13 results about "Inventory control" patented technology

This invention discloses a collaborative management method and system for the entire warehousing and logistics process, belonging to the field of warehousing and logistics information technology. Addressing the technical shortcomings of existing warehousing and logistics management, such as low inventory accuracy, poor scheduling efficiency, lagging information collaboration, and insufficient cost control, this invention integrates the entire process of inbound, storage, picking, outbound, transportation, and traceability, constructing a closed-loop management system of "data perception - algorithm decision-making - process collaboration - dynamic optimization." Through multi-dimensional data collection, optimized algorithm scheduling, cross-system data collaboration, and full-process traceability technology, this invention achieves precise inventory control, optimized logistics route planning, and efficient resource allocation, significantly improving warehousing and logistics operational efficiency, reducing overall costs, and enhancing management transparency. It is applicable to the digital management needs of warehousing and logistics in various industries, including manufacturing, retail, and e-commerce.

The application provides an intelligent inventory control management method and system based on a supply chain cooperation mechanism, applied to the technical field of supply chain management, preliminary demand prediction of a downstream partner is received, demand intention information containing customization component possibility weight and potential demand range is generated according to the prediction, after the upstream supplier receives the demand intention information, the influence of the demand intention information on future production resources is further evaluated, an evaluation result is obtained, finally, according to the evaluation result, the method performs flexible preparation in advance, including production window soft reservation, upstream material pre-communication and special mold preparation, effectively coping with the supply uncertainty of the customized components, avoiding the dilemma of standard component overstock and customized component shortage, realizing lean production and on-time delivery, and bringing significant economic benefits and competitive advantages to enterprises.

The application discloses a kind of inventory optimization methods of charging facility spare parts replacement cycle and failure rate analysis, and the core process of this method includes building data layer, multidimensional data is cleaned and optimized, to realize standardized storage;Based on the LSTM algorithm, a multidimensional linkage prediction model is built, the loss effect of design defects and environmental factors is quantified to optimize the model, and the accurate spare parts replacement cycle threshold and equipment failure rate are output;According to the flow properties of spare parts, a differentiated inventory management mode is designed, and the optimal management mode of slow-moving parts is selected;A full-cost quantification model is built to calculate the total cost and service level under each mode;Finally, with the goal of minimizing cost, an intelligent inventory control model is built, which dynamically outputs inventory warning thresholds, replenishment quantities and times;Through the algorithmization and parameter quantification of the whole process, the application realizes fine and intelligent management of inventory, significantly reduces operating costs and stockout risk, and improves the stability of charging facilities.

This invention discloses an automated inventory control method, system, and automated storage and retrieval system (AS / RS) for instant retail, relating to the field of warehouse automation control technology. The method includes: S1, acquiring the layout parameters of the AS / RS, constructing a topology map of the walking network based on the layout parameters, the topology map containing nodes and edges, nodes corresponding to work points, installing sensors on the stacker crane, and collecting motion state data of the stacker crane during task execution through the sensors; S2, associating and storing the motion state data with the path information of the stacker crane during task execution to form a motion state record; extracting the motion state data feature values ​​of each node interval from the historical motion state record, assigning weight adjustment values ​​to the corresponding edges based on the feature values, superimposing the weight adjustment values ​​onto the original weights to obtain updated weights, and recalculating the path based on the updated weights, so that the path planning result can reflect the actual traffic quality of the path segment.

The present application relates to the field of computer, particularly to a kind of injection molding machine storage control method, device and system, wherein injection molding machine storage control method can quickly pre-adjust screw before plasticizing, so that actual back pressure is below set back pressure, prevent the emergence of nozzle drooling and other problems caused by back pressure being too high in plasticizing stage;Further, through the coordinated adjustment of injection motor and plasticizing motor in plasticizing stage, the change of actual back pressure is more close to the set value, avoid the phenomenon of screw sudden stop, and the plasticizing motor starts to slow down when the screw reaches a certain position, instead of starting to slow down and stop running when reaching the end position of storage, so that the screw retraction speed is smaller, the end position of storage is smaller, and the repeatability of the end position of storage is guaranteed, so as to ensure the repeatability of production products.

The application discloses a kind of supercritical foaming material classification inventory management method for electrical protection, belong to material intelligent warehousing technical field, specifically include: first, obtain the antistatic index of the supercritical foaming material to be handled, flame-retardant grade, compressive yield strength and thermal decomposition temperature four basic attribute parameters;Then the parameters are constructed as multidimensional feature tensor, mapped to the preset attribute space coordinate system, according to the coordinate distribution to generate target warehouse classification label, the tensor, label and material storage coordinates are associated and stored in the inventory database;Subsequently, receive target electrical component protection task instruction, extract the demand attribute threshold of antistatic, flame-retardant, compression and thermal stability, generate dimension-matched demand feature tensor and place in the coordinate system;Then calculate the space euclidean distance and the included angle cosine similarity of both;Finally, according to double-threshold comparison, the target multidimensional feature tensor is screened out, the corresponding discharge matching scheduling instruction is generated and sent to the inventory control terminal.

AI-Based Computer using ERP software for Inventory Control, Management, and Financial Assessment

This invention provides a method and system for monitoring the inventory of hydrogen peroxide test solution with active isolation and decoupling, relating to the field of medical device measurement and monitoring technology. It aims to improve the problem of continuous inventory closed-loop control deviation caused by the inability to accurately reconstruct the actual pumped volume from the weighing signal due to the overlap of chemical decomposition weight loss and pumping weight loss in the test solution. By periodically switching between static calibration and dynamic pumping states, a pure chemical decomposition weight loss reference rate is obtained under static conditions through active isolation, avoiding overlap with pumping weight loss; under dynamic conditions, the total weight loss is obtained, and the real-time decomposition weight loss is obtained after environmental compensation, with the difference reconstructing the actual pumping mass flow rate. Closed-loop control automatically compensates for attenuation, eliminating the need for a flow meter, accurately separating the pumped volume from the overlapped signal, solving the long-term inventory control inaccuracy problem, and significantly improving replenishment accuracy and system stability.

An inventory control system comprises an object storage device, a display device, and one or more processors. The storage device includes several compartments, each with multiple locations for storing objects. The display device shows information about the storage device. The processors create a descriptive database of the objects stored in the system. The processors retrieve keywords that identify the objects stored in the locations of one of the compartments and generate a text block based on those keywords. On the display device, the processors show a representation of the storage device's compartments, with the text block applied to the selected compartment.

This invention relates to the field of management system technology, specifically to a data-driven electronic equipment rental inventory management system. The system includes: an initial equipment inspection unit, which verifies information based on user-submitted return information, the weight of the recycled equipment, and images; after verification, taking photos and filtering out abnormal images with discrepancies; an anomaly assessment unit, which divides the electronic equipment into multiple appearance areas, compares baseline images with abnormal images, analyzes and calculates the anomaly assessment value for each appearance area of ​​the recycled equipment, and substitutes this value into a maintenance assessment model to calculate the maintenance assessment value; and an inventory management unit, which periodically performs equipment recycling and allocation, calculates the reuse assessment value for each electronic device based on the equipment recycling cycle and the maintenance assessment value of each electronic device; and analyzes and calculates supply assessment parameters and performs inventory control management by combining rental data, the usage status of each electronic device in the inventory, and the reuse assessment value.

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.

The application discloses a supercritical foaming material classification inventory management method for electric appliance protection, and belongs to the technical field of material intelligent storage, and specifically comprises the following steps: first, obtaining four basic attribute parameters of the supercritical foaming material to be processed, namely, the antistatic index, the flame-retardant grade, the compressive yield strength and the thermal decomposition temperature; then, constructing the parameters into a multi-dimensional feature tensor, mapping the multi-dimensional feature tensor to a preset attribute space coordinate system, generating a target warehouse entry classification label according to the coordinate distribution, and storing the tensor, the label and the material storage coordinates into an inventory database in association; subsequently, receiving a target electric appliance component protection task instruction, extracting the demand attribute threshold values of antistatic, flame-retardant, compression and thermal stability, generating a demand feature tensor matched in dimension and placing the demand feature tensor in the coordinate system; then, calculating the spatial Euclidean distance and the included angle cosine similarity of the two; finally, screening out the target multi-dimensional feature tensor according to double-threshold comparison, generating a corresponding warehouse exit matching scheduling instruction and sending the warehouse exit matching scheduling instruction to an inventory control terminal.

This invention relates to the field of fresh food supply chain management technology, and more particularly to a digital management system for the entire fresh food supply chain process. The system includes: a data acquisition module for real-time acquisition of status data of fresh products throughout the entire supply chain process; a model building module for iterative training of a fusion prediction network framework to output a fresh product demand prediction model; an inventory control scheme generation module for dynamically adjusting inventory turnover parameters of fresh products to obtain an inventory control scheme; a route optimization scheme generation module for collaborative analysis of transportation route planning parameters to obtain a transportation route optimization scheme; and an anomaly monitoring module for monitoring and analyzing the flow status of fresh products, generating anomaly handling information when the monitoring results meet preset anomaly conditions. This invention enables real-time dynamic monitoring and collaborative optimization of the entire fresh food supply chain process, improving the overall operational efficiency of the supply chain.