66 results about "Inventory optimization" patented technology

The invention relates to an AI-based supply chain demand prediction and inventory optimization method and system, and the method comprises the following steps: calling a sales record from a supply chain, recognizing a periodic demand rule of a corresponding commodity, and predicting an estimated demand quantity in a future time period according to the periodic demand rule; carrying out supply and demand difference analysis by combining the current warehouse commodity stock margin, and judging the stock state; if the commodity stock surplus exists, making a corresponding stock digestion strategy; if a shortage quantification result appears, carrying out key degree evaluation and urgency sorting based on a shortage amount in combination with market factors, and generating a key commodity list and an out-of-stock urgency index; and finally, in combination with a preset commodity constraint condition, according to the key commodity list and the urgency index, an optimal replenishment scheme meeting resource and time limit is formulated, and the technical problem of how to realize conversion from passive response to active pre-judgment in a supply chain environment with large demand fluctuation and complex influence factors is solved.

The invention discloses a big data measurement asset supply and demand matching and inventory optimization method, and relates to the technical field of big data processing and supply chain management. The method comprises the following steps: segmenting supply chain asset data through a hash function according to multi-dimensional attributes to generate a data fragment set, and constructing a global index tree according to the data fragment set; and monitoring the load state of the leaf nodes of the global index tree in real time, and migrating and verifying data during unbalance. And querying the global index tree, positioning fragment data associated with the query request from the uniformly distributed leaf nodes, generating a preliminary matching asset set, calculating the matching degree of each asset and the query request, and generating a resource allocation result. And updating the global index tree, and generating the latest data view representation. And adjusting the attribute weight coefficient of each dimension in the hash function, and generating optimized storage layout configuration. Irrelevant data are filtered through a neighbor algorithm, and a final matching result set is generated. Balanced storage, efficient query and accurate matching of asset data are realized, and the overall response speed and the resource utilization rate are improved.

The invention provides a method and system for constructing a multi-source time series data fusion prediction model in BI analysis, and relates to the technical field of enterprise-level business intelligent analysis, and the method comprises the steps: obtaining multi-source original time series data, and carrying out the standardization preprocessing; obtaining a multi-dimensional feature set through time sequence feature extraction and business feature extraction; dynamically fusing the time sequence features and the service features by adopting an attention mechanism, and constructing an LSTM and XGBoost combined prediction model for training; performing parameter optimization based on the independent verification set to obtain a final prediction model; the system comprises a data acquisition and preprocessing unit, a multi-dimensional feature extraction unit, a fusion prediction model construction unit and a model verification and optimization unit. According to the method, the problems that multi-source time series data fusion is insufficient, the model is difficult to capture complex spatial-temporal characteristics and the dynamic adaptive ability is lacked are solved, the accuracy and the real-time performance of enterprise-level BI analysis such as sales trend prediction and inventory optimization are improved, and the prediction precision is improved.

A system and method for closed-loop tracking of advertising events through offer redemption with artificial intelligence feedback. A session identifier links device, pass, content, and contextual data throughout a content playback session. Advertisements dynamically inserted into the content stream inherit the session identifier. An offer platform generates offers linked to the session identifier and pass identifier, enabling tracking through distinct lifecycle states from push to redemption. A database captures timestamped and geo-referenced data for each event. An artificial intelligence engine uses redemption and avail events as verified ground-truth outcomes for training machine learning models, enabling supervised learning that establishes causal relationships between ad exposure and purchasing activity. An inventory gateway receives merchant product inventory data. An optimization engine employs demand forecasting and reinforcement learning to generate inventory-driven triggers and offer recommendations. A merchant interface presents analytics for inventory optimization.

The invention provides a demand degree and inventory optimization-based medicine data dynamic management method and system, and relates to the technical field of medicine data management. The method comprises the following steps: firstly, acquiring consumption time series data, inventory time series data and environmental factor time series data of a target medicine set, and fusing the data to generate medicine dynamic collaboration data; secondly, calculating a corresponding real-time demand degree in a target medicine set based on the medicine dynamic collaboration data; according to the real-time demand degree, dynamically generating a dynamic safety inventory benchmark; the real-time demand degree is coupled with the real-time safety inventory benchmark of the corresponding target medicine, and a dynamic inventory control line is generated; and finally, updating and managing the inventory data of the target medicine set according to the dynamic inventory management and control line. According to the technical scheme provided by the invention, closed-loop intelligent inventory management from environment perception to automatic replenishment execution is realized.

The invention discloses a WMS inventory optimization method and system based on digital twinning, and relates to the technical field of warehouse logistics management, and the method comprises the steps: collecting cargo inventory state data, refrigerated transport resource data and seasonal operation progress data from each cold chain warehouse management system, converting the data into different data formats through employing a standardized protocol, and carrying out the optimization of the data formats; the method comprises the following steps: acquiring a real-time inventory data set in a unified format, synchronizing the real-time inventory data set to a central processing node by adopting a distributed database mechanism according to the acquired real-time inventory data set, determining current cargo inventory levels and available refrigerated transport capacities of all cold-chain warehouse nodes, and if the cargo inventory level of a certain cold-chain warehouse node is determined to be lower than a preset threshold value, sending the cold-chain warehouse node to the central processing node; if yes, comparing the cargo inventory level and the transportation capacity available evaluation of adjacent warehouse nodes through an inventory level threshold; according to the WMS inventory optimization method and system based on digital twinning, efficient circulation of cold chain products such as goods is guaranteed.

The invention relates to the technical field of data processing, and provides an equipment material full life cycle-oriented big data platform management system and method, and the system comprises a multi-source data collection module which is used for collecting material data of equipment materials in each stage of the full life cycle to form a material data set; the data fusion module is used for carrying out data fusion on different material data in the material data set based on different application scenes so as to form a fused data group corresponding to the application scenes; and the decision module is used for generating at least one decision of an inventory optimization decision and a fault alarm decision based on the fused data set. According to the invention, the problems of information lagging and data islanding in the material management method in the prior art are effectively solved, the inventory optimization decision and the fault alarm decision are generated through the fusion data set, and the function of effective decision making according to the corresponding fusion data set in different application scenarios is realized. The beneficial effects of optimizing inventory or finding faults and giving an alarm are achieved.

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.

The application relates to a supply room consumable intelligent inventory management method, device, equipment and medium. The method comprises the following steps: acquiring multi-source sensing data of each consumable in a supply room, performing multi-source information fusion processing according to the multi-source sensing data, obtaining an inventory state posterior distribution of each consumable, performing event correction processing on the inventory state posterior distribution, performing demand prediction processing according to the inventory state distribution after event correction and consumable historical consumption data, obtaining multi-step demand prediction results of each consumable in a target prediction period, performing inventory optimization calculation processing according to the multi-step demand prediction results and the inventory state distribution after event correction, obtaining replenishment order quantities of each consumable in the target prediction period, performing consumable scheduling and risk early warning processing according to the replenishment order quantities and batch validity period information of each consumable, and generating an expired consumable priority use strategy and inventory risk early warning information. The method can identify consumable inventory and generate replenishment orders.

This invention relates to a data-driven intelligent logistics supply chain digital management system. The system includes a demand forecasting and analysis unit, an inventory optimization unit, a supplier management unit, a logistics route optimization unit, a system integration and feedback unit, and a real-time logistics marketing unit. By analyzing historical orders and market data through recurrent neural networks and long short-term memory networks, accurate forecasting of short-term and medium-to-long-term demand is achieved. The inventory optimization unit combines a Bayesian dynamic linear model and a convolutional neural network to achieve dynamic inventory management. The supplier management unit optimizes supplier selection using a multilayer perceptron and fuzzy clustering algorithm. The logistics route optimization unit achieves optimal scheduling of logistics routes based on genetic algorithms and an adaptive large-scale neighborhood search algorithm (ALNS). By integrating multiple functional units, this invention effectively improves the operational efficiency and response speed of the logistics supply chain.

This invention relates to the field of inventory cost analysis, addressing the problems of lagging information updates and lack of predictive warnings in cost accounting, which prevent the automatic triggering of targeted inventory optimization strategies. Specifically, it is a rolling analysis method for spot inventory costs with full-process tracking, including a pre-warehouse process cost statistics module, an in-warehouse cost statistics module, a rolling analysis module, an inventory cost prediction statistics module, and an inventory optimization decision-making module. Based on complete inventory cost accounting, this invention endows cost management with real-time and forward-looking capabilities through rolling analysis and dynamic prediction. Ultimately, through intelligent linkage between cost and profit and a multi-level decision-making mechanism, it achieves an intelligent closed loop from data to action, transforming inventory cost management from traditional recording into a core decision-making tool for profit protection and risk prevention, effectively improving the enterprise's inventory operation efficiency and overall profitability.

The invention discloses a standard component inventory management method and device, and relates to the technical field of inventory prediction, and the method comprises the steps: carrying out the ordering number prediction of J standard component products according to a preset future time zone, and building prediction distribution; performing inventory fitness detection in combination with the real-time inventory data set, and outputting J detection results; formulating a first inventory optimization strategy according to the detection result; based on the first strategy, optimizing the inventory environment to obtain a second strategy; constructing a supply risk prediction topology model, optimizing the second strategy, and generating a third strategy; and performing inventory optimization management on the manufacturers according to the third strategy. The technical problems of inventory overstock and low resource utilization rate caused by inaccurate demand prediction and lack of supply risk assessment in an existing inventory management method are solved, and the technical effects of realizing accurate inventory regulation and control, reducing storage cost and improving supply toughness and response efficiency through dynamic demand prediction and supply risk coupling optimization are achieved.

This invention discloses an AI-based method for hotel warehouse scheduling and predictive optimization, relating to the field of management and scheduling technology. It includes collecting data on target food consumption, business event intensity, supply chain indicators, and IoT quality sensing data for hotel areas. The invention uses a spatiotemporal correlation dataset to identify the time offset between fluctuations, calculates a causal influence coefficient matrix after decontamination correction, generates a counterfactual consumption sample library based on consumption change trajectories under different scenarios, and outputs a heatmap of quality failure risk where the quality index exceeds a warning threshold in the future, achieving early visualization and warning of quality risks. The counterfactual consumption sample library and the quality failure risk heatmap are input into a predictive analysis network for probabilistic consumption and quality analysis, obtaining the future consumption probability distribution and quality evolution results of valuable ingredients. Dynamic quality decay is incorporated into inventory optimization, ultimately achieving optimal warehouse scheduling and costs.

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.

The invention discloses a cigarette material demand prediction and inventory optimization method based on a time sequence model. The method comprises the following steps: acquiring cigarette production multi-source data, and constructing a standardized data set in combination with derived feature data calculated based on the cigarette production multi-source data; inputting the standardized data set into a pre-trained time sequence model, and outputting probabilistic demand prediction results respectively corresponding to the plurality of quantiles; calculating a relative confidence interval width according to a probabilistic demand prediction result; generating a matched target inventory value based on a comparison result of the relative confidence interval width and a preset confidence threshold; according to the target inventory numerical value and the current inventory data in the cigarette production multi-source data, the replenishment amount of the cigarette materials is calculated, an inventory optimization instruction is generated to optimize the inventory, the balance between the inventory and the risk is achieved, the cigarette production continuity is guaranteed, the inventory overstock and the storage cost are reduced, and the cigarette production efficiency is improved. And the lean and intelligent level of tobacco material inventory management is improved.

The invention relates to a WEEE recycling supply chain inventory optimization method based on evolutionary reinforcement learning, and belongs to the technical field of supply chain management and artificial intelligence. According to the method, a multi-level inventory cooperative control model is constructed, an inventory optimization problem is modeled as a Markov decision process, and an evolution reinforcement learning algorithm is utilized to train an intelligent agent to learn an optimal strategy. Core innovations include design state space capture inventory dynamics, action space processing continuation to discrete decision mapping, and cost-based minimization of reward functions. According to the algorithm, a population evolution mechanism and experience playback optimization are fused, and the learning efficiency and stability are improved through parallel exploration and sample screening. According to the method, the uncertainty of the recovery amount and demand can be effectively dealt with, the inventory cost is reduced, the supply chain robustness and sustainability are enhanced, and a self-adaptive intelligent solution is provided for WEEE management.

This invention discloses an internet-based livestock farming information data management system, belonging to the field of livestock farming technology. The invention includes a constraint prediction module, a segmented feeding curve generation module, a feedback correction module, a dynamic resource reallocation module, and an iteration module. The constraint prediction module uses feed shelf life and target market time as dual constraints to solve for feasible solution intervals. The segmented feeding curve generation module constructs constraint corridors based on feasible solution intervals to generate adaptive segmented feeding curves. The feedback correction module forces upstream recalculation when accumulated corrections penetrate the interval boundary, forming a forced closed loop of execution and strategy. The dynamic resource reallocation module forces inventory optimization when there is no solution and synchronizes the results upstream. The iteration module forces full-link recalculation when new data is added. This invention solves the problem of mismatch between feed shelf life and growth cycle through dual constraint coupling and forced linkage between modules, achieving a dual improvement in feed utilization and farming efficiency.

The application discloses an artificial intelligence driven SPD inventory dynamic optimization and cost control method, which belongs to the technical field of inventory optimization and cost control. The normalized data is time series reconstructed to obtain medical consumable data sequences in time sequence, so that the medical consumable data sequences are demand predicted through a medical consumable prediction model to obtain accurate future medical consumable prediction results, which can effectively and accurately predict the use trend of medical consumables, thereby reducing inventory backlog and capital occupation. A group optimization algorithm is used to collaboratively solve an SPD cost double-layer control model to obtain an optimal inventory strategy and a corresponding cost control strategy. Based on the optimal inventory strategy and the cost control strategy, all suppliers are dynamically scored, and the selected suppliers share the optimal inventory strategy and the cost control strategy, so that the dynamic optimization and adjustment of the inventory and the replenishment strategy are realized, the total cost is optimized, and the application and promotion are facilitated.

The invention relates to the technical field of logistics supply chain management, and discloses a logistics supply chain dynamic management system and method based on big data, a big data acquisition module is used for acquiring data of each link of a logistics supply chain; the demand prediction module is used for predicting the preprocessed customer demand data by adopting an improved long-short term memory (LSTM) network, namely an LSTM demand prediction model; and the inventory optimization module uses an improved economic order quantity model, namely an EOQ model, to calculate an optimal order quantity and an optimal order period according to the demand prediction value, the inventory data and the supplier data, and the logistics supply chain dynamic management system and method based on the big data realize intelligent management of each link of the logistics supply chain. The dynamic monitoring module can find abnormal conditions in real time and feed back the abnormal conditions in time, the decision support module can generate scientific and reasonable decision suggestions, and the response speed and management efficiency of the logistics supply chain are improved.

This invention relates to the field of enterprise resource planning (ERP) systems, specifically disclosing an ERP material specification interpretation and selection assistance system based on a large language model. The system includes a specification information collection and preprocessing module, a specification semantic parsing module driven by a large language model, a multi-dimensional selection decision assistance module, and a design result compliance and inventory optimization verification module. By automatically parsing unstructured specification data, intelligently matching candidate materials, and comprehensively evaluating technical parameters, cost-effectiveness, and inventory availability, it achieves efficient and accurate material selection and supply chain resource optimization.

The application discloses a color size grouping and purchase template generation method based on Gaussian mixture clustering, and the method comprises the following steps: obtaining historical sales data; mapping all sizes into continuous size code values to form a one-dimensional size sample set; constructing a Gaussian mixture model containing multiple Gaussian distribution components; iteratively fitting the Gaussian mixture model through an expectation maximization algorithm to obtain a mixture weight, a mean value and a variance, and a posterior probability of each size sample belonging to each Gaussian distribution component; for each color identification, calculating the average posterior probability of the color identification belonging to each Gaussian distribution component, and distributing the color identification to the size structure group corresponding to the Gaussian distribution component with the maximum average posterior probability; based on all size samples in each group, counting the occurrence frequency of each size code value, calculating the proportion in the size structure group, and generating a size purchase template corresponding to the size structure group. The application realizes accurate purchase and inventory optimization.

The invention relates to a coal machine equipment spare part management system and method. The system comprises a data input layer, an algorithm model layer and an inventory optimization and execution layer. The data input layer collects multi-source heterogeneous data and inputs the multi-source heterogeneous data into the algorithm model layer; the algorithm model layer comprises a periodic replacement prediction model for identifying a spare part periodic replacement rule and a spare part association relationship, an equipment state and life prediction model for predicting the remaining life of a part according to sensor data, and an industry trend prediction model for predicting the influence on spare part demands through industry trend data. The environment working condition influence prediction model evaluates the influence of the environment on the service life of the part by combining working condition parameters, and the decision fusion model fuses other model prediction results through integrated learning; and the inventory optimization and execution layer generates a spare part replenishment plan according to the prediction result. According to the method, the accuracy and comprehensiveness of coal machine equipment spare part demand prediction are improved through multi-source data and multi-model fusion.

The application discloses a kind of regional vehicle image maintenance spare parts dynamic inventory optimization method and system, the method includes: obtaining the basic information of vehicle in region, vehicle historical maintenance data and regional environment data;According to the basic information of vehicle in region, the clustering is carried out to vehicle in region, and a plurality of vehicle groups are obtained;From the basic information of vehicle and vehicle historical maintenance data, the maintenance demand characteristics of each vehicle group are extracted;According to the maintenance demand characteristics of each vehicle group, vehicle historical maintenance data and regional environment data, the maintenance demand of each vehicle group is determined;According to the maintenance demand of all vehicle groups, the inventory strategy of regional warehouse is determined.The accurate prediction of the maintenance demand of regional vehicle and the inventory strategy of regional warehouse is realized.

This invention discloses a dynamic inventory optimization method based on a dynamic safety stock model, belonging to the field of dynamic inventory optimization technology. The method includes the following steps: when safety stock participates in inventory optimization and negatively influences its own input data, the safety stock feedback mapping quantity is decomposed across periods to construct safety stock feedback split variables and safety stock feedback aggregate quantities. The cumulative degree of safety stock's influence on the input data is determined based on the combination relationship between the safety stock feedback split variables and the safety stock feedback aggregate quantities. A multi-dimensional mapping is performed based on the safety stock feedback split variables and the safety stock feedback aggregate quantities to generate a safety stock feedback tensor. The safety stock feedback tensor is then subjected to uniform scale compression to generate a feedback coupling expression result. This invention solves the problem of unsuppressed cumulative feedback effects when safety stock participates in inventory optimization and negatively influences the input data, thereby improving the stability of the safety stock calculation process and the accuracy of inventory optimization.

The invention relates to the technical field of inventory management, and particularly provides an equipment spare part inventory optimization method and device based on fault prediction and electronic equipment. The method comprises the steps of predicting a fault probability of an equipment spare part in a preset ordering period through a deep learning model according to historical operation data of target equipment corresponding to the equipment spare part and fault record data of the equipment spare part; based on the fault probability and the historical warehouse-out data of the equipment spare parts, calculating a target demand quantity of the equipment spare parts in a preset ordering period; based on the target demand quantity, constructing an equipment spare part inventory optimization function; wherein the target of the equipment spare part inventory optimization function is that the sum of the purchasing cost, the ordering cost and the holding cost is minimum; and solving the equipment spare part inventory optimization function to obtain the ordering number of the equipment spare parts in the preset ordering period. According to the technical scheme provided by the invention, the inventory management cost can be reduced under the condition of ensuring the stable operation of the power distribution system.

The invention discloses an intelligent container management system for dynamic inventory optimization and demand prediction. The intelligent container management system comprises the following modules: a demand behavior acquisition module, a dual-state behavior modeling prediction engine, an inventory dynamic optimization module and a container execution control module. The demand behavior acquisition module is used for acquiring original behavior data of interaction between the user and the intelligent container and performing cleaning and feature extraction on the data; the invention relates to the technical field of intelligent container management. According to the intelligent container management system for dynamic inventory optimization and demand prediction, data are classified and modeled through a two-state behavior modeling prediction engine, short-term and long-term demand prediction is combined, the accuracy of demand prediction is improved, an instant demand behavior and a planned demand behavior are distinguished, and the demand prediction efficiency is improved. And through fusion of the Markov chain model and the time sequence attention network model, the long-term dependency relationship of the behavior sequence is captured, so that a more accurate demand prediction result is provided.

The invention belongs to the field of inventory prediction and optimization, and provides a dynamic disturbance perception inventory optimization method and system based on supply chain digital twinning, and the method comprises the steps: obtaining the full-link node data of a power grid supply chain, and extracting features; the method comprises the following steps: constructing a three-dimensional twin model of a power grid supply chain, considering scene features of the power grid supply chain, constructing disturbance factors, classifying unstructured disturbance factors and structured disturbance factors, fusing, and training a hybrid prediction optimization model according to the fused features in combination with power grid inventory management requirements in a power scene. Optimizing parameter configuration of the model; and processing the inventory data of the target area in the target scene by using the trained hybrid prediction optimization model, and determining a replenishment scheme. The inventory optimization accuracy can be improved, and the method is particularly suitable for electric power scenes.

This disclosure relates to a material warehousing data processing method based on real-time weight sensing and data-driven approaches. The method includes: acquiring real-time weight data for each storage location in the warehouse; converting the real-time weight data into real-time quantity based on standard unit weight information in a pre-established digital material archive; comparing the real-time quantity with the recorded quantity in the warehouse management system; generating and sending an anomaly warning when the comparison deviation exceeds a preset threshold; binding warehouse material inbound / outbound and transfer operations with storage location weight change events; triggering related material management business processes when a compliant weight change is detected; calculating a dynamic turnover rate based on the frequency of material weight changes and historical data; and generating inventory optimization decisions based on the dynamic turnover rate. This solution achieves real-time, automated, and intelligent material warehousing.

The application discloses an AI-driven aviation consumable demand prediction and intelligent inventory optimization method and system, integrates multi-dimensional heterogeneous data such as flight dynamic scheduling, component service working conditions, 3D printing consumable attributes and a supply chain, constructs a collaborative prediction model through an AI algorithm, greatly improves the demand prediction accuracy compared with a traditional time series analysis method, effectively solves the problem of insufficient prediction accuracy in a complex aviation scene, and provides reliable data support for inventory optimization.

The application provides a method and system for constructing a multi-source time series data fusion prediction model in BI analysis, and relates to the technical field of enterprise-level business intelligent analysis, which comprises obtaining multi-source original time series data and performing standardization preprocessing; obtaining a multi-dimensional feature set through time series feature extraction and business feature extraction; dynamically fusing the time series features and the business features by using an attention mechanism, training an LSTM and XGBoost combined prediction model, and performing parameter optimization based on an independent verification set to obtain a final prediction model; and the system comprises a data acquisition and preprocessing unit, a multi-dimensional feature extraction unit, a fusion prediction model construction unit, and a model verification and optimization unit. The application solves the problems of insufficient multi-source time series data fusion, difficulty for a model to capture complex space-time features, and lack of dynamic self-adaptive capability, improves the accuracy and real-time performance of enterprise-level BI analysis such as sales trend prediction and inventory optimization, and improves the prediction accuracy.