configuring picking tasks based on the inventory information, whereby the picking tasks are configured based on unit carrier capacity; sending the picking tasks to picking objects to enable them to pick items and obtain containers ready for outbound processing; then performing a verification task via an RFID channel machine; and finally, responding to the execution of the verification task, outbound operations are performed on the containers that have passed the verification task. This achieves a batch outbound process based on an RFID channel machine. Because it uses unit carrier capacity to batch pick items with different inventory levels, and employs a piecing operation to collect scattered items, it reduces the picking process path. Furthermore, the RFID channel machine performs batch verification of the collected items, ensuring the accuracy of the picked outbound goods.

We provide server equipment, inventory management equipment, and programs to improve the maintenance of appropriate inventory levels of pharmaceuticals in medical institutions. [Solution] In the inventory management system 10, the server device 12 is capable of communicating with the client device 11. The communication interface 121 receives inventory lot information ST from the client device 11, which specifies the quantity and date of receipt of pharmaceuticals recorded in the medical institution's inventory. The storage 123 stores the inventory lot information ST. When the communication interface 121 receives outbound information DL from the client device 11, which specifies the outbound quantity of pharmaceuticals to be excluded from inventory, and inventory lot information ST specifying the expiration date of the pharmaceuticals is stored in the storage 123, the processor 122 subtracts the outbound quantity from the quantity specified by the inventory lot information ST that specifies the earlier expiration date first.

This application provides a method, system, equipment, medium, and product for optimizing crude oil supply chain scheduling, relating to the field of petroleum industry chain optimization technology. The method involves: determining a target operations operator based on current state information; determining the target inventory level and target inventory penalty function for each node in the crude oil supply chain based on current inventory levels and the target operations operator; determining the objective function of a mathematical programming model using a preset oil transportation cost function and the target inventory penalty function; determining a production scheduling plan for the day based on current inventory levels and the objective function; executing the production scheduling plan for the day to obtain daily cost information; determining a reward function for the day based on the daily cost information; obtaining the daily reward function for each day within a preset time period; accumulating the daily reward functions within the preset time period to obtain the accumulated reward function; if the accumulated reward function value is not the maximum value, using a backpropagation algorithm to adjust the parameters to obtain an adjusted reinforcement learning layer.

Examples relate to managing node inventory levels are disclosed. An example may involve: obtaining historical data of items at a node in a network for a past time period; generating a supply chain lead time variability for an item at the node based on the historical data; generating a demand variability for the item at the node based on the historical data and the supply chain lead time variability; generating, based on the supply chain lead time variability and the demand variability, a recommended inventory level of the item in the node for a future time period; and transmitting the recommended inventory level to a computing device associated with the node for inventory arrangement in the future time period.

This invention discloses a method and system for optimizing medical consignment inventory based on kit integrity constraints. The method includes: constructing a material dependency graph for surgical kits, representing each surgical kit as a graph structure containing nodes and edges; constructing a shortage risk propagation model based on historical borrowing and returning data, calculating the risk transmission coefficient of material shortages on kit availability, and forming a kit integrity probability assessment system; establishing an optimization model aimed at minimizing the probability of kit shortages under total safety stock budget constraints, dynamically allocating the safety stock level of each material within the kit; integrating surgical scheduling data with the inventory calculation model, and implementing differentiated inventory reservation and sharing strategies based on surgical certainty levels to achieve synergistic optimization of kit integrity and inventory costs. This effectively solves the problem that traditional single-material inventory calculation methods cannot guarantee kit integrity, significantly improving material availability and reducing redundant inventory costs.

This invention relates to the field of logistics supply chain and discloses a digital and intelligent collaborative management system and method for logistics supply chain. The system includes collecting order submission time, delivery area information, warehouse inventory levels, vehicle location, and remaining vehicle loading space to form order demand change data; identifying the changing trends of order demand across different delivery areas based on the order demand change data, and constructing an order demand diffusion relationship structure; jointly calculating the inventory consumption of each delivery area based on the order demand diffusion relationship structure, and selecting a set of supply warehouses based on warehouse inventory levels; generating transportation task combinations for the supply warehouse set, combined with the current vehicle location and remaining vehicle loading space; monitoring changes in order demand along the demand propagation path based on the transportation task combinations, recalculating unexecuted transportation task combinations to form updated transportation task combinations. This invention has the advantages of improving operational efficiency and responsiveness.

A trained model of an online system is used to generate action recommendations by predicting future demands. The online system gathers in-store data by receiving, from a device of a picker and / or a computing system of an in-store physical receptacle, data with information about an inventory of an item. The online system estimates, based on conversion data for the item, a level of inventory for the item. The trained model is then applied to predict, based on the in-store data and the estimated level of inventory, a demand prediction score indicative of a future demand for the item. The online system generates, based on the estimated level of inventory and the demand prediction score, a depletion metric indicative of a time period until the inventory of the item is depleted. Based on the depletion metric, the online system triggers an action in relation to the inventory of the item.

This invention discloses a smart warehouse inventory management method based on big data analysis, comprising the following steps: acquiring and cleaning multi-source warehouse data to form a standardized set of inventory event fragments; identifying valid demand and updating the quiescent state, generating a demand dormancy age sequence and an activity trigger propagation graph; estimating the wake-up risk based on the age sequence and propagation graph, generating a wake-up risk spectrum result; determining the released inventory objects and decoding demand impulses to generate demand impulse release packets; combining the release packets with inventory events to execute inventory envelope adjudication, generating inventory level results and inventory action results; updating the propagation graph and age sequence based on the level results, action results, and execution feedback to form a closed loop. This invention employs big data analysis and inventory event evolution modeling methods to achieve dynamic adjudication and closed-loop updating of smart warehouse inventory, possessing the advantages of accurate demand identification, fast decision response, and strong inventory risk control.

At least one processor may receive a plurality of review comments, identify at least a subset of the plurality of review comments representing at least one positive review, and identify at least one product indicated by the at least one positive review. The at least one processor may determine, from data produced by an inventory system, that the at least one product has a low inventory level. In response to the determining, the at least one processor may generate a message configured to automatically order the at least one product from a supplier system and send the message to the supplier system, thereby automatically ordering the at least one product.

This invention provides an information processing system, information processing method, and program that can contribute to reducing reliance on individual expertise in ordering operations at each store. [Solution] In the acquisition step, the information processing device acquires sales performance indicators for each store and each product. In the selection step, for each store and each product, based on the sales performance indicators for that product, it determines whether or not to select a demand forecasting algorithm from among multiple algorithms as the algorithm to be used for ordering that product, thereby selecting a recommended algorithm. In the ordering step, it executes the ordering process using the recommended algorithm. The multiple algorithms include a demand forecasting algorithm that determines the order quantity based on demand forecasts derived from past sales performance, and a conditional algorithm that determines the order quantity based on whether past sales or inventory levels meet certain conditions.

The application discloses a kind of food material inventory dynamic management method and system, comprising: through group meal food material distribution system, obtain order period and the basic correlation data set of multiple food material inventory life cycle;Order period influence feature set is constructed, and the life cycle influence weight data of each food material category is determined;Using probability distribution fitting method, obtain multiple food material consumption probability data set;Identify distribution mismatched inventory unit, determine the optimized inventory space distribution scheme;Order type identification model is constructed, and order type is identified, and generate inventory level demand interval;Multiple food material inventory adjustment instruction is generated, and the inventory of each food material category is adjusted;The consumption deviation value of different food materials is calculated, and multiple food material life cycle influence weight data is updated.The application can improve the matching degree between inventory configuration and actual group meal demand under the premise of guaranteeing food material life cycle constraint, reduce inventory backlog and shortage risk.

This invention belongs to the technical field of resource planning and management, and relates to an intelligent inventory preparation decision-making and execution system for multi-store cross-border e-commerce. The system includes: an event contract generation module that parses operation instructions to calculate absolute inventory levels and generates deterministic event contracts; a steady-state demand regeneration module that, based on the contract, extracts historical pulse sales and inverts distortion points to generate a regenerated steady-state demand signal; a baseline demand forecasting module that extrapolates this signal and combines it with a buffer coefficient to generate a probabilistic steady-state baseline inventory demand; a virtual inventory pool construction module that deducts the net available inventory from the demand to generate a global virtual available inventory pool; an inventory replenishment pipeline planning module that combines gap dates, contract delivery nodes, and logistics parameters to generate replenishment pipeline parameters; and a feasibility verification and execution module that compares output instructions or over-limit feedback signals. This invention solves the problem of inventory preparation decision-making bias caused by the ineffective deconstruction and separation of complex signals and the lack of closed-loop verification.

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

The application discloses a construction method of a supply chain optimization model, a supply chain optimization method and device, and relates to the technical field of computers. A specific embodiment of the construction method of the supply chain optimization model comprises the following steps: based on historical warehouse network information, historical purchasing information and historical allocation information, the single replenishment quantity, the single allocation quantity and the average inventory level of each warehouse are determined by using a single-period approximation algorithm; then, a path-level cost parameter is constructed accordingly; based on the path-level cost parameter, a supply chain optimization model taking path selection as a decision variable is constructed; and the supply chain optimization model is uniformly established by simultaneously considering multiple links such as a warehouse network, purchasing and allocation, thereby improving the overall optimization effect. A specific embodiment of the supply chain optimization method comprises the following steps: the to-be-optimized supply chain information is input into the supply chain optimization model, and multiple-link suggestion information corresponding to the to-be-optimized supply chain information is obtained; and comprehensive decision suggestions covering a warehouse network, purchasing, allocation and the like are obtained at one time, thereby improving the business execution efficiency.

The system according to this embodiment aims to streamline inventory management by proposing an optimal inventory level based on past sales data and demand forecast data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects past sales data and demand forecast data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an optimal inventory level based on the data analyzed by the analysis unit. The adjustment unit adjusts the inventory based on the inventory level proposed by the proposal unit.

This invention provides an inventory management method and system based on a robust optimization model, relating to the field of data management technology. The method includes: identifying potential risk transmission paths in a heterogeneous supply chain network diagram based on a two-layer message passing mechanism, and outputting risk-aware demand; inferring virtual extreme scenario samples based on a neural symbolic model to analyze risk transmission paths; using the influencing factors of demand and supply data as graph nodes, and combining the virtual extreme scenario samples with the shelf life and historical loss data of perishable goods for in-depth joint prediction of supply and demand forecasts; calculating the supply and demand matching degree based on the supply and demand forecast results and risk-aware demand, and dynamically optimizing the safety stock level and order lead time through a robust optimization algorithm to generate an inventory operation strategy; inputting the inventory operation strategy into a lightweight inventory optimization model for real-time inference to generate an executable inventory management solution. This invention solves the problem of failing to meet the requirements for refined, dynamic, and highly robust inventory management in heterogeneous supply chains.

A self-learning cluster control system for supply chain computing infrastructures, comprising a supply chain data acquisition unit, a node profiling unit, a cluster formation unit, a condition assessment unit, a self-learning control unit, and a resource allocation unit, wherein the system is configured to dynamically group supply chain nodes into clusters based on continuously acquired condition data and to enable adjustment of cluster allocation based on demand trends, inventory levels, transportation capacities, delivery times, and disruption risks.

This system provides a technical solution for managing product sales, inventory, and deliveries through QR codes and predictive analytics. It incorporates algorithms that generate and assign unique QR codes to products based on real-time sales data from farmers. These QR codes provide direct links to product information, facilitating efficient inventory management and customer engagement. The system also leverages predictive analytics to forecast product demand by analyzing historical sales data, adjust inventory levels, and optimize delivery driver assignments based on proximity to farms. The hardware supporting the system includes servers, processors, network components, and scanning devices to ensure reliable operation and data synchronization. By automating QR code generation, integrating sales data, and dynamically assigning drivers, the system improves operational efficiency, minimizes errors, and enhances the customer experience.

This application provides a method, apparatus, electronic device, and readable storage medium for dynamic collaborative production line scheduling. The method includes: determining the material availability constraint time for a production order based on its material requirements, real-time inventory levels, and in-transit procurement information; determining the available production line time window for each production process based on its occupancy status; generating a production plan by performing a flow-line time-connection scheduling for each production process based on the material availability constraint time, available production line time window, and employee capacity information for each production process; collecting daily data on the actual output of each production process and employee attendance for the following day during production according to the production plan; and dynamically adjusting subsequent production plans based on the deviation between the actual output of each process and the daily planned output, employee attendance information for the following day, and employee capacity information. This method improves the accuracy of the production plan itself.

This paper relates to the field of refined oil resource scheduling technology, and provides a method, apparatus, equipment, and storage medium for refined oil resource scheduling. The method includes: using a simulator to simulate the scheduling scheme for the first sub-cycle of the current cycle based on the node parameters of each node, to obtain the inventory level and sub-cycle reward value of each node at the end of the first sub-cycle; using a deep reinforcement learning module to predict the target inventory level of each node at the end of the first sub-cycle of the next cycle based on the inventory level, sub-cycle reward value, and scheme penalty value of the current cycle, with the second sub-cycle of the current cycle as the first sub-cycle of the next cycle; using a mathematical programming module to determine the scheduling scheme for the next cycle based on the target inventory level and the operational constraints of each node, with the goal of minimizing scheduling costs and inventory level differences; and performing refined oil resource scheduling on each node according to the scheduling scheme of the first sub-cycle of the next cycle. The embodiments described in this paper can improve the efficiency of refined oil resource scheduling.

This disclosure describes a computer-implemented method for efficiently managing warehouse operations by dynamically controlling tasks across multiple facilities. Upon receiving data related to a customer order, the system processes task sequences based on order-specific criteria such as delivery deadlines, product requirements, and inventory availability. A data processing module extracts order parameters and, through an orchestration module, adjusts the task sequence in real-time, addressing inventory levels, resource constraints, and operational conditions. The method leverages predictive analytics, historical data, and integration with external systems to optimize task assignment, ensuring efficient fulfillment. Additionally, the system can modify tasks based on customer preferences and environmental impact metrics, reallocating resources as needed to maintain operational continuity. This adaptable approach enhances warehouse flexibility, enabling tasks to be customized for unique orders, cross-docking processes, and specific customer requirements while minimizing delays and optimizing resource use.

The invention provides an intelligent acquisition and management method, a medium and a system for data in the whole production process, and belongs to the technical field of industrial products.Material batch tracing data are acquired by configuring a material identification system, edge data acquisition nodes are deployed, and equipment states and process parameters are acquired through an industrial protocol; performing multi-source data fusion to align the edge equipment data, the material code scanning data and the work order data according to timestamps, and aggregating the data into process-level data records by adopting a sliding time window; inputting a production state evaluation model, outputting a material distribution emergency degree index, a process bottleneck early warning value and an equipment abnormity probability by using a bidirectional long-short-term memory network and an artificial intelligence algorithm based on separable attention, and executing dynamic scheduling according to the emergency degree index and a line edge stock water level to generate a distribution task. The technical problem of material distribution scheduling lag caused by difficulty in real-time fusion of multi-source heterogeneous data in the production process is solved.

This information processing device: acquires a priority level set for a transportation route connecting a goods supply location and a location where a warehouse storing goods is provided, said priority level indicating the degree to which the transportation volume along the transportation route is to be stabilized; acquires the demand volume for the goods for each predetermined period; acquires route information indicating the transportation route; acquires cost information indicating costs regarding the warehouse; determines the allowable change in transportation volume along the transportation route according to the priority level; optimizes the transportation volume of goods along the transportation route and the inventory volume of goods in the warehouse on the basis of the demand volume, the route information, the cost information, and the allowable change in volume; and outputs the optimized transportation volume and inventory volume.

This application relates to the field of digital operation and artificial intelligence application technology in the plastics and chemical trade, and provides a method for intelligent demand forecasting and dynamic pricing optimization in the plastics and chemical trade. The method includes: collecting multi-source heterogeneous data from the entire plastics and chemical trade chain to construct an integrated demand forecasting model; inputting the collected current characteristic data into the trained demand forecasting model to output demand forecast results for each plastics and chemical product within a preset future time period; constructing a dynamic pricing optimization model with the demand forecast results, real-time inventory levels, customer characteristic data, historical quotation success rates, and publicly available competitor quotation data as inputs, with the goal of maximizing sales profit; and iteratively calculating through the dynamic pricing optimization model to output dynamic pricing results for each plastics and chemical product based on different customer types and purchase quantities. This method improves the accuracy of demand forecasting for key plastics and chemical products, providing a scientific basis for enterprise procurement planning and inventory management.

This invention provides a tool inventory management system and method. A database module collects and encodes information on various tools, classifying them into consumable tools and special-purpose tools, thus constructing a tool database covering the entire business chain. The inventory management module, based on the tool database, performs fixed-quota rolling adjustments to the inventory of consumable tools, monitors the inventory of special-purpose tools throughout their entire lifecycle, and determines the dynamic maximum and minimum inventory levels for each type of tool based on production needs, enabling continuous production while adopting a minimum inventory strategy. The procurement management module, also based on the tool database, monitors the current, maximum, and minimum inventory levels of each type of tool. The cost management module, based on the tool database, dynamically monitors the costs of each type of tool based on preset cost analysis indicators. This invention, through the coordinated management of multiple modules, ensures production continuity while controlling costs using a minimum inventory strategy, thereby improving enterprise operating profits.

This invention belongs to the field of industrial production management technology, specifically relating to a dynamic scheduling method and system for concrete production tasks that considers solid waste utilization. The method includes the following steps: Real-time data collection is achieved through an industrial Internet of Things (IoT) system deployed at the production site, including raw material moisture content and output rate at the pre-processing production line, real-time inventory levels at the intermediate inventory level, and pending production order data at the order demand level. Based on the collected historical moisture content and output rate sequences, a gradient boosting decision tree algorithm is used to predict the raw material moisture content deviation and predicted output rate within a future scheduling cycle, serving as scheduling boundary constraints. This invention, through a rheological matching mechanism and an urgency-linked strategy, significantly improves solid waste utilization and production efficiency while ensuring that the rheological properties of concrete meet standards.

This invention relates to the field of intelligent manufacturing and industrial automation technology, specifically to a manufacturing intelligent scheduling and dispatching method based on AI edge computing terminals. The method includes: generating a theoretical scheduling baseline timeline based on work order BOMs and standard process routes (SOPs) issued by ERP / MES, as well as real-time data collected by the edge computing terminal on equipment OEE time-series slices, workstation cycle times, and edge buffer pool inventory levels; performing parametric disturbance simulation on this data to generate a theoretical residual time-series set; generating a real residual time-series based on actual workstation completion times; determining the disturbance type using dynamic time warping distance calculation and similarity calculation based on process route topology; and generating local work order translation scheduling instructions accordingly, followed by dynamic calibration to optimize the scheduling accuracy of subsequent production plans.